EGB_res网络可视化

目标：

1. 学习神经网络在训练过程中，隐含层的特征变化机制

2. 学习端到端梯度提升算法(End to end gradient boosting, EGB)

代码中的基学习器为残差网络(residual network, res)

参考：

葛家驿,杨乃森,唐宏,徐朋磊,纪超.端到端的梯度提升网络分类过程可视化[J].信号处理,2022,38(02):355-366.DOI:10.16798/j.issn.1003-0530.2022.02.015.

研究准备

环境配置

'''
端到端梯度提升模型分类过程可视化
每个基分类器是残差网络
'''

import numpy as np
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import backend as K
from tensorflow.keras import layers
from tensorflow.keras.callbacks import CSVLogger
from sklearn.preprocessing import MinMaxScaler
from matplotlib.colors import ListedColormap
from matplotlib import cm
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import make_circles #同心圆数据
from sklearn.model_selection import train_test_split
import sys
sys.setrecursionlimit(500000)
import imageio
%pylab inline

Populating the interactive namespace from numpy and matplotlib

设置模拟数据

n_samples = 1000 #样本点数
X, y = make_circles(n_samples=1000,factor=.4,noise=.06,random_state=0) #生成同心圆数据
test_size = 0.5

#划分训练集、测试集
X_train, X_test, Y_train, Y_test = train_test_split(X, y, test_size=test_size, random_state=2)

c,r = np.mgrid[[slice(X.min()- .2,X.max() + .2,50j)]*2]
p = np.c_[c.flat,r.flat]

ss = StandardScaler().fit(X_train)
X = ss.transform(X)
p = ss.transform(p)
X_train = ss.transform(X_train)
X_test = ss.transform(X_test)

配置绘图环境

#设置画布大小和颜色
fig = plt.figure(figsize = (9,3))
top = cm.get_cmap('Oranges_r', 512)
bottom = cm.get_cmap('Blues', 512)
newcolors = np.vstack((top(np.linspace(0.55, 1, 512)),
                       bottom(np.linspace(0, 0.75, 512))))
cm_bright = ListedColormap(newcolors, name='OrangeBlue')

#训练数据可视化
plt.subplot(121)
m1 = plt.scatter(*X_train.T,c = Y_train,cmap = cm_bright,edgecolors='white',s = 20,linewidths = 0.5)
plt.title(f'train data ({int(n_samples*(1-test_size))} points)')
plt.axis('equal')

#测试数据可视化
plt.subplot(122)
m2 = plt.scatter(*X_test.T,c = Y_test,cmap = cm_bright,edgecolors='white',s = 20,linewidths = 0.5);
plt.title(f'test data ({int(n_samples*test_size)} points)')
plt.axis('equal')
ax = fig.get_axes()
plt.colorbar(ax = ax)
#plt.savefig(f'data_{n_samples}_points.png')
#plt.savefig(f'data_{n_samples}_points.pdf')
plt.show()

#全部数据可视化
fig = plt.figure(figsize = (7,6))
plt.scatter(*X.T,c = y,cmap = cm_bright,edgecolors='white',s = 20,linewidths = 0.5)
plt.title(f'Raw data ({n_samples} points)')
plt.axis('equal')
#plt.savefig(f'Raw data ({n_samples} points)')
#plt.savefig(f'Raw data ({n_samples} points).pdf')
plt.axis('equal')
#plt.colorbar(ax = ax)
plt.show()

配置数据与损失函数

num_classes=2 #设置类别数
y_train=keras.utils.to_categorical(Y_train,num_classes) #类别标签转换为onehot编码
y_test=keras.utils.to_categorical(Y_test,num_classes)
#定义损失曲线绘制函数

def plot_loss_accuracy(history, title_text, file_name):

    fig, ax1 = plt.subplots()
    ax2 = ax1.twinx()
    ax1.set_xlabel("Epoch")
    ax1.set_ylabel("Accuracy")
    ax2.set_ylabel("Loss")

    #ax1.set_ylim(-0.01,1.01)
    ax1.plot(history.epoch,
             history.history['accuracy'],
             label="Training Accuracy")
    ax1.plot(history.epoch,
             history.history['val_accuracy'],
             linestyle='--',
             label="Test Accuracy")

    #ax2.set_ylim(-0.01,1.01)
    ax2._get_lines.prop_cycler = ax1._get_lines.prop_cycler

    ax2.plot(history.epoch,
             history.history['loss'],
             label="Training Loss")
    ax2.plot(history.epoch,
             history.history['val_loss'],
             linestyle='--',
             label="Test Loss")

    ax1.legend()
    ax2.legend()
    plt.suptitle(title_text)
    plt.savefig(file_name)

配置基于残差网络的端到端梯度提升模型

#定义残差块

def ResMLP_Block(name_of_classifiers,
                 num_of_res_blocks,
                 num_hidden_layers_of_res_block,
                 num_neurons_of_hidden_layer,
                 inputs):

    x = inputs
    for i in range(num_of_res_blocks):
        inputs = x
        for j in range(num_hidden_layers_of_res_block):
            x = layers.Dense(num_neurons_of_hidden_layer,
                             activation=tf.nn.relu,
                             name = f'{name_of_classifiers}th-clf_{i}th-block_{j}th-hidden')(x)
        x = layers.Dense(2, name = f'{name_of_classifiers}th-clf_{i}th-resBlock_linear')(x)
        x = layers.Add(name = f'{name_of_classifiers}th-clf_{i}th-resBlock_Add')([x,inputs])

    return x
#构建残差基学习器

def build_res_model(name_of_classifiers,
                    num_of_res_blocks,
                    num_hidden_layers_of_res_block,
                    num_neurons_of_hidden_layer,
                    inputs,
                    num_of_classes=2):
    x = ResMLP_Block(name_of_classifiers,
                     num_of_res_blocks,
                     num_hidden_layers_of_res_block,
                     num_neurons_of_hidden_layer,
                     inputs)
    outputs = x
    #outputs = layers.Dense(num_of_classes,name=f'{name_of_classifiers}th-clf_logits')(x)
    res_model = keras.Model(inputs, outputs)
    return res_model
#构建EGB网络

def build_boosting_model(classifiers,
                         name_of_classifiers,
                         num_of_res_blocks,
                         num_hidden_layers_of_res_block,
                         num_neurons_of_hidden_layer,
                         inputs):
    model_logits = build_res_model(name_of_classifiers,
                                   num_of_res_blocks,
                                   num_hidden_layers_of_res_block,
                                   num_neurons_of_hidden_layer,
                                   inputs)
    classifiers.append(model_logits)
    if len(classifiers)>1:
        res_boost_model = layers.Add(name='classifiers_Add')([item.outputs[0] for item in classifiers])
    else:
        res_boost_model = model_logits.outputs[0]
    outputs = layers.Dense(2, activation='softmax',name = 'activation')(res_boost_model)
    #print(outputs)
    boosting_model = keras.Model(inputs, outputs)
    return boosting_model,model_logits

定义模型训练方式

#定义模型训练

def train_ResBoost_model(number_of_weak_classifiers,
                         num_of_res_blocks,
                         num_hidden_layers_of_res_block,
                         num_neurons_of_hidden_layer,
                         batch_size,
                         epochs):

    classifiers = []
    history = []
    boosting_models = []

    inputs = keras.Input(shape=(2, ))

    #每次叠加一个基分类器拟合训练数据
    for n_th_weak in range(number_of_weak_classifiers):
        name_of_classifiers = n_th_weak
        boosting_model,model_logits=build_boosting_model(classifiers,
                                                         name_of_classifiers,
                                                         num_of_res_blocks,
                                                         num_hidden_layers_of_res_block,
                                                         num_neurons_of_hidden_layer,
                                                         inputs)

        boosting_model.compile(loss=keras.losses.categorical_crossentropy,
                                  optimizer=keras.optimizers.Adam(lr=3e-4),
                                  metrics=['accuracy'])
        #csv_logger = CSVLogger(f'training_{name_of_classifiers}.log') #保存训练日志
        new_history = boosting_model.fit(X_train,
                                        y_train,
                                        batch_size=batch_size,
                                        epochs=epochs,
                                        verbose=2,
                                        #callbacks=[csv_logger],
                                        validation_data=(X_test, y_test))

        #在训练新的基分类器时，所有前面的基分类器每层的参数冻结
        for layer in classifiers[-1].layers:
            layer.trainable = False
        history.append(new_history)
        boosting_models.append(boosting_model)
        #model_logits.save_weights(f"single_classifier_{name_of_classifiers}.h5") #保存基分类器
        print('第{}个弱分类器训练完毕'.format(n_th_weak+1))

    #boosting_model.save(f'boosting_model_with_{number_of_weak_classifiers}_classifiers.h5') #保存模型
    return boosting_model,boosting_models,history

进行实验

设置训练参数

#设置参数

epochs=400 #迭代次数
batch_size=32 #batchsize
number_of_weak_classifiers=5 #基分类器个数
num_of_res_blocks=1 #基分类器的残差块个数
num_hidden_layers_of_res_block=4 #残差块的隐层数
num_neurons_of_hidden_layer=2 #隐层的神经元数

#训练模型
boosting_model,boosting_models,history=train_ResBoost_model(number_of_weak_classifiers,
                                                            num_of_res_blocks,
                                                            num_hidden_layers_of_res_block,
                                                            num_neurons_of_hidden_layer,
                                                            batch_size=batch_size,
                                                            epochs=epochs)

/usr/local/lib/python3.7/dist-packages/keras/optimizers/optimizer_v2/adam.py:110: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(Adam, self).__init__(name, **kwargs)

Epoch 1/400
16/16 - 1s - loss: 1.2555 - accuracy: 0.5040 - val_loss: 1.3123 - val_accuracy: 0.4860 - 1s/epoch - 67ms/step
Epoch 2/400
16/16 - 0s - loss: 1.2451 - accuracy: 0.5040 - val_loss: 1.3009 - val_accuracy: 0.4860 - 65ms/epoch - 4ms/step
Epoch 3/400
16/16 - 0s - loss: 1.2351 - accuracy: 0.5000 - val_loss: 1.2891 - val_accuracy: 0.4860 - 110ms/epoch - 7ms/step
Epoch 4/400
16/16 - 0s - loss: 1.2250 - accuracy: 0.4960 - val_loss: 1.2779 - val_accuracy: 0.4900 - 187ms/epoch - 12ms/step
Epoch 5/400
16/16 - 0s - loss: 1.2153 - accuracy: 0.4940 - val_loss: 1.2669 - val_accuracy: 0.4960 - 172ms/epoch - 11ms/step
Epoch 6/400
16/16 - 0s - loss: 1.2059 - accuracy: 0.5000 - val_loss: 1.2556 - val_accuracy: 0.4920 - 118ms/epoch - 7ms/step
Epoch 7/400
16/16 - 0s - loss: 1.1969 - accuracy: 0.4960 - val_loss: 1.2441 - val_accuracy: 0.4900 - 146ms/epoch - 9ms/step
Epoch 8/400
16/16 - 0s - loss: 1.1870 - accuracy: 0.4940 - val_loss: 1.2343 - val_accuracy: 0.4940 - 205ms/epoch - 13ms/step
Epoch 9/400
16/16 - 0s - loss: 1.1782 - accuracy: 0.4960 - val_loss: 1.2238 - val_accuracy: 0.4920 - 141ms/epoch - 9ms/step
Epoch 10/400
16/16 - 0s - loss: 1.1697 - accuracy: 0.4940 - val_loss: 1.2132 - val_accuracy: 0.4880 - 170ms/epoch - 11ms/step
Epoch 11/400
16/16 - 0s - loss: 1.1606 - accuracy: 0.4920 - val_loss: 1.2038 - val_accuracy: 0.4860 - 131ms/epoch - 8ms/step
Epoch 12/400
16/16 - 0s - loss: 1.1524 - accuracy: 0.4880 - val_loss: 1.1940 - val_accuracy: 0.4820 - 150ms/epoch - 9ms/step
Epoch 13/400
16/16 - 0s - loss: 1.1441 - accuracy: 0.4840 - val_loss: 1.1842 - val_accuracy: 0.4860 - 157ms/epoch - 10ms/step
Epoch 14/400
16/16 - 0s - loss: 1.1360 - accuracy: 0.4840 - val_loss: 1.1746 - val_accuracy: 0.4820 - 125ms/epoch - 8ms/step
Epoch 15/400
16/16 - 0s - loss: 1.1281 - accuracy: 0.4860 - val_loss: 1.1654 - val_accuracy: 0.4820 - 147ms/epoch - 9ms/step
Epoch 16/400
16/16 - 0s - loss: 1.1202 - accuracy: 0.4860 - val_loss: 1.1566 - val_accuracy: 0.4800 - 149ms/epoch - 9ms/step
Epoch 17/400
16/16 - 0s - loss: 1.1126 - accuracy: 0.4860 - val_loss: 1.1483 - val_accuracy: 0.4780 - 152ms/epoch - 10ms/step
Epoch 18/400
16/16 - 0s - loss: 1.1053 - accuracy: 0.4860 - val_loss: 1.1394 - val_accuracy: 0.4800 - 222ms/epoch - 14ms/step
Epoch 19/400
16/16 - 0s - loss: 1.0985 - accuracy: 0.4860 - val_loss: 1.1303 - val_accuracy: 0.4840 - 116ms/epoch - 7ms/step
Epoch 20/400
16/16 - 0s - loss: 1.0906 - accuracy: 0.4840 - val_loss: 1.1227 - val_accuracy: 0.4840 - 140ms/epoch - 9ms/step
Epoch 21/400
16/16 - 0s - loss: 1.0836 - accuracy: 0.4860 - val_loss: 1.1150 - val_accuracy: 0.4860 - 206ms/epoch - 13ms/step
Epoch 22/400
16/16 - 0s - loss: 1.0770 - accuracy: 0.4840 - val_loss: 1.1066 - val_accuracy: 0.4860 - 152ms/epoch - 9ms/step
Epoch 23/400
16/16 - 0s - loss: 1.0703 - accuracy: 0.4800 - val_loss: 1.0987 - val_accuracy: 0.4840 - 135ms/epoch - 8ms/step
Epoch 24/400
16/16 - 0s - loss: 1.0635 - accuracy: 0.4800 - val_loss: 1.0915 - val_accuracy: 0.4840 - 142ms/epoch - 9ms/step
Epoch 25/400
16/16 - 0s - loss: 1.0572 - accuracy: 0.4800 - val_loss: 1.0840 - val_accuracy: 0.4840 - 170ms/epoch - 11ms/step
Epoch 26/400
16/16 - 0s - loss: 1.0506 - accuracy: 0.4800 - val_loss: 1.0768 - val_accuracy: 0.4840 - 140ms/epoch - 9ms/step
Epoch 27/400
16/16 - 0s - loss: 1.0445 - accuracy: 0.4800 - val_loss: 1.0694 - val_accuracy: 0.4820 - 127ms/epoch - 8ms/step
Epoch 28/400
16/16 - 0s - loss: 1.0383 - accuracy: 0.4800 - val_loss: 1.0626 - val_accuracy: 0.4820 - 166ms/epoch - 10ms/step
Epoch 29/400
16/16 - 0s - loss: 1.0323 - accuracy: 0.4760 - val_loss: 1.0559 - val_accuracy: 0.4820 - 169ms/epoch - 11ms/step
Epoch 30/400
16/16 - 0s - loss: 1.0265 - accuracy: 0.4760 - val_loss: 1.0490 - val_accuracy: 0.4880 - 177ms/epoch - 11ms/step
Epoch 31/400
16/16 - 0s - loss: 1.0207 - accuracy: 0.4760 - val_loss: 1.0422 - val_accuracy: 0.4860 - 147ms/epoch - 9ms/step
Epoch 32/400
16/16 - 0s - loss: 1.0148 - accuracy: 0.4740 - val_loss: 1.0362 - val_accuracy: 0.4820 - 166ms/epoch - 10ms/step
Epoch 33/400
16/16 - 0s - loss: 1.0092 - accuracy: 0.4720 - val_loss: 1.0299 - val_accuracy: 0.4800 - 164ms/epoch - 10ms/step
Epoch 34/400
16/16 - 0s - loss: 1.0038 - accuracy: 0.4720 - val_loss: 1.0235 - val_accuracy: 0.4780 - 154ms/epoch - 10ms/step
Epoch 35/400
16/16 - 0s - loss: 0.9982 - accuracy: 0.4720 - val_loss: 1.0175 - val_accuracy: 0.4760 - 132ms/epoch - 8ms/step
Epoch 36/400
16/16 - 0s - loss: 0.9931 - accuracy: 0.4700 - val_loss: 1.0107 - val_accuracy: 0.4740 - 149ms/epoch - 9ms/step
Epoch 37/400
16/16 - 0s - loss: 0.9875 - accuracy: 0.4720 - val_loss: 1.0046 - val_accuracy: 0.4760 - 174ms/epoch - 11ms/step
Epoch 38/400
16/16 - 0s - loss: 0.9823 - accuracy: 0.4720 - val_loss: 0.9988 - val_accuracy: 0.4780 - 170ms/epoch - 11ms/step
Epoch 39/400
16/16 - 0s - loss: 0.9772 - accuracy: 0.4760 - val_loss: 0.9934 - val_accuracy: 0.4780 - 111ms/epoch - 7ms/step
Epoch 40/400
16/16 - 0s - loss: 0.9723 - accuracy: 0.4760 - val_loss: 0.9876 - val_accuracy: 0.4780 - 185ms/epoch - 12ms/step
Epoch 41/400
16/16 - 0s - loss: 0.9672 - accuracy: 0.4760 - val_loss: 0.9823 - val_accuracy: 0.4800 - 156ms/epoch - 10ms/step
Epoch 42/400
16/16 - 0s - loss: 0.9628 - accuracy: 0.4740 - val_loss: 0.9762 - val_accuracy: 0.4800 - 147ms/epoch - 9ms/step
Epoch 43/400
16/16 - 0s - loss: 0.9575 - accuracy: 0.4740 - val_loss: 0.9712 - val_accuracy: 0.4800 - 227ms/epoch - 14ms/step
Epoch 44/400
16/16 - 0s - loss: 0.9531 - accuracy: 0.4720 - val_loss: 0.9654 - val_accuracy: 0.4820 - 151ms/epoch - 9ms/step
Epoch 45/400
16/16 - 0s - loss: 0.9483 - accuracy: 0.4720 - val_loss: 0.9606 - val_accuracy: 0.4800 - 103ms/epoch - 6ms/step
Epoch 46/400
16/16 - 0s - loss: 0.9438 - accuracy: 0.4720 - val_loss: 0.9554 - val_accuracy: 0.4820 - 133ms/epoch - 8ms/step
Epoch 47/400
16/16 - 0s - loss: 0.9393 - accuracy: 0.4720 - val_loss: 0.9502 - val_accuracy: 0.4820 - 130ms/epoch - 8ms/step
Epoch 48/400
16/16 - 0s - loss: 0.9347 - accuracy: 0.4740 - val_loss: 0.9458 - val_accuracy: 0.4860 - 151ms/epoch - 9ms/step
Epoch 49/400
16/16 - 0s - loss: 0.9306 - accuracy: 0.4680 - val_loss: 0.9405 - val_accuracy: 0.4840 - 148ms/epoch - 9ms/step
Epoch 50/400
16/16 - 0s - loss: 0.9260 - accuracy: 0.4700 - val_loss: 0.9356 - val_accuracy: 0.4820 - 150ms/epoch - 9ms/step
Epoch 51/400
16/16 - 0s - loss: 0.9218 - accuracy: 0.4700 - val_loss: 0.9306 - val_accuracy: 0.4820 - 140ms/epoch - 9ms/step
Epoch 52/400
16/16 - 0s - loss: 0.9176 - accuracy: 0.4720 - val_loss: 0.9257 - val_accuracy: 0.4820 - 164ms/epoch - 10ms/step
Epoch 53/400
16/16 - 0s - loss: 0.9134 - accuracy: 0.4720 - val_loss: 0.9214 - val_accuracy: 0.4820 - 134ms/epoch - 8ms/step
Epoch 54/400
16/16 - 0s - loss: 0.9094 - accuracy: 0.4720 - val_loss: 0.9168 - val_accuracy: 0.4820 - 258ms/epoch - 16ms/step
Epoch 55/400
16/16 - 0s - loss: 0.9053 - accuracy: 0.4700 - val_loss: 0.9123 - val_accuracy: 0.4820 - 130ms/epoch - 8ms/step
Epoch 56/400
16/16 - 0s - loss: 0.9014 - accuracy: 0.4720 - val_loss: 0.9077 - val_accuracy: 0.4840 - 160ms/epoch - 10ms/step
Epoch 57/400
16/16 - 0s - loss: 0.8975 - accuracy: 0.4720 - val_loss: 0.9033 - val_accuracy: 0.4840 - 214ms/epoch - 13ms/step
Epoch 58/400
16/16 - 0s - loss: 0.8934 - accuracy: 0.4720 - val_loss: 0.8994 - val_accuracy: 0.4860 - 146ms/epoch - 9ms/step
Epoch 59/400
16/16 - 0s - loss: 0.8898 - accuracy: 0.4740 - val_loss: 0.8949 - val_accuracy: 0.4900 - 134ms/epoch - 8ms/step
Epoch 60/400
16/16 - 0s - loss: 0.8859 - accuracy: 0.4740 - val_loss: 0.8907 - val_accuracy: 0.4900 - 142ms/epoch - 9ms/step
Epoch 61/400
16/16 - 0s - loss: 0.8820 - accuracy: 0.4760 - val_loss: 0.8864 - val_accuracy: 0.4880 - 154ms/epoch - 10ms/step
Epoch 62/400
16/16 - 0s - loss: 0.8783 - accuracy: 0.4760 - val_loss: 0.8822 - val_accuracy: 0.4920 - 291ms/epoch - 18ms/step
Epoch 63/400
16/16 - 0s - loss: 0.8747 - accuracy: 0.4760 - val_loss: 0.8778 - val_accuracy: 0.4920 - 135ms/epoch - 8ms/step
Epoch 64/400
16/16 - 0s - loss: 0.8709 - accuracy: 0.4780 - val_loss: 0.8741 - val_accuracy: 0.4920 - 146ms/epoch - 9ms/step
Epoch 65/400
16/16 - 0s - loss: 0.8674 - accuracy: 0.4780 - val_loss: 0.8698 - val_accuracy: 0.4900 - 140ms/epoch - 9ms/step
Epoch 66/400
16/16 - 0s - loss: 0.8638 - accuracy: 0.4800 - val_loss: 0.8659 - val_accuracy: 0.4900 - 100ms/epoch - 6ms/step
Epoch 67/400
16/16 - 0s - loss: 0.8603 - accuracy: 0.4820 - val_loss: 0.8618 - val_accuracy: 0.4920 - 166ms/epoch - 10ms/step
Epoch 68/400
16/16 - 0s - loss: 0.8568 - accuracy: 0.4820 - val_loss: 0.8583 - val_accuracy: 0.4900 - 127ms/epoch - 8ms/step
Epoch 69/400
16/16 - 0s - loss: 0.8533 - accuracy: 0.4820 - val_loss: 0.8543 - val_accuracy: 0.4920 - 113ms/epoch - 7ms/step
Epoch 70/400
16/16 - 0s - loss: 0.8501 - accuracy: 0.4840 - val_loss: 0.8501 - val_accuracy: 0.4920 - 114ms/epoch - 7ms/step
Epoch 71/400
16/16 - 0s - loss: 0.8465 - accuracy: 0.4860 - val_loss: 0.8466 - val_accuracy: 0.4940 - 254ms/epoch - 16ms/step
Epoch 72/400
16/16 - 0s - loss: 0.8433 - accuracy: 0.4840 - val_loss: 0.8426 - val_accuracy: 0.4940 - 303ms/epoch - 19ms/step
Epoch 73/400
16/16 - 0s - loss: 0.8398 - accuracy: 0.4840 - val_loss: 0.8391 - val_accuracy: 0.4940 - 192ms/epoch - 12ms/step
Epoch 74/400
16/16 - 0s - loss: 0.8364 - accuracy: 0.4840 - val_loss: 0.8358 - val_accuracy: 0.4960 - 155ms/epoch - 10ms/step
Epoch 75/400
16/16 - 0s - loss: 0.8332 - accuracy: 0.4840 - val_loss: 0.8318 - val_accuracy: 0.4960 - 138ms/epoch - 9ms/step
Epoch 76/400
16/16 - 0s - loss: 0.8298 - accuracy: 0.4840 - val_loss: 0.8281 - val_accuracy: 0.4980 - 331ms/epoch - 21ms/step
Epoch 77/400
16/16 - 0s - loss: 0.8264 - accuracy: 0.4820 - val_loss: 0.8245 - val_accuracy: 0.5000 - 135ms/epoch - 8ms/step
Epoch 78/400
16/16 - 0s - loss: 0.8233 - accuracy: 0.4880 - val_loss: 0.8206 - val_accuracy: 0.5000 - 160ms/epoch - 10ms/step
Epoch 79/400
16/16 - 0s - loss: 0.8200 - accuracy: 0.4920 - val_loss: 0.8171 - val_accuracy: 0.5020 - 152ms/epoch - 10ms/step
Epoch 80/400
16/16 - 0s - loss: 0.8167 - accuracy: 0.4940 - val_loss: 0.8139 - val_accuracy: 0.5020 - 162ms/epoch - 10ms/step
Epoch 81/400
16/16 - 0s - loss: 0.8135 - accuracy: 0.4940 - val_loss: 0.8101 - val_accuracy: 0.5020 - 109ms/epoch - 7ms/step
Epoch 82/400
16/16 - 0s - loss: 0.8102 - accuracy: 0.4920 - val_loss: 0.8065 - val_accuracy: 0.5080 - 142ms/epoch - 9ms/step
Epoch 83/400
16/16 - 0s - loss: 0.8071 - accuracy: 0.4960 - val_loss: 0.8028 - val_accuracy: 0.5080 - 223ms/epoch - 14ms/step
Epoch 84/400
16/16 - 0s - loss: 0.8037 - accuracy: 0.4960 - val_loss: 0.7993 - val_accuracy: 0.5080 - 174ms/epoch - 11ms/step
Epoch 85/400
16/16 - 0s - loss: 0.8007 - accuracy: 0.4940 - val_loss: 0.7954 - val_accuracy: 0.5100 - 195ms/epoch - 12ms/step
Epoch 86/400
16/16 - 0s - loss: 0.7973 - accuracy: 0.4960 - val_loss: 0.7920 - val_accuracy: 0.5120 - 141ms/epoch - 9ms/step
Epoch 87/400
16/16 - 0s - loss: 0.7942 - accuracy: 0.5000 - val_loss: 0.7885 - val_accuracy: 0.5180 - 195ms/epoch - 12ms/step
Epoch 88/400
16/16 - 0s - loss: 0.7909 - accuracy: 0.4980 - val_loss: 0.7852 - val_accuracy: 0.5220 - 123ms/epoch - 8ms/step
Epoch 89/400
16/16 - 0s - loss: 0.7878 - accuracy: 0.5000 - val_loss: 0.7816 - val_accuracy: 0.5220 - 127ms/epoch - 8ms/step
Epoch 90/400
16/16 - 0s - loss: 0.7847 - accuracy: 0.5020 - val_loss: 0.7778 - val_accuracy: 0.5220 - 258ms/epoch - 16ms/step
Epoch 91/400
16/16 - 0s - loss: 0.7815 - accuracy: 0.5020 - val_loss: 0.7741 - val_accuracy: 0.5220 - 214ms/epoch - 13ms/step
Epoch 92/400
16/16 - 0s - loss: 0.7782 - accuracy: 0.5040 - val_loss: 0.7706 - val_accuracy: 0.5160 - 211ms/epoch - 13ms/step
Epoch 93/400
16/16 - 0s - loss: 0.7750 - accuracy: 0.5020 - val_loss: 0.7671 - val_accuracy: 0.5180 - 136ms/epoch - 9ms/step
Epoch 94/400
16/16 - 0s - loss: 0.7720 - accuracy: 0.5040 - val_loss: 0.7635 - val_accuracy: 0.5220 - 100ms/epoch - 6ms/step
Epoch 95/400
16/16 - 0s - loss: 0.7688 - accuracy: 0.5040 - val_loss: 0.7598 - val_accuracy: 0.5200 - 155ms/epoch - 10ms/step
Epoch 96/400
16/16 - 0s - loss: 0.7656 - accuracy: 0.5040 - val_loss: 0.7563 - val_accuracy: 0.5180 - 170ms/epoch - 11ms/step
Epoch 97/400
16/16 - 0s - loss: 0.7624 - accuracy: 0.5040 - val_loss: 0.7529 - val_accuracy: 0.5180 - 137ms/epoch - 9ms/step
Epoch 98/400
16/16 - 0s - loss: 0.7594 - accuracy: 0.5060 - val_loss: 0.7494 - val_accuracy: 0.5140 - 128ms/epoch - 8ms/step
Epoch 99/400
16/16 - 0s - loss: 0.7562 - accuracy: 0.5080 - val_loss: 0.7458 - val_accuracy: 0.5120 - 163ms/epoch - 10ms/step
Epoch 100/400
16/16 - 0s - loss: 0.7531 - accuracy: 0.5060 - val_loss: 0.7422 - val_accuracy: 0.5120 - 115ms/epoch - 7ms/step
Epoch 101/400
16/16 - 0s - loss: 0.7500 - accuracy: 0.5000 - val_loss: 0.7391 - val_accuracy: 0.5140 - 134ms/epoch - 8ms/step
Epoch 102/400
16/16 - 0s - loss: 0.7469 - accuracy: 0.4960 - val_loss: 0.7356 - val_accuracy: 0.5120 - 209ms/epoch - 13ms/step
Epoch 103/400
16/16 - 0s - loss: 0.7439 - accuracy: 0.4940 - val_loss: 0.7318 - val_accuracy: 0.5140 - 276ms/epoch - 17ms/step
Epoch 104/400
16/16 - 0s - loss: 0.7406 - accuracy: 0.4960 - val_loss: 0.7285 - val_accuracy: 0.5180 - 250ms/epoch - 16ms/step
Epoch 105/400
16/16 - 0s - loss: 0.7376 - accuracy: 0.5020 - val_loss: 0.7249 - val_accuracy: 0.5180 - 145ms/epoch - 9ms/step
Epoch 106/400
16/16 - 0s - loss: 0.7344 - accuracy: 0.5020 - val_loss: 0.7214 - val_accuracy: 0.5220 - 129ms/epoch - 8ms/step
Epoch 107/400
16/16 - 0s - loss: 0.7313 - accuracy: 0.4980 - val_loss: 0.7178 - val_accuracy: 0.5280 - 173ms/epoch - 11ms/step
Epoch 108/400
16/16 - 0s - loss: 0.7282 - accuracy: 0.4980 - val_loss: 0.7145 - val_accuracy: 0.5300 - 223ms/epoch - 14ms/step
Epoch 109/400
16/16 - 0s - loss: 0.7250 - accuracy: 0.4980 - val_loss: 0.7112 - val_accuracy: 0.5320 - 149ms/epoch - 9ms/step
Epoch 110/400
16/16 - 0s - loss: 0.7220 - accuracy: 0.4980 - val_loss: 0.7074 - val_accuracy: 0.5320 - 179ms/epoch - 11ms/step
Epoch 111/400
16/16 - 0s - loss: 0.7189 - accuracy: 0.5020 - val_loss: 0.7038 - val_accuracy: 0.5300 - 275ms/epoch - 17ms/step
Epoch 112/400
16/16 - 0s - loss: 0.7158 - accuracy: 0.5020 - val_loss: 0.7004 - val_accuracy: 0.5300 - 372ms/epoch - 23ms/step
Epoch 113/400
16/16 - 0s - loss: 0.7127 - accuracy: 0.5080 - val_loss: 0.6972 - val_accuracy: 0.5300 - 276ms/epoch - 17ms/step
Epoch 114/400
16/16 - 0s - loss: 0.7097 - accuracy: 0.5080 - val_loss: 0.6938 - val_accuracy: 0.5320 - 186ms/epoch - 12ms/step
Epoch 115/400
16/16 - 0s - loss: 0.7067 - accuracy: 0.5120 - val_loss: 0.6906 - val_accuracy: 0.5340 - 96ms/epoch - 6ms/step
Epoch 116/400
16/16 - 0s - loss: 0.7037 - accuracy: 0.5120 - val_loss: 0.6869 - val_accuracy: 0.5340 - 74ms/epoch - 5ms/step
Epoch 117/400
16/16 - 0s - loss: 0.7006 - accuracy: 0.5120 - val_loss: 0.6834 - val_accuracy: 0.5360 - 73ms/epoch - 5ms/step
Epoch 118/400
16/16 - 0s - loss: 0.6977 - accuracy: 0.5140 - val_loss: 0.6800 - val_accuracy: 0.5360 - 114ms/epoch - 7ms/step
Epoch 119/400
16/16 - 0s - loss: 0.6946 - accuracy: 0.5140 - val_loss: 0.6768 - val_accuracy: 0.5380 - 115ms/epoch - 7ms/step
Epoch 120/400
16/16 - 0s - loss: 0.6917 - accuracy: 0.5160 - val_loss: 0.6736 - val_accuracy: 0.5400 - 76ms/epoch - 5ms/step
Epoch 121/400
16/16 - 0s - loss: 0.6887 - accuracy: 0.5180 - val_loss: 0.6705 - val_accuracy: 0.5420 - 108ms/epoch - 7ms/step
Epoch 122/400
16/16 - 0s - loss: 0.6859 - accuracy: 0.5180 - val_loss: 0.6671 - val_accuracy: 0.5460 - 107ms/epoch - 7ms/step
Epoch 123/400
16/16 - 0s - loss: 0.6829 - accuracy: 0.5220 - val_loss: 0.6642 - val_accuracy: 0.5460 - 68ms/epoch - 4ms/step
Epoch 124/400
16/16 - 0s - loss: 0.6802 - accuracy: 0.5220 - val_loss: 0.6610 - val_accuracy: 0.5480 - 77ms/epoch - 5ms/step
Epoch 125/400
16/16 - 0s - loss: 0.6773 - accuracy: 0.5220 - val_loss: 0.6577 - val_accuracy: 0.5480 - 69ms/epoch - 4ms/step
Epoch 126/400
16/16 - 0s - loss: 0.6744 - accuracy: 0.5220 - val_loss: 0.6548 - val_accuracy: 0.5500 - 71ms/epoch - 4ms/step
Epoch 127/400
16/16 - 0s - loss: 0.6716 - accuracy: 0.5260 - val_loss: 0.6517 - val_accuracy: 0.5500 - 67ms/epoch - 4ms/step
Epoch 128/400
16/16 - 0s - loss: 0.6690 - accuracy: 0.5280 - val_loss: 0.6485 - val_accuracy: 0.5480 - 123ms/epoch - 8ms/step
Epoch 129/400
16/16 - 0s - loss: 0.6662 - accuracy: 0.5320 - val_loss: 0.6456 - val_accuracy: 0.5480 - 72ms/epoch - 4ms/step
Epoch 130/400
16/16 - 0s - loss: 0.6635 - accuracy: 0.5360 - val_loss: 0.6425 - val_accuracy: 0.5500 - 66ms/epoch - 4ms/step
Epoch 131/400
16/16 - 0s - loss: 0.6608 - accuracy: 0.5380 - val_loss: 0.6399 - val_accuracy: 0.5520 - 68ms/epoch - 4ms/step
Epoch 132/400
16/16 - 0s - loss: 0.6582 - accuracy: 0.5360 - val_loss: 0.6367 - val_accuracy: 0.5520 - 66ms/epoch - 4ms/step
Epoch 133/400
16/16 - 0s - loss: 0.6555 - accuracy: 0.5360 - val_loss: 0.6339 - val_accuracy: 0.5520 - 113ms/epoch - 7ms/step
Epoch 134/400
16/16 - 0s - loss: 0.6529 - accuracy: 0.5340 - val_loss: 0.6313 - val_accuracy: 0.5540 - 68ms/epoch - 4ms/step
Epoch 135/400
16/16 - 0s - loss: 0.6504 - accuracy: 0.5380 - val_loss: 0.6285 - val_accuracy: 0.5520 - 107ms/epoch - 7ms/step
Epoch 136/400
16/16 - 0s - loss: 0.6478 - accuracy: 0.5420 - val_loss: 0.6260 - val_accuracy: 0.5580 - 108ms/epoch - 7ms/step
Epoch 137/400
16/16 - 0s - loss: 0.6454 - accuracy: 0.5540 - val_loss: 0.6231 - val_accuracy: 0.5880 - 108ms/epoch - 7ms/step
Epoch 138/400
16/16 - 0s - loss: 0.6429 - accuracy: 0.5780 - val_loss: 0.6206 - val_accuracy: 0.6480 - 69ms/epoch - 4ms/step
Epoch 139/400
16/16 - 0s - loss: 0.6405 - accuracy: 0.6240 - val_loss: 0.6180 - val_accuracy: 0.6940 - 65ms/epoch - 4ms/step
Epoch 140/400
16/16 - 0s - loss: 0.6379 - accuracy: 0.6320 - val_loss: 0.6156 - val_accuracy: 0.7020 - 73ms/epoch - 5ms/step
Epoch 141/400
16/16 - 0s - loss: 0.6357 - accuracy: 0.6540 - val_loss: 0.6130 - val_accuracy: 0.7220 - 65ms/epoch - 4ms/step
Epoch 142/400
16/16 - 0s - loss: 0.6333 - accuracy: 0.6640 - val_loss: 0.6105 - val_accuracy: 0.7340 - 70ms/epoch - 4ms/step
Epoch 143/400
16/16 - 0s - loss: 0.6311 - accuracy: 0.6760 - val_loss: 0.6082 - val_accuracy: 0.7380 - 105ms/epoch - 7ms/step
Epoch 144/400
16/16 - 0s - loss: 0.6287 - accuracy: 0.6780 - val_loss: 0.6061 - val_accuracy: 0.7480 - 71ms/epoch - 4ms/step
Epoch 145/400
16/16 - 0s - loss: 0.6266 - accuracy: 0.6860 - val_loss: 0.6035 - val_accuracy: 0.7500 - 68ms/epoch - 4ms/step
Epoch 146/400
16/16 - 0s - loss: 0.6243 - accuracy: 0.6980 - val_loss: 0.6013 - val_accuracy: 0.7540 - 69ms/epoch - 4ms/step
Epoch 147/400
16/16 - 0s - loss: 0.6221 - accuracy: 0.7000 - val_loss: 0.5990 - val_accuracy: 0.7500 - 66ms/epoch - 4ms/step
Epoch 148/400
16/16 - 0s - loss: 0.6200 - accuracy: 0.7020 - val_loss: 0.5969 - val_accuracy: 0.7520 - 70ms/epoch - 4ms/step
Epoch 149/400
16/16 - 0s - loss: 0.6179 - accuracy: 0.7060 - val_loss: 0.5946 - val_accuracy: 0.7500 - 79ms/epoch - 5ms/step
Epoch 150/400
16/16 - 0s - loss: 0.6159 - accuracy: 0.7080 - val_loss: 0.5925 - val_accuracy: 0.7540 - 64ms/epoch - 4ms/step
Epoch 151/400
16/16 - 0s - loss: 0.6138 - accuracy: 0.7120 - val_loss: 0.5904 - val_accuracy: 0.7560 - 64ms/epoch - 4ms/step
Epoch 152/400
16/16 - 0s - loss: 0.6118 - accuracy: 0.7220 - val_loss: 0.5883 - val_accuracy: 0.7580 - 70ms/epoch - 4ms/step
Epoch 153/400
16/16 - 0s - loss: 0.6100 - accuracy: 0.7200 - val_loss: 0.5865 - val_accuracy: 0.7620 - 114ms/epoch - 7ms/step
Epoch 154/400
16/16 - 0s - loss: 0.6080 - accuracy: 0.7140 - val_loss: 0.5843 - val_accuracy: 0.7580 - 109ms/epoch - 7ms/step
Epoch 155/400
16/16 - 0s - loss: 0.6061 - accuracy: 0.7160 - val_loss: 0.5825 - val_accuracy: 0.7600 - 74ms/epoch - 5ms/step
Epoch 156/400
16/16 - 0s - loss: 0.6041 - accuracy: 0.7160 - val_loss: 0.5807 - val_accuracy: 0.7580 - 69ms/epoch - 4ms/step
Epoch 157/400
16/16 - 0s - loss: 0.6023 - accuracy: 0.7160 - val_loss: 0.5787 - val_accuracy: 0.7540 - 67ms/epoch - 4ms/step
Epoch 158/400
16/16 - 0s - loss: 0.6005 - accuracy: 0.7160 - val_loss: 0.5769 - val_accuracy: 0.7540 - 68ms/epoch - 4ms/step
Epoch 159/400
16/16 - 0s - loss: 0.5987 - accuracy: 0.7160 - val_loss: 0.5751 - val_accuracy: 0.7600 - 64ms/epoch - 4ms/step
Epoch 160/400
16/16 - 0s - loss: 0.5970 - accuracy: 0.7100 - val_loss: 0.5734 - val_accuracy: 0.7600 - 107ms/epoch - 7ms/step
Epoch 161/400
16/16 - 0s - loss: 0.5952 - accuracy: 0.7080 - val_loss: 0.5717 - val_accuracy: 0.7620 - 68ms/epoch - 4ms/step
Epoch 162/400
16/16 - 0s - loss: 0.5936 - accuracy: 0.7000 - val_loss: 0.5699 - val_accuracy: 0.7620 - 69ms/epoch - 4ms/step
Epoch 163/400
16/16 - 0s - loss: 0.5920 - accuracy: 0.7060 - val_loss: 0.5683 - val_accuracy: 0.7660 - 69ms/epoch - 4ms/step
Epoch 164/400
16/16 - 0s - loss: 0.5902 - accuracy: 0.7100 - val_loss: 0.5667 - val_accuracy: 0.7660 - 104ms/epoch - 6ms/step
Epoch 165/400
16/16 - 0s - loss: 0.5887 - accuracy: 0.7100 - val_loss: 0.5651 - val_accuracy: 0.7680 - 77ms/epoch - 5ms/step
Epoch 166/400
16/16 - 0s - loss: 0.5870 - accuracy: 0.7100 - val_loss: 0.5636 - val_accuracy: 0.7700 - 64ms/epoch - 4ms/step
Epoch 167/400
16/16 - 0s - loss: 0.5856 - accuracy: 0.7060 - val_loss: 0.5618 - val_accuracy: 0.7640 - 104ms/epoch - 6ms/step
Epoch 168/400
16/16 - 0s - loss: 0.5840 - accuracy: 0.7140 - val_loss: 0.5604 - val_accuracy: 0.7620 - 64ms/epoch - 4ms/step
Epoch 169/400
16/16 - 0s - loss: 0.5824 - accuracy: 0.7140 - val_loss: 0.5589 - val_accuracy: 0.7620 - 64ms/epoch - 4ms/step
Epoch 170/400
16/16 - 0s - loss: 0.5809 - accuracy: 0.7160 - val_loss: 0.5574 - val_accuracy: 0.7660 - 67ms/epoch - 4ms/step
Epoch 171/400
16/16 - 0s - loss: 0.5795 - accuracy: 0.7180 - val_loss: 0.5560 - val_accuracy: 0.7640 - 64ms/epoch - 4ms/step
Epoch 172/400
16/16 - 0s - loss: 0.5779 - accuracy: 0.7220 - val_loss: 0.5545 - val_accuracy: 0.7660 - 105ms/epoch - 7ms/step
Epoch 173/400
16/16 - 0s - loss: 0.5766 - accuracy: 0.7260 - val_loss: 0.5530 - val_accuracy: 0.7620 - 106ms/epoch - 7ms/step
Epoch 174/400
16/16 - 0s - loss: 0.5751 - accuracy: 0.7260 - val_loss: 0.5515 - val_accuracy: 0.7580 - 73ms/epoch - 5ms/step
Epoch 175/400
16/16 - 0s - loss: 0.5736 - accuracy: 0.7260 - val_loss: 0.5503 - val_accuracy: 0.7620 - 67ms/epoch - 4ms/step
Epoch 176/400
16/16 - 0s - loss: 0.5724 - accuracy: 0.7280 - val_loss: 0.5489 - val_accuracy: 0.7640 - 64ms/epoch - 4ms/step
Epoch 177/400
16/16 - 0s - loss: 0.5709 - accuracy: 0.7320 - val_loss: 0.5476 - val_accuracy: 0.7640 - 113ms/epoch - 7ms/step
Epoch 178/400
16/16 - 0s - loss: 0.5696 - accuracy: 0.7360 - val_loss: 0.5462 - val_accuracy: 0.7660 - 77ms/epoch - 5ms/step
Epoch 179/400
16/16 - 0s - loss: 0.5683 - accuracy: 0.7360 - val_loss: 0.5448 - val_accuracy: 0.7620 - 64ms/epoch - 4ms/step
Epoch 180/400
16/16 - 0s - loss: 0.5669 - accuracy: 0.7380 - val_loss: 0.5435 - val_accuracy: 0.7620 - 68ms/epoch - 4ms/step
Epoch 181/400
16/16 - 0s - loss: 0.5657 - accuracy: 0.7380 - val_loss: 0.5421 - val_accuracy: 0.7640 - 68ms/epoch - 4ms/step
Epoch 182/400
16/16 - 0s - loss: 0.5644 - accuracy: 0.7340 - val_loss: 0.5407 - val_accuracy: 0.7640 - 66ms/epoch - 4ms/step
Epoch 183/400
16/16 - 0s - loss: 0.5631 - accuracy: 0.7360 - val_loss: 0.5395 - val_accuracy: 0.7620 - 65ms/epoch - 4ms/step
Epoch 184/400
16/16 - 0s - loss: 0.5618 - accuracy: 0.7400 - val_loss: 0.5383 - val_accuracy: 0.7620 - 63ms/epoch - 4ms/step
Epoch 185/400
16/16 - 0s - loss: 0.5606 - accuracy: 0.7480 - val_loss: 0.5372 - val_accuracy: 0.7640 - 106ms/epoch - 7ms/step
Epoch 186/400
16/16 - 0s - loss: 0.5593 - accuracy: 0.7480 - val_loss: 0.5360 - val_accuracy: 0.7680 - 68ms/epoch - 4ms/step
Epoch 187/400
16/16 - 0s - loss: 0.5581 - accuracy: 0.7480 - val_loss: 0.5347 - val_accuracy: 0.7700 - 71ms/epoch - 4ms/step
Epoch 188/400
16/16 - 0s - loss: 0.5570 - accuracy: 0.7480 - val_loss: 0.5335 - val_accuracy: 0.7740 - 111ms/epoch - 7ms/step
Epoch 189/400
16/16 - 0s - loss: 0.5558 - accuracy: 0.7500 - val_loss: 0.5323 - val_accuracy: 0.7720 - 66ms/epoch - 4ms/step
Epoch 190/400
16/16 - 0s - loss: 0.5546 - accuracy: 0.7500 - val_loss: 0.5311 - val_accuracy: 0.7720 - 114ms/epoch - 7ms/step
Epoch 191/400
16/16 - 0s - loss: 0.5534 - accuracy: 0.7540 - val_loss: 0.5300 - val_accuracy: 0.7800 - 70ms/epoch - 4ms/step
Epoch 192/400
16/16 - 0s - loss: 0.5523 - accuracy: 0.7580 - val_loss: 0.5288 - val_accuracy: 0.7800 - 106ms/epoch - 7ms/step
Epoch 193/400
16/16 - 0s - loss: 0.5512 - accuracy: 0.7620 - val_loss: 0.5278 - val_accuracy: 0.7820 - 70ms/epoch - 4ms/step
Epoch 194/400
16/16 - 0s - loss: 0.5502 - accuracy: 0.7620 - val_loss: 0.5266 - val_accuracy: 0.7800 - 64ms/epoch - 4ms/step
Epoch 195/400
16/16 - 0s - loss: 0.5491 - accuracy: 0.7620 - val_loss: 0.5255 - val_accuracy: 0.7880 - 108ms/epoch - 7ms/step
Epoch 196/400
16/16 - 0s - loss: 0.5480 - accuracy: 0.7620 - val_loss: 0.5244 - val_accuracy: 0.7900 - 65ms/epoch - 4ms/step
Epoch 197/400
16/16 - 0s - loss: 0.5470 - accuracy: 0.7620 - val_loss: 0.5234 - val_accuracy: 0.7900 - 73ms/epoch - 5ms/step
Epoch 198/400
16/16 - 0s - loss: 0.5459 - accuracy: 0.7680 - val_loss: 0.5224 - val_accuracy: 0.7900 - 69ms/epoch - 4ms/step
Epoch 199/400
16/16 - 0s - loss: 0.5449 - accuracy: 0.7680 - val_loss: 0.5212 - val_accuracy: 0.7860 - 73ms/epoch - 5ms/step
Epoch 200/400
16/16 - 0s - loss: 0.5439 - accuracy: 0.7660 - val_loss: 0.5201 - val_accuracy: 0.7900 - 74ms/epoch - 5ms/step
Epoch 201/400
16/16 - 0s - loss: 0.5430 - accuracy: 0.7680 - val_loss: 0.5193 - val_accuracy: 0.7900 - 69ms/epoch - 4ms/step
Epoch 202/400
16/16 - 0s - loss: 0.5420 - accuracy: 0.7740 - val_loss: 0.5183 - val_accuracy: 0.7900 - 121ms/epoch - 8ms/step
Epoch 203/400
16/16 - 0s - loss: 0.5410 - accuracy: 0.7760 - val_loss: 0.5172 - val_accuracy: 0.7900 - 68ms/epoch - 4ms/step
Epoch 204/400
16/16 - 0s - loss: 0.5400 - accuracy: 0.7760 - val_loss: 0.5162 - val_accuracy: 0.7920 - 113ms/epoch - 7ms/step
Epoch 205/400
16/16 - 0s - loss: 0.5391 - accuracy: 0.7780 - val_loss: 0.5152 - val_accuracy: 0.7960 - 72ms/epoch - 5ms/step
Epoch 206/400
16/16 - 0s - loss: 0.5382 - accuracy: 0.7840 - val_loss: 0.5143 - val_accuracy: 0.7980 - 70ms/epoch - 4ms/step
Epoch 207/400
16/16 - 0s - loss: 0.5373 - accuracy: 0.7860 - val_loss: 0.5133 - val_accuracy: 0.7980 - 66ms/epoch - 4ms/step
Epoch 208/400
16/16 - 0s - loss: 0.5364 - accuracy: 0.7860 - val_loss: 0.5125 - val_accuracy: 0.7980 - 72ms/epoch - 4ms/step
Epoch 209/400
16/16 - 0s - loss: 0.5355 - accuracy: 0.7880 - val_loss: 0.5114 - val_accuracy: 0.7980 - 71ms/epoch - 4ms/step
Epoch 210/400
16/16 - 0s - loss: 0.5346 - accuracy: 0.7880 - val_loss: 0.5105 - val_accuracy: 0.7980 - 74ms/epoch - 5ms/step
Epoch 211/400
16/16 - 0s - loss: 0.5337 - accuracy: 0.7940 - val_loss: 0.5097 - val_accuracy: 0.8000 - 69ms/epoch - 4ms/step
Epoch 212/400
16/16 - 0s - loss: 0.5329 - accuracy: 0.7940 - val_loss: 0.5087 - val_accuracy: 0.8000 - 109ms/epoch - 7ms/step
Epoch 213/400
16/16 - 0s - loss: 0.5320 - accuracy: 0.7960 - val_loss: 0.5079 - val_accuracy: 0.8000 - 106ms/epoch - 7ms/step
Epoch 214/400
16/16 - 0s - loss: 0.5312 - accuracy: 0.8020 - val_loss: 0.5071 - val_accuracy: 0.7960 - 72ms/epoch - 5ms/step
Epoch 215/400
16/16 - 0s - loss: 0.5304 - accuracy: 0.8000 - val_loss: 0.5061 - val_accuracy: 0.7960 - 65ms/epoch - 4ms/step
Epoch 216/400
16/16 - 0s - loss: 0.5296 - accuracy: 0.7980 - val_loss: 0.5052 - val_accuracy: 0.7960 - 69ms/epoch - 4ms/step
Epoch 217/400
16/16 - 0s - loss: 0.5288 - accuracy: 0.7960 - val_loss: 0.5042 - val_accuracy: 0.7980 - 64ms/epoch - 4ms/step
Epoch 218/400
16/16 - 0s - loss: 0.5279 - accuracy: 0.7980 - val_loss: 0.5034 - val_accuracy: 0.7980 - 71ms/epoch - 4ms/step
Epoch 219/400
16/16 - 0s - loss: 0.5272 - accuracy: 0.8000 - val_loss: 0.5027 - val_accuracy: 0.8020 - 66ms/epoch - 4ms/step
Epoch 220/400
16/16 - 0s - loss: 0.5265 - accuracy: 0.8000 - val_loss: 0.5019 - val_accuracy: 0.8000 - 65ms/epoch - 4ms/step
Epoch 221/400
16/16 - 0s - loss: 0.5257 - accuracy: 0.8000 - val_loss: 0.5010 - val_accuracy: 0.8000 - 69ms/epoch - 4ms/step
Epoch 222/400
16/16 - 0s - loss: 0.5249 - accuracy: 0.8000 - val_loss: 0.5002 - val_accuracy: 0.7980 - 65ms/epoch - 4ms/step
Epoch 223/400
16/16 - 0s - loss: 0.5242 - accuracy: 0.8020 - val_loss: 0.4995 - val_accuracy: 0.8020 - 68ms/epoch - 4ms/step
Epoch 224/400
16/16 - 0s - loss: 0.5235 - accuracy: 0.8040 - val_loss: 0.4987 - val_accuracy: 0.8020 - 66ms/epoch - 4ms/step
Epoch 225/400
16/16 - 0s - loss: 0.5228 - accuracy: 0.8040 - val_loss: 0.4978 - val_accuracy: 0.8020 - 108ms/epoch - 7ms/step
Epoch 226/400
16/16 - 0s - loss: 0.5222 - accuracy: 0.8020 - val_loss: 0.4969 - val_accuracy: 0.7980 - 113ms/epoch - 7ms/step
Epoch 227/400
16/16 - 0s - loss: 0.5214 - accuracy: 0.8040 - val_loss: 0.4962 - val_accuracy: 0.8020 - 68ms/epoch - 4ms/step
Epoch 228/400
16/16 - 0s - loss: 0.5207 - accuracy: 0.8060 - val_loss: 0.4955 - val_accuracy: 0.8020 - 69ms/epoch - 4ms/step
Epoch 229/400
16/16 - 0s - loss: 0.5201 - accuracy: 0.8020 - val_loss: 0.4947 - val_accuracy: 0.8020 - 108ms/epoch - 7ms/step
Epoch 230/400
16/16 - 0s - loss: 0.5194 - accuracy: 0.8060 - val_loss: 0.4940 - val_accuracy: 0.8020 - 67ms/epoch - 4ms/step
Epoch 231/400
16/16 - 0s - loss: 0.5187 - accuracy: 0.8040 - val_loss: 0.4933 - val_accuracy: 0.8020 - 67ms/epoch - 4ms/step
Epoch 232/400
16/16 - 0s - loss: 0.5181 - accuracy: 0.8020 - val_loss: 0.4926 - val_accuracy: 0.8020 - 68ms/epoch - 4ms/step
Epoch 233/400
16/16 - 0s - loss: 0.5174 - accuracy: 0.8020 - val_loss: 0.4918 - val_accuracy: 0.8020 - 66ms/epoch - 4ms/step
Epoch 234/400
16/16 - 0s - loss: 0.5168 - accuracy: 0.8020 - val_loss: 0.4911 - val_accuracy: 0.8020 - 64ms/epoch - 4ms/step
Epoch 235/400
16/16 - 0s - loss: 0.5161 - accuracy: 0.8020 - val_loss: 0.4903 - val_accuracy: 0.8020 - 67ms/epoch - 4ms/step
Epoch 236/400
16/16 - 0s - loss: 0.5155 - accuracy: 0.8020 - val_loss: 0.4897 - val_accuracy: 0.8020 - 71ms/epoch - 4ms/step
Epoch 237/400
16/16 - 0s - loss: 0.5149 - accuracy: 0.8000 - val_loss: 0.4890 - val_accuracy: 0.8020 - 65ms/epoch - 4ms/step
Epoch 238/400
16/16 - 0s - loss: 0.5143 - accuracy: 0.7980 - val_loss: 0.4883 - val_accuracy: 0.8060 - 65ms/epoch - 4ms/step
Epoch 239/400
16/16 - 0s - loss: 0.5136 - accuracy: 0.7980 - val_loss: 0.4877 - val_accuracy: 0.8060 - 105ms/epoch - 7ms/step
Epoch 240/400
16/16 - 0s - loss: 0.5131 - accuracy: 0.8000 - val_loss: 0.4869 - val_accuracy: 0.8060 - 113ms/epoch - 7ms/step
Epoch 241/400
16/16 - 0s - loss: 0.5124 - accuracy: 0.7960 - val_loss: 0.4863 - val_accuracy: 0.8060 - 66ms/epoch - 4ms/step
Epoch 242/400
16/16 - 0s - loss: 0.5119 - accuracy: 0.7980 - val_loss: 0.4855 - val_accuracy: 0.8060 - 74ms/epoch - 5ms/step
Epoch 243/400
16/16 - 0s - loss: 0.5113 - accuracy: 0.7960 - val_loss: 0.4849 - val_accuracy: 0.8060 - 70ms/epoch - 4ms/step
Epoch 244/400
16/16 - 0s - loss: 0.5107 - accuracy: 0.7980 - val_loss: 0.4842 - val_accuracy: 0.8060 - 65ms/epoch - 4ms/step
Epoch 245/400
16/16 - 0s - loss: 0.5101 - accuracy: 0.7980 - val_loss: 0.4836 - val_accuracy: 0.8040 - 64ms/epoch - 4ms/step
Epoch 246/400
16/16 - 0s - loss: 0.5096 - accuracy: 0.7980 - val_loss: 0.4829 - val_accuracy: 0.8040 - 69ms/epoch - 4ms/step
Epoch 247/400
16/16 - 0s - loss: 0.5090 - accuracy: 0.8000 - val_loss: 0.4823 - val_accuracy: 0.8040 - 67ms/epoch - 4ms/step
Epoch 248/400
16/16 - 0s - loss: 0.5085 - accuracy: 0.8020 - val_loss: 0.4817 - val_accuracy: 0.8040 - 108ms/epoch - 7ms/step
Epoch 249/400
16/16 - 0s - loss: 0.5080 - accuracy: 0.8000 - val_loss: 0.4811 - val_accuracy: 0.8040 - 69ms/epoch - 4ms/step
Epoch 250/400
16/16 - 0s - loss: 0.5074 - accuracy: 0.8000 - val_loss: 0.4803 - val_accuracy: 0.8040 - 69ms/epoch - 4ms/step
Epoch 251/400
16/16 - 0s - loss: 0.5069 - accuracy: 0.7980 - val_loss: 0.4796 - val_accuracy: 0.8060 - 74ms/epoch - 5ms/step
Epoch 252/400
16/16 - 0s - loss: 0.5064 - accuracy: 0.8020 - val_loss: 0.4789 - val_accuracy: 0.8060 - 68ms/epoch - 4ms/step
Epoch 253/400
16/16 - 0s - loss: 0.5059 - accuracy: 0.8000 - val_loss: 0.4784 - val_accuracy: 0.8060 - 107ms/epoch - 7ms/step
Epoch 254/400
16/16 - 0s - loss: 0.5054 - accuracy: 0.8000 - val_loss: 0.4778 - val_accuracy: 0.8060 - 68ms/epoch - 4ms/step
Epoch 255/400
16/16 - 0s - loss: 0.5049 - accuracy: 0.8020 - val_loss: 0.4772 - val_accuracy: 0.8080 - 66ms/epoch - 4ms/step
Epoch 256/400
16/16 - 0s - loss: 0.5044 - accuracy: 0.8020 - val_loss: 0.4766 - val_accuracy: 0.8080 - 107ms/epoch - 7ms/step
Epoch 257/400
16/16 - 0s - loss: 0.5038 - accuracy: 0.8040 - val_loss: 0.4760 - val_accuracy: 0.8080 - 68ms/epoch - 4ms/step
Epoch 258/400
16/16 - 0s - loss: 0.5034 - accuracy: 0.8040 - val_loss: 0.4754 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 259/400
16/16 - 0s - loss: 0.5029 - accuracy: 0.8060 - val_loss: 0.4748 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 260/400
16/16 - 0s - loss: 0.5024 - accuracy: 0.8060 - val_loss: 0.4741 - val_accuracy: 0.8140 - 65ms/epoch - 4ms/step
Epoch 261/400
16/16 - 0s - loss: 0.5020 - accuracy: 0.8080 - val_loss: 0.4737 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 262/400
16/16 - 0s - loss: 0.5016 - accuracy: 0.8100 - val_loss: 0.4732 - val_accuracy: 0.8140 - 107ms/epoch - 7ms/step
Epoch 263/400
16/16 - 0s - loss: 0.5010 - accuracy: 0.8100 - val_loss: 0.4726 - val_accuracy: 0.8120 - 70ms/epoch - 4ms/step
Epoch 264/400
16/16 - 0s - loss: 0.5006 - accuracy: 0.8100 - val_loss: 0.4719 - val_accuracy: 0.8100 - 125ms/epoch - 8ms/step
Epoch 265/400
16/16 - 0s - loss: 0.5001 - accuracy: 0.8060 - val_loss: 0.4713 - val_accuracy: 0.8120 - 71ms/epoch - 4ms/step
Epoch 266/400
16/16 - 0s - loss: 0.4998 - accuracy: 0.8100 - val_loss: 0.4708 - val_accuracy: 0.8100 - 68ms/epoch - 4ms/step
Epoch 267/400
16/16 - 0s - loss: 0.4992 - accuracy: 0.8100 - val_loss: 0.4703 - val_accuracy: 0.8100 - 105ms/epoch - 7ms/step
Epoch 268/400
16/16 - 0s - loss: 0.4989 - accuracy: 0.8100 - val_loss: 0.4697 - val_accuracy: 0.8100 - 68ms/epoch - 4ms/step
Epoch 269/400
16/16 - 0s - loss: 0.4984 - accuracy: 0.8080 - val_loss: 0.4691 - val_accuracy: 0.8100 - 65ms/epoch - 4ms/step
Epoch 270/400
16/16 - 0s - loss: 0.4980 - accuracy: 0.8100 - val_loss: 0.4687 - val_accuracy: 0.8120 - 72ms/epoch - 5ms/step
Epoch 271/400
16/16 - 0s - loss: 0.4976 - accuracy: 0.8100 - val_loss: 0.4682 - val_accuracy: 0.8120 - 71ms/epoch - 4ms/step
Epoch 272/400
16/16 - 0s - loss: 0.4972 - accuracy: 0.8100 - val_loss: 0.4676 - val_accuracy: 0.8100 - 71ms/epoch - 4ms/step
Epoch 273/400
16/16 - 0s - loss: 0.4967 - accuracy: 0.8120 - val_loss: 0.4672 - val_accuracy: 0.8120 - 69ms/epoch - 4ms/step
Epoch 274/400
16/16 - 0s - loss: 0.4964 - accuracy: 0.8140 - val_loss: 0.4668 - val_accuracy: 0.8140 - 67ms/epoch - 4ms/step
Epoch 275/400
16/16 - 0s - loss: 0.4959 - accuracy: 0.8140 - val_loss: 0.4663 - val_accuracy: 0.8120 - 66ms/epoch - 4ms/step
Epoch 276/400
16/16 - 0s - loss: 0.4956 - accuracy: 0.8140 - val_loss: 0.4658 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 277/400
16/16 - 0s - loss: 0.4951 - accuracy: 0.8140 - val_loss: 0.4652 - val_accuracy: 0.8120 - 75ms/epoch - 5ms/step
Epoch 278/400
16/16 - 0s - loss: 0.4948 - accuracy: 0.8140 - val_loss: 0.4647 - val_accuracy: 0.8120 - 109ms/epoch - 7ms/step
Epoch 279/400
16/16 - 0s - loss: 0.4943 - accuracy: 0.8120 - val_loss: 0.4642 - val_accuracy: 0.8120 - 65ms/epoch - 4ms/step
Epoch 280/400
16/16 - 0s - loss: 0.4940 - accuracy: 0.8120 - val_loss: 0.4636 - val_accuracy: 0.8120 - 67ms/epoch - 4ms/step
Epoch 281/400
16/16 - 0s - loss: 0.4936 - accuracy: 0.8100 - val_loss: 0.4631 - val_accuracy: 0.8140 - 65ms/epoch - 4ms/step
Epoch 282/400
16/16 - 0s - loss: 0.4931 - accuracy: 0.8100 - val_loss: 0.4628 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 283/400
16/16 - 0s - loss: 0.4928 - accuracy: 0.8100 - val_loss: 0.4622 - val_accuracy: 0.8140 - 66ms/epoch - 4ms/step
Epoch 284/400
16/16 - 0s - loss: 0.4923 - accuracy: 0.8100 - val_loss: 0.4617 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 285/400
16/16 - 0s - loss: 0.4920 - accuracy: 0.8060 - val_loss: 0.4612 - val_accuracy: 0.8160 - 109ms/epoch - 7ms/step
Epoch 286/400
16/16 - 0s - loss: 0.4915 - accuracy: 0.8080 - val_loss: 0.4607 - val_accuracy: 0.8140 - 70ms/epoch - 4ms/step
Epoch 287/400
16/16 - 0s - loss: 0.4911 - accuracy: 0.8080 - val_loss: 0.4602 - val_accuracy: 0.8140 - 111ms/epoch - 7ms/step
Epoch 288/400
16/16 - 0s - loss: 0.4907 - accuracy: 0.8080 - val_loss: 0.4596 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 289/400
16/16 - 0s - loss: 0.4903 - accuracy: 0.8080 - val_loss: 0.4591 - val_accuracy: 0.8140 - 78ms/epoch - 5ms/step
Epoch 290/400
16/16 - 0s - loss: 0.4899 - accuracy: 0.8060 - val_loss: 0.4585 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 291/400
16/16 - 0s - loss: 0.4895 - accuracy: 0.8080 - val_loss: 0.4581 - val_accuracy: 0.8140 - 68ms/epoch - 4ms/step
Epoch 292/400
16/16 - 0s - loss: 0.4891 - accuracy: 0.8060 - val_loss: 0.4575 - val_accuracy: 0.8160 - 109ms/epoch - 7ms/step
Epoch 293/400
16/16 - 0s - loss: 0.4887 - accuracy: 0.8080 - val_loss: 0.4570 - val_accuracy: 0.8160 - 64ms/epoch - 4ms/step
Epoch 294/400
16/16 - 0s - loss: 0.4882 - accuracy: 0.8060 - val_loss: 0.4564 - val_accuracy: 0.8140 - 73ms/epoch - 5ms/step
Epoch 295/400
16/16 - 0s - loss: 0.4878 - accuracy: 0.8060 - val_loss: 0.4559 - val_accuracy: 0.8140 - 75ms/epoch - 5ms/step
Epoch 296/400
16/16 - 0s - loss: 0.4873 - accuracy: 0.8040 - val_loss: 0.4553 - val_accuracy: 0.8140 - 114ms/epoch - 7ms/step
Epoch 297/400
16/16 - 0s - loss: 0.4870 - accuracy: 0.8060 - val_loss: 0.4549 - val_accuracy: 0.8160 - 114ms/epoch - 7ms/step
Epoch 298/400
16/16 - 0s - loss: 0.4864 - accuracy: 0.8080 - val_loss: 0.4543 - val_accuracy: 0.8140 - 68ms/epoch - 4ms/step
Epoch 299/400
16/16 - 0s - loss: 0.4860 - accuracy: 0.8060 - val_loss: 0.4537 - val_accuracy: 0.8140 - 76ms/epoch - 5ms/step
Epoch 300/400
16/16 - 0s - loss: 0.4855 - accuracy: 0.8040 - val_loss: 0.4531 - val_accuracy: 0.8120 - 65ms/epoch - 4ms/step
Epoch 301/400
16/16 - 0s - loss: 0.4850 - accuracy: 0.8040 - val_loss: 0.4525 - val_accuracy: 0.8120 - 78ms/epoch - 5ms/step
Epoch 302/400
16/16 - 0s - loss: 0.4845 - accuracy: 0.8020 - val_loss: 0.4519 - val_accuracy: 0.8120 - 66ms/epoch - 4ms/step
Epoch 303/400
16/16 - 0s - loss: 0.4841 - accuracy: 0.8020 - val_loss: 0.4514 - val_accuracy: 0.8140 - 65ms/epoch - 4ms/step
Epoch 304/400
16/16 - 0s - loss: 0.4837 - accuracy: 0.8020 - val_loss: 0.4507 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 305/400
16/16 - 0s - loss: 0.4831 - accuracy: 0.8020 - val_loss: 0.4503 - val_accuracy: 0.8140 - 107ms/epoch - 7ms/step
Epoch 306/400
16/16 - 0s - loss: 0.4826 - accuracy: 0.8020 - val_loss: 0.4496 - val_accuracy: 0.8140 - 72ms/epoch - 4ms/step
Epoch 307/400
16/16 - 0s - loss: 0.4822 - accuracy: 0.8020 - val_loss: 0.4490 - val_accuracy: 0.8100 - 108ms/epoch - 7ms/step
Epoch 308/400
16/16 - 0s - loss: 0.4817 - accuracy: 0.8020 - val_loss: 0.4486 - val_accuracy: 0.8140 - 109ms/epoch - 7ms/step
Epoch 309/400
16/16 - 0s - loss: 0.4811 - accuracy: 0.8020 - val_loss: 0.4479 - val_accuracy: 0.8140 - 69ms/epoch - 4ms/step
Epoch 310/400
16/16 - 0s - loss: 0.4806 - accuracy: 0.8020 - val_loss: 0.4473 - val_accuracy: 0.8120 - 65ms/epoch - 4ms/step
Epoch 311/400
16/16 - 0s - loss: 0.4801 - accuracy: 0.8020 - val_loss: 0.4468 - val_accuracy: 0.8120 - 64ms/epoch - 4ms/step
Epoch 312/400
16/16 - 0s - loss: 0.4796 - accuracy: 0.8020 - val_loss: 0.4462 - val_accuracy: 0.8120 - 72ms/epoch - 4ms/step
Epoch 313/400
16/16 - 0s - loss: 0.4792 - accuracy: 0.8020 - val_loss: 0.4456 - val_accuracy: 0.8140 - 77ms/epoch - 5ms/step
Epoch 314/400
16/16 - 0s - loss: 0.4785 - accuracy: 0.8000 - val_loss: 0.4450 - val_accuracy: 0.8120 - 70ms/epoch - 4ms/step
Epoch 315/400
16/16 - 0s - loss: 0.4779 - accuracy: 0.7980 - val_loss: 0.4444 - val_accuracy: 0.8120 - 105ms/epoch - 7ms/step
Epoch 316/400
16/16 - 0s - loss: 0.4774 - accuracy: 0.8000 - val_loss: 0.4437 - val_accuracy: 0.8120 - 66ms/epoch - 4ms/step
Epoch 317/400
16/16 - 0s - loss: 0.4768 - accuracy: 0.7980 - val_loss: 0.4431 - val_accuracy: 0.8120 - 66ms/epoch - 4ms/step
Epoch 318/400
16/16 - 0s - loss: 0.4763 - accuracy: 0.7960 - val_loss: 0.4424 - val_accuracy: 0.8120 - 76ms/epoch - 5ms/step
Epoch 319/400
16/16 - 0s - loss: 0.4757 - accuracy: 0.7960 - val_loss: 0.4417 - val_accuracy: 0.8120 - 65ms/epoch - 4ms/step
Epoch 320/400
16/16 - 0s - loss: 0.4751 - accuracy: 0.7960 - val_loss: 0.4411 - val_accuracy: 0.8140 - 71ms/epoch - 4ms/step
Epoch 321/400
16/16 - 0s - loss: 0.4745 - accuracy: 0.7980 - val_loss: 0.4404 - val_accuracy: 0.8140 - 65ms/epoch - 4ms/step
Epoch 322/400
16/16 - 0s - loss: 0.4738 - accuracy: 0.8000 - val_loss: 0.4397 - val_accuracy: 0.8120 - 64ms/epoch - 4ms/step
Epoch 323/400
16/16 - 0s - loss: 0.4732 - accuracy: 0.8020 - val_loss: 0.4390 - val_accuracy: 0.8120 - 66ms/epoch - 4ms/step
Epoch 324/400
16/16 - 0s - loss: 0.4726 - accuracy: 0.8020 - val_loss: 0.4384 - val_accuracy: 0.8120 - 69ms/epoch - 4ms/step
Epoch 325/400
16/16 - 0s - loss: 0.4719 - accuracy: 0.8060 - val_loss: 0.4376 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 326/400
16/16 - 0s - loss: 0.4711 - accuracy: 0.8060 - val_loss: 0.4368 - val_accuracy: 0.8100 - 74ms/epoch - 5ms/step
Epoch 327/400
16/16 - 0s - loss: 0.4705 - accuracy: 0.8040 - val_loss: 0.4359 - val_accuracy: 0.8080 - 107ms/epoch - 7ms/step
Epoch 328/400
16/16 - 0s - loss: 0.4697 - accuracy: 0.8040 - val_loss: 0.4352 - val_accuracy: 0.8080 - 65ms/epoch - 4ms/step
Epoch 329/400
16/16 - 0s - loss: 0.4690 - accuracy: 0.8060 - val_loss: 0.4345 - val_accuracy: 0.8100 - 61ms/epoch - 4ms/step
Epoch 330/400
16/16 - 0s - loss: 0.4685 - accuracy: 0.8020 - val_loss: 0.4336 - val_accuracy: 0.8060 - 66ms/epoch - 4ms/step
Epoch 331/400
16/16 - 0s - loss: 0.4675 - accuracy: 0.8040 - val_loss: 0.4329 - val_accuracy: 0.8080 - 64ms/epoch - 4ms/step
Epoch 332/400
16/16 - 0s - loss: 0.4669 - accuracy: 0.8020 - val_loss: 0.4321 - val_accuracy: 0.8100 - 111ms/epoch - 7ms/step
Epoch 333/400
16/16 - 0s - loss: 0.4661 - accuracy: 0.8020 - val_loss: 0.4312 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 334/400
16/16 - 0s - loss: 0.4653 - accuracy: 0.8020 - val_loss: 0.4305 - val_accuracy: 0.8100 - 64ms/epoch - 4ms/step
Epoch 335/400
16/16 - 0s - loss: 0.4645 - accuracy: 0.8020 - val_loss: 0.4297 - val_accuracy: 0.8100 - 66ms/epoch - 4ms/step
Epoch 336/400
16/16 - 0s - loss: 0.4638 - accuracy: 0.8000 - val_loss: 0.4288 - val_accuracy: 0.8100 - 66ms/epoch - 4ms/step
Epoch 337/400
16/16 - 0s - loss: 0.4629 - accuracy: 0.8000 - val_loss: 0.4280 - val_accuracy: 0.8100 - 71ms/epoch - 4ms/step
Epoch 338/400
16/16 - 0s - loss: 0.4622 - accuracy: 0.8000 - val_loss: 0.4271 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 339/400
16/16 - 0s - loss: 0.4613 - accuracy: 0.8000 - val_loss: 0.4263 - val_accuracy: 0.8140 - 109ms/epoch - 7ms/step
Epoch 340/400
16/16 - 0s - loss: 0.4604 - accuracy: 0.8000 - val_loss: 0.4254 - val_accuracy: 0.8140 - 63ms/epoch - 4ms/step
Epoch 341/400
16/16 - 0s - loss: 0.4595 - accuracy: 0.8000 - val_loss: 0.4244 - val_accuracy: 0.8140 - 109ms/epoch - 7ms/step
Epoch 342/400
16/16 - 0s - loss: 0.4585 - accuracy: 0.8040 - val_loss: 0.4235 - val_accuracy: 0.8140 - 74ms/epoch - 5ms/step
Epoch 343/400
16/16 - 0s - loss: 0.4576 - accuracy: 0.8040 - val_loss: 0.4225 - val_accuracy: 0.8140 - 70ms/epoch - 4ms/step
Epoch 344/400
16/16 - 0s - loss: 0.4567 - accuracy: 0.8040 - val_loss: 0.4215 - val_accuracy: 0.8120 - 70ms/epoch - 4ms/step
Epoch 345/400
16/16 - 0s - loss: 0.4557 - accuracy: 0.8040 - val_loss: 0.4206 - val_accuracy: 0.8120 - 115ms/epoch - 7ms/step
Epoch 346/400
16/16 - 0s - loss: 0.4547 - accuracy: 0.8020 - val_loss: 0.4194 - val_accuracy: 0.8100 - 66ms/epoch - 4ms/step
Epoch 347/400
16/16 - 0s - loss: 0.4536 - accuracy: 0.8020 - val_loss: 0.4186 - val_accuracy: 0.8100 - 73ms/epoch - 5ms/step
Epoch 348/400
16/16 - 0s - loss: 0.4526 - accuracy: 0.8020 - val_loss: 0.4176 - val_accuracy: 0.8100 - 68ms/epoch - 4ms/step
Epoch 349/400
16/16 - 0s - loss: 0.4516 - accuracy: 0.8040 - val_loss: 0.4165 - val_accuracy: 0.8100 - 68ms/epoch - 4ms/step
Epoch 350/400
16/16 - 0s - loss: 0.4506 - accuracy: 0.8060 - val_loss: 0.4155 - val_accuracy: 0.8100 - 112ms/epoch - 7ms/step
Epoch 351/400
16/16 - 0s - loss: 0.4494 - accuracy: 0.8080 - val_loss: 0.4144 - val_accuracy: 0.8120 - 74ms/epoch - 5ms/step
Epoch 352/400
16/16 - 0s - loss: 0.4483 - accuracy: 0.8100 - val_loss: 0.4133 - val_accuracy: 0.8140 - 106ms/epoch - 7ms/step
Epoch 353/400
16/16 - 0s - loss: 0.4474 - accuracy: 0.8100 - val_loss: 0.4122 - val_accuracy: 0.8120 - 110ms/epoch - 7ms/step
Epoch 354/400
16/16 - 0s - loss: 0.4462 - accuracy: 0.8120 - val_loss: 0.4111 - val_accuracy: 0.8120 - 65ms/epoch - 4ms/step
Epoch 355/400
16/16 - 0s - loss: 0.4450 - accuracy: 0.8140 - val_loss: 0.4101 - val_accuracy: 0.8160 - 66ms/epoch - 4ms/step
Epoch 356/400
16/16 - 0s - loss: 0.4440 - accuracy: 0.8180 - val_loss: 0.4090 - val_accuracy: 0.8180 - 106ms/epoch - 7ms/step
Epoch 357/400
16/16 - 0s - loss: 0.4427 - accuracy: 0.8180 - val_loss: 0.4079 - val_accuracy: 0.8160 - 68ms/epoch - 4ms/step
Epoch 358/400
16/16 - 0s - loss: 0.4415 - accuracy: 0.8200 - val_loss: 0.4067 - val_accuracy: 0.8160 - 70ms/epoch - 4ms/step
Epoch 359/400
16/16 - 0s - loss: 0.4403 - accuracy: 0.8240 - val_loss: 0.4056 - val_accuracy: 0.8160 - 72ms/epoch - 4ms/step
Epoch 360/400
16/16 - 0s - loss: 0.4391 - accuracy: 0.8260 - val_loss: 0.4045 - val_accuracy: 0.8180 - 111ms/epoch - 7ms/step
Epoch 361/400
16/16 - 0s - loss: 0.4379 - accuracy: 0.8260 - val_loss: 0.4033 - val_accuracy: 0.8180 - 69ms/epoch - 4ms/step
Epoch 362/400
16/16 - 0s - loss: 0.4366 - accuracy: 0.8220 - val_loss: 0.4020 - val_accuracy: 0.8180 - 73ms/epoch - 5ms/step
Epoch 363/400
16/16 - 0s - loss: 0.4353 - accuracy: 0.8220 - val_loss: 0.4008 - val_accuracy: 0.8180 - 69ms/epoch - 4ms/step
Epoch 364/400
16/16 - 0s - loss: 0.4341 - accuracy: 0.8220 - val_loss: 0.3996 - val_accuracy: 0.8180 - 113ms/epoch - 7ms/step
Epoch 365/400
16/16 - 0s - loss: 0.4328 - accuracy: 0.8220 - val_loss: 0.3983 - val_accuracy: 0.8180 - 72ms/epoch - 5ms/step
Epoch 366/400
16/16 - 0s - loss: 0.4314 - accuracy: 0.8220 - val_loss: 0.3971 - val_accuracy: 0.8220 - 74ms/epoch - 5ms/step
Epoch 367/400
16/16 - 0s - loss: 0.4301 - accuracy: 0.8220 - val_loss: 0.3958 - val_accuracy: 0.8240 - 72ms/epoch - 4ms/step
Epoch 368/400
16/16 - 0s - loss: 0.4287 - accuracy: 0.8220 - val_loss: 0.3945 - val_accuracy: 0.8260 - 105ms/epoch - 7ms/step
Epoch 369/400
16/16 - 0s - loss: 0.4273 - accuracy: 0.8240 - val_loss: 0.3932 - val_accuracy: 0.8260 - 70ms/epoch - 4ms/step
Epoch 370/400
16/16 - 0s - loss: 0.4260 - accuracy: 0.8260 - val_loss: 0.3919 - val_accuracy: 0.8260 - 77ms/epoch - 5ms/step
Epoch 371/400
16/16 - 0s - loss: 0.4246 - accuracy: 0.8260 - val_loss: 0.3906 - val_accuracy: 0.8260 - 73ms/epoch - 5ms/step
Epoch 372/400
16/16 - 0s - loss: 0.4231 - accuracy: 0.8260 - val_loss: 0.3893 - val_accuracy: 0.8260 - 111ms/epoch - 7ms/step
Epoch 373/400
16/16 - 0s - loss: 0.4217 - accuracy: 0.8280 - val_loss: 0.3879 - val_accuracy: 0.8280 - 70ms/epoch - 4ms/step
Epoch 374/400
16/16 - 0s - loss: 0.4202 - accuracy: 0.8300 - val_loss: 0.3865 - val_accuracy: 0.8280 - 117ms/epoch - 7ms/step
Epoch 375/400
16/16 - 0s - loss: 0.4188 - accuracy: 0.8300 - val_loss: 0.3851 - val_accuracy: 0.8280 - 70ms/epoch - 4ms/step
Epoch 376/400
16/16 - 0s - loss: 0.4173 - accuracy: 0.8280 - val_loss: 0.3837 - val_accuracy: 0.8280 - 66ms/epoch - 4ms/step
Epoch 377/400
16/16 - 0s - loss: 0.4157 - accuracy: 0.8280 - val_loss: 0.3822 - val_accuracy: 0.8320 - 76ms/epoch - 5ms/step
Epoch 378/400
16/16 - 0s - loss: 0.4141 - accuracy: 0.8280 - val_loss: 0.3808 - val_accuracy: 0.8320 - 75ms/epoch - 5ms/step
Epoch 379/400
16/16 - 0s - loss: 0.4125 - accuracy: 0.8300 - val_loss: 0.3794 - val_accuracy: 0.8380 - 66ms/epoch - 4ms/step
Epoch 380/400
16/16 - 0s - loss: 0.4110 - accuracy: 0.8280 - val_loss: 0.3779 - val_accuracy: 0.8400 - 68ms/epoch - 4ms/step
Epoch 381/400
16/16 - 0s - loss: 0.4094 - accuracy: 0.8280 - val_loss: 0.3764 - val_accuracy: 0.8400 - 107ms/epoch - 7ms/step
Epoch 382/400
16/16 - 0s - loss: 0.4079 - accuracy: 0.8260 - val_loss: 0.3749 - val_accuracy: 0.8440 - 72ms/epoch - 4ms/step
Epoch 383/400
16/16 - 0s - loss: 0.4062 - accuracy: 0.8280 - val_loss: 0.3736 - val_accuracy: 0.8460 - 67ms/epoch - 4ms/step
Epoch 384/400
16/16 - 0s - loss: 0.4046 - accuracy: 0.8280 - val_loss: 0.3720 - val_accuracy: 0.8460 - 107ms/epoch - 7ms/step
Epoch 385/400
16/16 - 0s - loss: 0.4029 - accuracy: 0.8300 - val_loss: 0.3706 - val_accuracy: 0.8460 - 66ms/epoch - 4ms/step
Epoch 386/400
16/16 - 0s - loss: 0.4014 - accuracy: 0.8300 - val_loss: 0.3691 - val_accuracy: 0.8460 - 109ms/epoch - 7ms/step
Epoch 387/400
16/16 - 0s - loss: 0.3996 - accuracy: 0.8300 - val_loss: 0.3676 - val_accuracy: 0.8460 - 70ms/epoch - 4ms/step
Epoch 388/400
16/16 - 0s - loss: 0.3980 - accuracy: 0.8280 - val_loss: 0.3662 - val_accuracy: 0.8460 - 67ms/epoch - 4ms/step
Epoch 389/400
16/16 - 0s - loss: 0.3964 - accuracy: 0.8280 - val_loss: 0.3646 - val_accuracy: 0.8460 - 66ms/epoch - 4ms/step
Epoch 390/400
16/16 - 0s - loss: 0.3946 - accuracy: 0.8300 - val_loss: 0.3631 - val_accuracy: 0.8460 - 69ms/epoch - 4ms/step
Epoch 391/400
16/16 - 0s - loss: 0.3930 - accuracy: 0.8340 - val_loss: 0.3616 - val_accuracy: 0.8480 - 65ms/epoch - 4ms/step
Epoch 392/400
16/16 - 0s - loss: 0.3912 - accuracy: 0.8320 - val_loss: 0.3600 - val_accuracy: 0.8520 - 65ms/epoch - 4ms/step
Epoch 393/400
16/16 - 0s - loss: 0.3895 - accuracy: 0.8360 - val_loss: 0.3585 - val_accuracy: 0.8520 - 75ms/epoch - 5ms/step
Epoch 394/400
16/16 - 0s - loss: 0.3878 - accuracy: 0.8340 - val_loss: 0.3570 - val_accuracy: 0.8540 - 73ms/epoch - 5ms/step
Epoch 395/400
16/16 - 0s - loss: 0.3862 - accuracy: 0.8400 - val_loss: 0.3554 - val_accuracy: 0.8540 - 69ms/epoch - 4ms/step
Epoch 396/400
16/16 - 0s - loss: 0.3844 - accuracy: 0.8440 - val_loss: 0.3538 - val_accuracy: 0.8560 - 69ms/epoch - 4ms/step
Epoch 397/400
16/16 - 0s - loss: 0.3826 - accuracy: 0.8440 - val_loss: 0.3523 - val_accuracy: 0.8580 - 72ms/epoch - 4ms/step
Epoch 398/400
16/16 - 0s - loss: 0.3809 - accuracy: 0.8440 - val_loss: 0.3507 - val_accuracy: 0.8560 - 111ms/epoch - 7ms/step
Epoch 399/400
16/16 - 0s - loss: 0.3791 - accuracy: 0.8460 - val_loss: 0.3491 - val_accuracy: 0.8580 - 84ms/epoch - 5ms/step
Epoch 400/400
16/16 - 0s - loss: 0.3774 - accuracy: 0.8460 - val_loss: 0.3474 - val_accuracy: 0.8600 - 70ms/epoch - 4ms/step
第1个弱分类器训练完毕
Epoch 1/400
16/16 - 1s - loss: 0.6834 - accuracy: 0.3760 - val_loss: 0.6751 - val_accuracy: 0.4420 - 801ms/epoch - 50ms/step
Epoch 2/400
16/16 - 0s - loss: 0.6795 - accuracy: 0.3960 - val_loss: 0.6706 - val_accuracy: 0.4600 - 111ms/epoch - 7ms/step
Epoch 3/400
16/16 - 0s - loss: 0.6759 - accuracy: 0.4040 - val_loss: 0.6665 - val_accuracy: 0.4720 - 73ms/epoch - 5ms/step
Epoch 4/400
16/16 - 0s - loss: 0.6724 - accuracy: 0.4200 - val_loss: 0.6627 - val_accuracy: 0.4900 - 70ms/epoch - 4ms/step
Epoch 5/400
16/16 - 0s - loss: 0.6692 - accuracy: 0.4300 - val_loss: 0.6587 - val_accuracy: 0.5140 - 70ms/epoch - 4ms/step
Epoch 6/400
16/16 - 0s - loss: 0.6658 - accuracy: 0.4520 - val_loss: 0.6551 - val_accuracy: 0.5260 - 67ms/epoch - 4ms/step
Epoch 7/400
16/16 - 0s - loss: 0.6627 - accuracy: 0.4720 - val_loss: 0.6518 - val_accuracy: 0.5260 - 81ms/epoch - 5ms/step
Epoch 8/400
16/16 - 0s - loss: 0.6596 - accuracy: 0.4740 - val_loss: 0.6483 - val_accuracy: 0.5260 - 109ms/epoch - 7ms/step
Epoch 9/400
16/16 - 0s - loss: 0.6565 - accuracy: 0.4740 - val_loss: 0.6448 - val_accuracy: 0.5260 - 67ms/epoch - 4ms/step
Epoch 10/400
16/16 - 0s - loss: 0.6534 - accuracy: 0.4740 - val_loss: 0.6415 - val_accuracy: 0.5260 - 70ms/epoch - 4ms/step
Epoch 11/400
16/16 - 0s - loss: 0.6504 - accuracy: 0.4740 - val_loss: 0.6380 - val_accuracy: 0.5260 - 68ms/epoch - 4ms/step
Epoch 12/400
16/16 - 0s - loss: 0.6474 - accuracy: 0.4840 - val_loss: 0.6349 - val_accuracy: 0.5360 - 117ms/epoch - 7ms/step
Epoch 13/400
16/16 - 0s - loss: 0.6445 - accuracy: 0.5080 - val_loss: 0.6317 - val_accuracy: 0.5600 - 78ms/epoch - 5ms/step
Epoch 14/400
16/16 - 0s - loss: 0.6416 - accuracy: 0.5460 - val_loss: 0.6283 - val_accuracy: 0.5900 - 111ms/epoch - 7ms/step
Epoch 15/400
16/16 - 0s - loss: 0.6386 - accuracy: 0.5740 - val_loss: 0.6252 - val_accuracy: 0.6120 - 66ms/epoch - 4ms/step
Epoch 16/400
16/16 - 0s - loss: 0.6357 - accuracy: 0.5900 - val_loss: 0.6222 - val_accuracy: 0.6280 - 77ms/epoch - 5ms/step
Epoch 17/400
16/16 - 0s - loss: 0.6329 - accuracy: 0.6000 - val_loss: 0.6191 - val_accuracy: 0.6300 - 111ms/epoch - 7ms/step
Epoch 18/400
16/16 - 0s - loss: 0.6300 - accuracy: 0.6100 - val_loss: 0.6160 - val_accuracy: 0.6400 - 111ms/epoch - 7ms/step
Epoch 19/400
16/16 - 0s - loss: 0.6272 - accuracy: 0.6160 - val_loss: 0.6129 - val_accuracy: 0.6400 - 114ms/epoch - 7ms/step
Epoch 20/400
16/16 - 0s - loss: 0.6243 - accuracy: 0.6120 - val_loss: 0.6099 - val_accuracy: 0.6300 - 72ms/epoch - 5ms/step
Epoch 21/400
16/16 - 0s - loss: 0.6215 - accuracy: 0.6180 - val_loss: 0.6069 - val_accuracy: 0.6280 - 108ms/epoch - 7ms/step
Epoch 22/400
16/16 - 0s - loss: 0.6187 - accuracy: 0.6180 - val_loss: 0.6039 - val_accuracy: 0.6280 - 68ms/epoch - 4ms/step
Epoch 23/400
16/16 - 0s - loss: 0.6159 - accuracy: 0.6160 - val_loss: 0.6008 - val_accuracy: 0.6260 - 109ms/epoch - 7ms/step
Epoch 24/400
16/16 - 0s - loss: 0.6131 - accuracy: 0.6040 - val_loss: 0.5979 - val_accuracy: 0.6220 - 109ms/epoch - 7ms/step
Epoch 25/400
16/16 - 0s - loss: 0.6103 - accuracy: 0.6000 - val_loss: 0.5950 - val_accuracy: 0.6220 - 64ms/epoch - 4ms/step
Epoch 26/400
16/16 - 0s - loss: 0.6076 - accuracy: 0.5960 - val_loss: 0.5920 - val_accuracy: 0.6140 - 71ms/epoch - 4ms/step
Epoch 27/400
16/16 - 0s - loss: 0.6048 - accuracy: 0.6020 - val_loss: 0.5892 - val_accuracy: 0.6040 - 65ms/epoch - 4ms/step
Epoch 28/400
16/16 - 0s - loss: 0.6021 - accuracy: 0.6020 - val_loss: 0.5863 - val_accuracy: 0.6040 - 67ms/epoch - 4ms/step
Epoch 29/400
16/16 - 0s - loss: 0.5993 - accuracy: 0.5980 - val_loss: 0.5834 - val_accuracy: 0.6120 - 68ms/epoch - 4ms/step
Epoch 30/400
16/16 - 0s - loss: 0.5967 - accuracy: 0.6000 - val_loss: 0.5806 - val_accuracy: 0.6140 - 78ms/epoch - 5ms/step
Epoch 31/400
16/16 - 0s - loss: 0.5939 - accuracy: 0.6020 - val_loss: 0.5778 - val_accuracy: 0.6280 - 112ms/epoch - 7ms/step
Epoch 32/400
16/16 - 0s - loss: 0.5912 - accuracy: 0.6060 - val_loss: 0.5751 - val_accuracy: 0.6360 - 111ms/epoch - 7ms/step
Epoch 33/400
16/16 - 0s - loss: 0.5885 - accuracy: 0.6100 - val_loss: 0.5721 - val_accuracy: 0.6380 - 71ms/epoch - 4ms/step
Epoch 34/400
16/16 - 0s - loss: 0.5858 - accuracy: 0.6160 - val_loss: 0.5693 - val_accuracy: 0.6500 - 112ms/epoch - 7ms/step
Epoch 35/400
16/16 - 0s - loss: 0.5831 - accuracy: 0.6200 - val_loss: 0.5665 - val_accuracy: 0.6520 - 114ms/epoch - 7ms/step
Epoch 36/400
16/16 - 0s - loss: 0.5804 - accuracy: 0.6240 - val_loss: 0.5636 - val_accuracy: 0.6500 - 71ms/epoch - 4ms/step
Epoch 37/400
16/16 - 0s - loss: 0.5777 - accuracy: 0.6260 - val_loss: 0.5607 - val_accuracy: 0.6540 - 109ms/epoch - 7ms/step
Epoch 38/400
16/16 - 0s - loss: 0.5750 - accuracy: 0.6360 - val_loss: 0.5579 - val_accuracy: 0.6520 - 66ms/epoch - 4ms/step
Epoch 39/400
16/16 - 0s - loss: 0.5724 - accuracy: 0.6360 - val_loss: 0.5551 - val_accuracy: 0.6540 - 69ms/epoch - 4ms/step
Epoch 40/400
16/16 - 0s - loss: 0.5696 - accuracy: 0.6440 - val_loss: 0.5523 - val_accuracy: 0.6600 - 106ms/epoch - 7ms/step
Epoch 41/400
16/16 - 0s - loss: 0.5670 - accuracy: 0.6540 - val_loss: 0.5495 - val_accuracy: 0.6620 - 72ms/epoch - 4ms/step
Epoch 42/400
16/16 - 0s - loss: 0.5643 - accuracy: 0.6560 - val_loss: 0.5467 - val_accuracy: 0.6680 - 105ms/epoch - 7ms/step
Epoch 43/400
16/16 - 0s - loss: 0.5616 - accuracy: 0.6640 - val_loss: 0.5438 - val_accuracy: 0.6700 - 76ms/epoch - 5ms/step
Epoch 44/400
16/16 - 0s - loss: 0.5589 - accuracy: 0.6640 - val_loss: 0.5410 - val_accuracy: 0.6700 - 71ms/epoch - 4ms/step
Epoch 45/400
16/16 - 0s - loss: 0.5562 - accuracy: 0.6680 - val_loss: 0.5383 - val_accuracy: 0.6740 - 71ms/epoch - 4ms/step
Epoch 46/400
16/16 - 0s - loss: 0.5535 - accuracy: 0.6680 - val_loss: 0.5355 - val_accuracy: 0.6800 - 122ms/epoch - 8ms/step
Epoch 47/400
16/16 - 0s - loss: 0.5508 - accuracy: 0.6740 - val_loss: 0.5326 - val_accuracy: 0.6820 - 70ms/epoch - 4ms/step
Epoch 48/400
16/16 - 0s - loss: 0.5481 - accuracy: 0.6800 - val_loss: 0.5299 - val_accuracy: 0.6840 - 64ms/epoch - 4ms/step
Epoch 49/400
16/16 - 0s - loss: 0.5454 - accuracy: 0.6860 - val_loss: 0.5271 - val_accuracy: 0.6860 - 113ms/epoch - 7ms/step
Epoch 50/400
16/16 - 0s - loss: 0.5427 - accuracy: 0.6900 - val_loss: 0.5244 - val_accuracy: 0.6960 - 70ms/epoch - 4ms/step
Epoch 51/400
16/16 - 0s - loss: 0.5399 - accuracy: 0.6960 - val_loss: 0.5215 - val_accuracy: 0.6960 - 73ms/epoch - 5ms/step
Epoch 52/400
16/16 - 0s - loss: 0.5372 - accuracy: 0.6960 - val_loss: 0.5186 - val_accuracy: 0.7020 - 67ms/epoch - 4ms/step
Epoch 53/400
16/16 - 0s - loss: 0.5345 - accuracy: 0.6980 - val_loss: 0.5158 - val_accuracy: 0.7100 - 113ms/epoch - 7ms/step
Epoch 54/400
16/16 - 0s - loss: 0.5318 - accuracy: 0.7080 - val_loss: 0.5130 - val_accuracy: 0.7180 - 69ms/epoch - 4ms/step
Epoch 55/400
16/16 - 0s - loss: 0.5290 - accuracy: 0.7140 - val_loss: 0.5102 - val_accuracy: 0.7260 - 75ms/epoch - 5ms/step
Epoch 56/400
16/16 - 0s - loss: 0.5263 - accuracy: 0.7140 - val_loss: 0.5074 - val_accuracy: 0.7380 - 71ms/epoch - 4ms/step
Epoch 57/400
16/16 - 0s - loss: 0.5236 - accuracy: 0.7160 - val_loss: 0.5046 - val_accuracy: 0.7420 - 81ms/epoch - 5ms/step
Epoch 58/400
16/16 - 0s - loss: 0.5208 - accuracy: 0.7200 - val_loss: 0.5017 - val_accuracy: 0.7400 - 70ms/epoch - 4ms/step
Epoch 59/400
16/16 - 0s - loss: 0.5181 - accuracy: 0.7260 - val_loss: 0.4988 - val_accuracy: 0.7420 - 80ms/epoch - 5ms/step
Epoch 60/400
16/16 - 0s - loss: 0.5154 - accuracy: 0.7320 - val_loss: 0.4961 - val_accuracy: 0.7440 - 72ms/epoch - 4ms/step
Epoch 61/400
16/16 - 0s - loss: 0.5126 - accuracy: 0.7400 - val_loss: 0.4933 - val_accuracy: 0.7520 - 111ms/epoch - 7ms/step
Epoch 62/400
16/16 - 0s - loss: 0.5098 - accuracy: 0.7440 - val_loss: 0.4904 - val_accuracy: 0.7600 - 68ms/epoch - 4ms/step
Epoch 63/400
16/16 - 0s - loss: 0.5070 - accuracy: 0.7440 - val_loss: 0.4875 - val_accuracy: 0.7600 - 110ms/epoch - 7ms/step
Epoch 64/400
16/16 - 0s - loss: 0.5042 - accuracy: 0.7500 - val_loss: 0.4847 - val_accuracy: 0.7640 - 68ms/epoch - 4ms/step
Epoch 65/400
16/16 - 0s - loss: 0.5014 - accuracy: 0.7540 - val_loss: 0.4818 - val_accuracy: 0.7660 - 112ms/epoch - 7ms/step
Epoch 66/400
16/16 - 0s - loss: 0.4986 - accuracy: 0.7640 - val_loss: 0.4789 - val_accuracy: 0.7720 - 110ms/epoch - 7ms/step
Epoch 67/400
16/16 - 0s - loss: 0.4958 - accuracy: 0.7720 - val_loss: 0.4761 - val_accuracy: 0.7780 - 115ms/epoch - 7ms/step
Epoch 68/400
16/16 - 0s - loss: 0.4929 - accuracy: 0.7760 - val_loss: 0.4731 - val_accuracy: 0.7780 - 117ms/epoch - 7ms/step
Epoch 69/400
16/16 - 0s - loss: 0.4900 - accuracy: 0.7780 - val_loss: 0.4702 - val_accuracy: 0.7800 - 68ms/epoch - 4ms/step
Epoch 70/400
16/16 - 0s - loss: 0.4871 - accuracy: 0.7860 - val_loss: 0.4673 - val_accuracy: 0.7840 - 119ms/epoch - 7ms/step
Epoch 71/400
16/16 - 0s - loss: 0.4842 - accuracy: 0.7900 - val_loss: 0.4643 - val_accuracy: 0.7920 - 72ms/epoch - 4ms/step
Epoch 72/400
16/16 - 0s - loss: 0.4812 - accuracy: 0.8020 - val_loss: 0.4614 - val_accuracy: 0.7980 - 78ms/epoch - 5ms/step
Epoch 73/400
16/16 - 0s - loss: 0.4783 - accuracy: 0.8080 - val_loss: 0.4585 - val_accuracy: 0.8080 - 79ms/epoch - 5ms/step
Epoch 74/400
16/16 - 0s - loss: 0.4753 - accuracy: 0.8100 - val_loss: 0.4556 - val_accuracy: 0.8140 - 108ms/epoch - 7ms/step
Epoch 75/400
16/16 - 0s - loss: 0.4723 - accuracy: 0.8180 - val_loss: 0.4526 - val_accuracy: 0.8220 - 67ms/epoch - 4ms/step
Epoch 76/400
16/16 - 0s - loss: 0.4694 - accuracy: 0.8180 - val_loss: 0.4496 - val_accuracy: 0.8240 - 78ms/epoch - 5ms/step
Epoch 77/400
16/16 - 0s - loss: 0.4664 - accuracy: 0.8240 - val_loss: 0.4466 - val_accuracy: 0.8280 - 67ms/epoch - 4ms/step
Epoch 78/400
16/16 - 0s - loss: 0.4634 - accuracy: 0.8260 - val_loss: 0.4437 - val_accuracy: 0.8380 - 67ms/epoch - 4ms/step
Epoch 79/400
16/16 - 0s - loss: 0.4603 - accuracy: 0.8340 - val_loss: 0.4407 - val_accuracy: 0.8400 - 84ms/epoch - 5ms/step
Epoch 80/400
16/16 - 0s - loss: 0.4573 - accuracy: 0.8400 - val_loss: 0.4376 - val_accuracy: 0.8420 - 81ms/epoch - 5ms/step
Epoch 81/400
16/16 - 0s - loss: 0.4542 - accuracy: 0.8460 - val_loss: 0.4348 - val_accuracy: 0.8460 - 66ms/epoch - 4ms/step
Epoch 82/400
16/16 - 0s - loss: 0.4512 - accuracy: 0.8500 - val_loss: 0.4318 - val_accuracy: 0.8500 - 70ms/epoch - 4ms/step
Epoch 83/400
16/16 - 0s - loss: 0.4481 - accuracy: 0.8560 - val_loss: 0.4287 - val_accuracy: 0.8560 - 68ms/epoch - 4ms/step
Epoch 84/400
16/16 - 0s - loss: 0.4450 - accuracy: 0.8600 - val_loss: 0.4257 - val_accuracy: 0.8600 - 71ms/epoch - 4ms/step
Epoch 85/400
16/16 - 0s - loss: 0.4419 - accuracy: 0.8640 - val_loss: 0.4226 - val_accuracy: 0.8620 - 70ms/epoch - 4ms/step
Epoch 86/400
16/16 - 0s - loss: 0.4388 - accuracy: 0.8700 - val_loss: 0.4196 - val_accuracy: 0.8660 - 73ms/epoch - 5ms/step
Epoch 87/400
16/16 - 0s - loss: 0.4356 - accuracy: 0.8800 - val_loss: 0.4165 - val_accuracy: 0.8740 - 72ms/epoch - 4ms/step
Epoch 88/400
16/16 - 0s - loss: 0.4324 - accuracy: 0.8820 - val_loss: 0.4134 - val_accuracy: 0.8820 - 108ms/epoch - 7ms/step
Epoch 89/400
16/16 - 0s - loss: 0.4292 - accuracy: 0.8820 - val_loss: 0.4103 - val_accuracy: 0.8900 - 71ms/epoch - 4ms/step
Epoch 90/400
16/16 - 0s - loss: 0.4260 - accuracy: 0.8840 - val_loss: 0.4073 - val_accuracy: 0.8920 - 72ms/epoch - 5ms/step
Epoch 91/400
16/16 - 0s - loss: 0.4228 - accuracy: 0.8900 - val_loss: 0.4042 - val_accuracy: 0.8940 - 111ms/epoch - 7ms/step
Epoch 92/400
16/16 - 0s - loss: 0.4195 - accuracy: 0.8960 - val_loss: 0.4011 - val_accuracy: 0.9000 - 77ms/epoch - 5ms/step
Epoch 93/400
16/16 - 0s - loss: 0.4163 - accuracy: 0.8960 - val_loss: 0.3981 - val_accuracy: 0.9080 - 70ms/epoch - 4ms/step
Epoch 94/400
16/16 - 0s - loss: 0.4130 - accuracy: 0.9020 - val_loss: 0.3949 - val_accuracy: 0.9120 - 67ms/epoch - 4ms/step
Epoch 95/400
16/16 - 0s - loss: 0.4098 - accuracy: 0.9040 - val_loss: 0.3918 - val_accuracy: 0.9140 - 66ms/epoch - 4ms/step
Epoch 96/400
16/16 - 0s - loss: 0.4065 - accuracy: 0.9060 - val_loss: 0.3887 - val_accuracy: 0.9140 - 111ms/epoch - 7ms/step
Epoch 97/400
16/16 - 0s - loss: 0.4032 - accuracy: 0.9080 - val_loss: 0.3856 - val_accuracy: 0.9180 - 72ms/epoch - 4ms/step
Epoch 98/400
16/16 - 0s - loss: 0.4000 - accuracy: 0.9100 - val_loss: 0.3825 - val_accuracy: 0.9200 - 67ms/epoch - 4ms/step
Epoch 99/400
16/16 - 0s - loss: 0.3967 - accuracy: 0.9100 - val_loss: 0.3795 - val_accuracy: 0.9260 - 112ms/epoch - 7ms/step
Epoch 100/400
16/16 - 0s - loss: 0.3934 - accuracy: 0.9100 - val_loss: 0.3764 - val_accuracy: 0.9280 - 111ms/epoch - 7ms/step
Epoch 101/400
16/16 - 0s - loss: 0.3901 - accuracy: 0.9100 - val_loss: 0.3733 - val_accuracy: 0.9320 - 117ms/epoch - 7ms/step
Epoch 102/400
16/16 - 0s - loss: 0.3869 - accuracy: 0.9100 - val_loss: 0.3702 - val_accuracy: 0.9340 - 67ms/epoch - 4ms/step
Epoch 103/400
16/16 - 0s - loss: 0.3836 - accuracy: 0.9140 - val_loss: 0.3671 - val_accuracy: 0.9340 - 112ms/epoch - 7ms/step
Epoch 104/400
16/16 - 0s - loss: 0.3803 - accuracy: 0.9160 - val_loss: 0.3642 - val_accuracy: 0.9380 - 75ms/epoch - 5ms/step
Epoch 105/400
16/16 - 0s - loss: 0.3770 - accuracy: 0.9200 - val_loss: 0.3610 - val_accuracy: 0.9400 - 67ms/epoch - 4ms/step
Epoch 106/400
16/16 - 0s - loss: 0.3737 - accuracy: 0.9200 - val_loss: 0.3580 - val_accuracy: 0.9420 - 108ms/epoch - 7ms/step
Epoch 107/400
16/16 - 0s - loss: 0.3703 - accuracy: 0.9220 - val_loss: 0.3550 - val_accuracy: 0.9420 - 69ms/epoch - 4ms/step
Epoch 108/400
16/16 - 0s - loss: 0.3670 - accuracy: 0.9280 - val_loss: 0.3519 - val_accuracy: 0.9420 - 113ms/epoch - 7ms/step
Epoch 109/400
16/16 - 0s - loss: 0.3638 - accuracy: 0.9320 - val_loss: 0.3489 - val_accuracy: 0.9440 - 75ms/epoch - 5ms/step
Epoch 110/400
16/16 - 0s - loss: 0.3605 - accuracy: 0.9320 - val_loss: 0.3459 - val_accuracy: 0.9460 - 67ms/epoch - 4ms/step
Epoch 111/400
16/16 - 0s - loss: 0.3573 - accuracy: 0.9360 - val_loss: 0.3428 - val_accuracy: 0.9480 - 75ms/epoch - 5ms/step
Epoch 112/400
16/16 - 0s - loss: 0.3540 - accuracy: 0.9360 - val_loss: 0.3399 - val_accuracy: 0.9500 - 67ms/epoch - 4ms/step
Epoch 113/400
16/16 - 0s - loss: 0.3508 - accuracy: 0.9360 - val_loss: 0.3370 - val_accuracy: 0.9520 - 64ms/epoch - 4ms/step
Epoch 114/400
16/16 - 0s - loss: 0.3476 - accuracy: 0.9380 - val_loss: 0.3340 - val_accuracy: 0.9520 - 69ms/epoch - 4ms/step
Epoch 115/400
16/16 - 0s - loss: 0.3444 - accuracy: 0.9420 - val_loss: 0.3311 - val_accuracy: 0.9560 - 112ms/epoch - 7ms/step
Epoch 116/400
16/16 - 0s - loss: 0.3412 - accuracy: 0.9400 - val_loss: 0.3281 - val_accuracy: 0.9560 - 72ms/epoch - 5ms/step
Epoch 117/400
16/16 - 0s - loss: 0.3380 - accuracy: 0.9460 - val_loss: 0.3252 - val_accuracy: 0.9580 - 81ms/epoch - 5ms/step
Epoch 118/400
16/16 - 0s - loss: 0.3350 - accuracy: 0.9460 - val_loss: 0.3222 - val_accuracy: 0.9600 - 67ms/epoch - 4ms/step
Epoch 119/400
16/16 - 0s - loss: 0.3318 - accuracy: 0.9500 - val_loss: 0.3193 - val_accuracy: 0.9620 - 68ms/epoch - 4ms/step
Epoch 120/400
16/16 - 0s - loss: 0.3286 - accuracy: 0.9520 - val_loss: 0.3164 - val_accuracy: 0.9620 - 68ms/epoch - 4ms/step
Epoch 121/400
16/16 - 0s - loss: 0.3255 - accuracy: 0.9520 - val_loss: 0.3135 - val_accuracy: 0.9660 - 71ms/epoch - 4ms/step
Epoch 122/400
16/16 - 0s - loss: 0.3225 - accuracy: 0.9520 - val_loss: 0.3106 - val_accuracy: 0.9700 - 68ms/epoch - 4ms/step
Epoch 123/400
16/16 - 0s - loss: 0.3194 - accuracy: 0.9560 - val_loss: 0.3078 - val_accuracy: 0.9740 - 70ms/epoch - 4ms/step
Epoch 124/400
16/16 - 0s - loss: 0.3164 - accuracy: 0.9600 - val_loss: 0.3050 - val_accuracy: 0.9740 - 67ms/epoch - 4ms/step
Epoch 125/400
16/16 - 0s - loss: 0.3133 - accuracy: 0.9620 - val_loss: 0.3022 - val_accuracy: 0.9740 - 66ms/epoch - 4ms/step
Epoch 126/400
16/16 - 0s - loss: 0.3103 - accuracy: 0.9660 - val_loss: 0.2993 - val_accuracy: 0.9760 - 67ms/epoch - 4ms/step
Epoch 127/400
16/16 - 0s - loss: 0.3072 - accuracy: 0.9660 - val_loss: 0.2966 - val_accuracy: 0.9760 - 111ms/epoch - 7ms/step
Epoch 128/400
16/16 - 0s - loss: 0.3043 - accuracy: 0.9680 - val_loss: 0.2938 - val_accuracy: 0.9760 - 79ms/epoch - 5ms/step
Epoch 129/400
16/16 - 0s - loss: 0.3013 - accuracy: 0.9700 - val_loss: 0.2910 - val_accuracy: 0.9760 - 73ms/epoch - 5ms/step
Epoch 130/400
16/16 - 0s - loss: 0.2983 - accuracy: 0.9720 - val_loss: 0.2883 - val_accuracy: 0.9780 - 75ms/epoch - 5ms/step
Epoch 131/400
16/16 - 0s - loss: 0.2954 - accuracy: 0.9720 - val_loss: 0.2856 - val_accuracy: 0.9780 - 69ms/epoch - 4ms/step
Epoch 132/400
16/16 - 0s - loss: 0.2925 - accuracy: 0.9700 - val_loss: 0.2829 - val_accuracy: 0.9800 - 112ms/epoch - 7ms/step
Epoch 133/400
16/16 - 0s - loss: 0.2896 - accuracy: 0.9700 - val_loss: 0.2802 - val_accuracy: 0.9800 - 110ms/epoch - 7ms/step
Epoch 134/400
16/16 - 0s - loss: 0.2867 - accuracy: 0.9700 - val_loss: 0.2775 - val_accuracy: 0.9820 - 68ms/epoch - 4ms/step
Epoch 135/400
16/16 - 0s - loss: 0.2838 - accuracy: 0.9700 - val_loss: 0.2749 - val_accuracy: 0.9840 - 108ms/epoch - 7ms/step
Epoch 136/400
16/16 - 0s - loss: 0.2809 - accuracy: 0.9700 - val_loss: 0.2723 - val_accuracy: 0.9860 - 68ms/epoch - 4ms/step
Epoch 137/400
16/16 - 0s - loss: 0.2781 - accuracy: 0.9740 - val_loss: 0.2696 - val_accuracy: 0.9880 - 72ms/epoch - 5ms/step
Epoch 138/400
16/16 - 0s - loss: 0.2753 - accuracy: 0.9760 - val_loss: 0.2670 - val_accuracy: 0.9880 - 71ms/epoch - 4ms/step
Epoch 139/400
16/16 - 0s - loss: 0.2724 - accuracy: 0.9780 - val_loss: 0.2643 - val_accuracy: 0.9880 - 106ms/epoch - 7ms/step
Epoch 140/400
16/16 - 0s - loss: 0.2696 - accuracy: 0.9780 - val_loss: 0.2617 - val_accuracy: 0.9880 - 72ms/epoch - 5ms/step
Epoch 141/400
16/16 - 0s - loss: 0.2668 - accuracy: 0.9780 - val_loss: 0.2591 - val_accuracy: 0.9900 - 113ms/epoch - 7ms/step
Epoch 142/400
16/16 - 0s - loss: 0.2640 - accuracy: 0.9780 - val_loss: 0.2565 - val_accuracy: 0.9900 - 112ms/epoch - 7ms/step
Epoch 143/400
16/16 - 0s - loss: 0.2611 - accuracy: 0.9800 - val_loss: 0.2540 - val_accuracy: 0.9900 - 72ms/epoch - 4ms/step
Epoch 144/400
16/16 - 0s - loss: 0.2584 - accuracy: 0.9800 - val_loss: 0.2515 - val_accuracy: 0.9920 - 71ms/epoch - 4ms/step
Epoch 145/400
16/16 - 0s - loss: 0.2557 - accuracy: 0.9820 - val_loss: 0.2489 - val_accuracy: 0.9920 - 75ms/epoch - 5ms/step
Epoch 146/400
16/16 - 0s - loss: 0.2530 - accuracy: 0.9820 - val_loss: 0.2465 - val_accuracy: 0.9920 - 76ms/epoch - 5ms/step
Epoch 147/400
16/16 - 0s - loss: 0.2503 - accuracy: 0.9840 - val_loss: 0.2440 - val_accuracy: 0.9940 - 110ms/epoch - 7ms/step
Epoch 148/400
16/16 - 0s - loss: 0.2476 - accuracy: 0.9860 - val_loss: 0.2415 - val_accuracy: 0.9940 - 67ms/epoch - 4ms/step
Epoch 149/400
16/16 - 0s - loss: 0.2449 - accuracy: 0.9860 - val_loss: 0.2390 - val_accuracy: 0.9940 - 110ms/epoch - 7ms/step
Epoch 150/400
16/16 - 0s - loss: 0.2423 - accuracy: 0.9880 - val_loss: 0.2365 - val_accuracy: 0.9940 - 77ms/epoch - 5ms/step
Epoch 151/400
16/16 - 0s - loss: 0.2396 - accuracy: 0.9880 - val_loss: 0.2341 - val_accuracy: 0.9960 - 67ms/epoch - 4ms/step
Epoch 152/400
16/16 - 0s - loss: 0.2370 - accuracy: 0.9880 - val_loss: 0.2317 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 153/400
16/16 - 0s - loss: 0.2343 - accuracy: 0.9880 - val_loss: 0.2293 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 154/400
16/16 - 0s - loss: 0.2318 - accuracy: 0.9880 - val_loss: 0.2269 - val_accuracy: 0.9980 - 110ms/epoch - 7ms/step
Epoch 155/400
16/16 - 0s - loss: 0.2292 - accuracy: 0.9900 - val_loss: 0.2246 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 156/400
16/16 - 0s - loss: 0.2267 - accuracy: 0.9920 - val_loss: 0.2222 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 157/400
16/16 - 0s - loss: 0.2242 - accuracy: 0.9920 - val_loss: 0.2199 - val_accuracy: 1.0000 - 108ms/epoch - 7ms/step
Epoch 158/400
16/16 - 0s - loss: 0.2217 - accuracy: 0.9920 - val_loss: 0.2177 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 159/400
16/16 - 0s - loss: 0.2192 - accuracy: 0.9920 - val_loss: 0.2154 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 160/400
16/16 - 0s - loss: 0.2168 - accuracy: 0.9920 - val_loss: 0.2132 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 161/400
16/16 - 0s - loss: 0.2145 - accuracy: 0.9920 - val_loss: 0.2110 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 162/400
16/16 - 0s - loss: 0.2121 - accuracy: 0.9940 - val_loss: 0.2088 - val_accuracy: 1.0000 - 123ms/epoch - 8ms/step
Epoch 163/400
16/16 - 0s - loss: 0.2097 - accuracy: 0.9940 - val_loss: 0.2066 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 164/400
16/16 - 0s - loss: 0.2074 - accuracy: 0.9940 - val_loss: 0.2045 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 165/400
16/16 - 0s - loss: 0.2052 - accuracy: 0.9940 - val_loss: 0.2024 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 166/400
16/16 - 0s - loss: 0.2029 - accuracy: 0.9940 - val_loss: 0.2004 - val_accuracy: 0.9980 - 66ms/epoch - 4ms/step
Epoch 167/400
16/16 - 0s - loss: 0.2007 - accuracy: 0.9960 - val_loss: 0.1983 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 168/400
16/16 - 0s - loss: 0.1986 - accuracy: 0.9960 - val_loss: 0.1963 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 169/400
16/16 - 0s - loss: 0.1963 - accuracy: 0.9960 - val_loss: 0.1943 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 170/400
16/16 - 0s - loss: 0.1942 - accuracy: 0.9960 - val_loss: 0.1923 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 171/400
16/16 - 0s - loss: 0.1921 - accuracy: 0.9960 - val_loss: 0.1903 - val_accuracy: 0.9980 - 108ms/epoch - 7ms/step
Epoch 172/400
16/16 - 0s - loss: 0.1900 - accuracy: 0.9960 - val_loss: 0.1883 - val_accuracy: 0.9980 - 66ms/epoch - 4ms/step
Epoch 173/400
16/16 - 0s - loss: 0.1879 - accuracy: 0.9960 - val_loss: 0.1864 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 174/400
16/16 - 0s - loss: 0.1859 - accuracy: 0.9960 - val_loss: 0.1845 - val_accuracy: 0.9980 - 85ms/epoch - 5ms/step
Epoch 175/400
16/16 - 0s - loss: 0.1838 - accuracy: 0.9960 - val_loss: 0.1826 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 176/400
16/16 - 0s - loss: 0.1819 - accuracy: 0.9960 - val_loss: 0.1808 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 177/400
16/16 - 0s - loss: 0.1799 - accuracy: 0.9960 - val_loss: 0.1790 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 178/400
16/16 - 0s - loss: 0.1780 - accuracy: 0.9980 - val_loss: 0.1773 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 179/400
16/16 - 0s - loss: 0.1761 - accuracy: 0.9980 - val_loss: 0.1755 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 180/400
16/16 - 0s - loss: 0.1743 - accuracy: 0.9980 - val_loss: 0.1738 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 181/400
16/16 - 0s - loss: 0.1724 - accuracy: 0.9980 - val_loss: 0.1721 - val_accuracy: 0.9980 - 66ms/epoch - 4ms/step
Epoch 182/400
16/16 - 0s - loss: 0.1706 - accuracy: 0.9980 - val_loss: 0.1705 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 183/400
16/16 - 0s - loss: 0.1689 - accuracy: 0.9980 - val_loss: 0.1688 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 184/400
16/16 - 0s - loss: 0.1671 - accuracy: 0.9980 - val_loss: 0.1672 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 185/400
16/16 - 0s - loss: 0.1653 - accuracy: 0.9980 - val_loss: 0.1656 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 186/400
16/16 - 0s - loss: 0.1636 - accuracy: 0.9980 - val_loss: 0.1640 - val_accuracy: 0.9980 - 77ms/epoch - 5ms/step
Epoch 187/400
16/16 - 0s - loss: 0.1619 - accuracy: 0.9980 - val_loss: 0.1624 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 188/400
16/16 - 0s - loss: 0.1601 - accuracy: 0.9980 - val_loss: 0.1608 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 189/400
16/16 - 0s - loss: 0.1584 - accuracy: 0.9980 - val_loss: 0.1593 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 190/400
16/16 - 0s - loss: 0.1568 - accuracy: 0.9980 - val_loss: 0.1578 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 191/400
16/16 - 0s - loss: 0.1552 - accuracy: 0.9980 - val_loss: 0.1563 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 192/400
16/16 - 0s - loss: 0.1535 - accuracy: 0.9980 - val_loss: 0.1548 - val_accuracy: 0.9980 - 110ms/epoch - 7ms/step
Epoch 193/400
16/16 - 0s - loss: 0.1519 - accuracy: 0.9980 - val_loss: 0.1533 - val_accuracy: 0.9980 - 110ms/epoch - 7ms/step
Epoch 194/400
16/16 - 0s - loss: 0.1503 - accuracy: 0.9980 - val_loss: 0.1518 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 195/400
16/16 - 0s - loss: 0.1487 - accuracy: 0.9980 - val_loss: 0.1504 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 196/400
16/16 - 0s - loss: 0.1471 - accuracy: 0.9980 - val_loss: 0.1489 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 197/400
16/16 - 0s - loss: 0.1456 - accuracy: 0.9980 - val_loss: 0.1475 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 198/400
16/16 - 0s - loss: 0.1441 - accuracy: 0.9980 - val_loss: 0.1461 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 199/400
16/16 - 0s - loss: 0.1426 - accuracy: 0.9980 - val_loss: 0.1448 - val_accuracy: 0.9980 - 122ms/epoch - 8ms/step
Epoch 200/400
16/16 - 0s - loss: 0.1411 - accuracy: 0.9980 - val_loss: 0.1434 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 201/400
16/16 - 0s - loss: 0.1396 - accuracy: 0.9980 - val_loss: 0.1421 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 202/400
16/16 - 0s - loss: 0.1382 - accuracy: 0.9980 - val_loss: 0.1408 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 203/400
16/16 - 0s - loss: 0.1368 - accuracy: 0.9980 - val_loss: 0.1395 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 204/400
16/16 - 0s - loss: 0.1353 - accuracy: 0.9980 - val_loss: 0.1382 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 205/400
16/16 - 0s - loss: 0.1339 - accuracy: 0.9980 - val_loss: 0.1369 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 206/400
16/16 - 0s - loss: 0.1326 - accuracy: 0.9980 - val_loss: 0.1357 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 207/400
16/16 - 0s - loss: 0.1312 - accuracy: 0.9980 - val_loss: 0.1345 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 208/400
16/16 - 0s - loss: 0.1299 - accuracy: 0.9980 - val_loss: 0.1333 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 209/400
16/16 - 0s - loss: 0.1286 - accuracy: 0.9980 - val_loss: 0.1321 - val_accuracy: 0.9980 - 83ms/epoch - 5ms/step
Epoch 210/400
16/16 - 0s - loss: 0.1272 - accuracy: 0.9980 - val_loss: 0.1309 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 211/400
16/16 - 0s - loss: 0.1259 - accuracy: 0.9980 - val_loss: 0.1298 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 212/400
16/16 - 0s - loss: 0.1246 - accuracy: 0.9980 - val_loss: 0.1287 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 213/400
16/16 - 0s - loss: 0.1233 - accuracy: 0.9980 - val_loss: 0.1276 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 214/400
16/16 - 0s - loss: 0.1221 - accuracy: 0.9980 - val_loss: 0.1265 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 215/400
16/16 - 0s - loss: 0.1209 - accuracy: 0.9980 - val_loss: 0.1255 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 216/400
16/16 - 0s - loss: 0.1197 - accuracy: 0.9980 - val_loss: 0.1245 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 217/400
16/16 - 0s - loss: 0.1185 - accuracy: 0.9980 - val_loss: 0.1234 - val_accuracy: 1.0000 - 64ms/epoch - 4ms/step
Epoch 218/400
16/16 - 0s - loss: 0.1174 - accuracy: 0.9980 - val_loss: 0.1224 - val_accuracy: 1.0000 - 66ms/epoch - 4ms/step
Epoch 219/400
16/16 - 0s - loss: 0.1162 - accuracy: 0.9980 - val_loss: 0.1214 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 220/400
16/16 - 0s - loss: 0.1151 - accuracy: 0.9980 - val_loss: 0.1204 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 221/400
16/16 - 0s - loss: 0.1140 - accuracy: 0.9980 - val_loss: 0.1194 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 222/400
16/16 - 0s - loss: 0.1129 - accuracy: 0.9980 - val_loss: 0.1185 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 223/400
16/16 - 0s - loss: 0.1118 - accuracy: 0.9980 - val_loss: 0.1175 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 224/400
16/16 - 0s - loss: 0.1107 - accuracy: 0.9980 - val_loss: 0.1165 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 225/400
16/16 - 0s - loss: 0.1097 - accuracy: 0.9980 - val_loss: 0.1156 - val_accuracy: 1.0000 - 65ms/epoch - 4ms/step
Epoch 226/400
16/16 - 0s - loss: 0.1086 - accuracy: 0.9980 - val_loss: 0.1147 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 227/400
16/16 - 0s - loss: 0.1077 - accuracy: 0.9980 - val_loss: 0.1137 - val_accuracy: 1.0000 - 66ms/epoch - 4ms/step
Epoch 228/400
16/16 - 0s - loss: 0.1067 - accuracy: 0.9980 - val_loss: 0.1128 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 229/400
16/16 - 0s - loss: 0.1057 - accuracy: 0.9980 - val_loss: 0.1120 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 230/400
16/16 - 0s - loss: 0.1047 - accuracy: 0.9980 - val_loss: 0.1111 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 231/400
16/16 - 0s - loss: 0.1038 - accuracy: 0.9980 - val_loss: 0.1102 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 232/400
16/16 - 0s - loss: 0.1028 - accuracy: 1.0000 - val_loss: 0.1093 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 233/400
16/16 - 0s - loss: 0.1019 - accuracy: 1.0000 - val_loss: 0.1085 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 234/400
16/16 - 0s - loss: 0.1010 - accuracy: 1.0000 - val_loss: 0.1076 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 235/400
16/16 - 0s - loss: 0.1001 - accuracy: 1.0000 - val_loss: 0.1068 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 236/400
16/16 - 0s - loss: 0.0991 - accuracy: 1.0000 - val_loss: 0.1060 - val_accuracy: 1.0000 - 107ms/epoch - 7ms/step
Epoch 237/400
16/16 - 0s - loss: 0.0982 - accuracy: 1.0000 - val_loss: 0.1052 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 238/400
16/16 - 0s - loss: 0.0973 - accuracy: 1.0000 - val_loss: 0.1044 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 239/400
16/16 - 0s - loss: 0.0965 - accuracy: 1.0000 - val_loss: 0.1036 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 240/400
16/16 - 0s - loss: 0.0956 - accuracy: 1.0000 - val_loss: 0.1029 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 241/400
16/16 - 0s - loss: 0.0948 - accuracy: 1.0000 - val_loss: 0.1021 - val_accuracy: 1.0000 - 65ms/epoch - 4ms/step
Epoch 242/400
16/16 - 0s - loss: 0.0939 - accuracy: 1.0000 - val_loss: 0.1014 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 243/400
16/16 - 0s - loss: 0.0931 - accuracy: 1.0000 - val_loss: 0.1006 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 244/400
16/16 - 0s - loss: 0.0923 - accuracy: 1.0000 - val_loss: 0.0998 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 245/400
16/16 - 0s - loss: 0.0915 - accuracy: 1.0000 - val_loss: 0.0991 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 246/400
16/16 - 0s - loss: 0.0907 - accuracy: 1.0000 - val_loss: 0.0984 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 247/400
16/16 - 0s - loss: 0.0899 - accuracy: 1.0000 - val_loss: 0.0976 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 248/400
16/16 - 0s - loss: 0.0892 - accuracy: 1.0000 - val_loss: 0.0969 - val_accuracy: 1.0000 - 108ms/epoch - 7ms/step
Epoch 249/400
16/16 - 0s - loss: 0.0884 - accuracy: 1.0000 - val_loss: 0.0962 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 250/400
16/16 - 0s - loss: 0.0877 - accuracy: 1.0000 - val_loss: 0.0955 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 251/400
16/16 - 0s - loss: 0.0869 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 252/400
16/16 - 0s - loss: 0.0862 - accuracy: 1.0000 - val_loss: 0.0941 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 253/400
16/16 - 0s - loss: 0.0854 - accuracy: 1.0000 - val_loss: 0.0934 - val_accuracy: 1.0000 - 106ms/epoch - 7ms/step
Epoch 254/400
16/16 - 0s - loss: 0.0847 - accuracy: 1.0000 - val_loss: 0.0927 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 255/400
16/16 - 0s - loss: 0.0840 - accuracy: 1.0000 - val_loss: 0.0921 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 256/400
16/16 - 0s - loss: 0.0833 - accuracy: 1.0000 - val_loss: 0.0914 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 257/400
16/16 - 0s - loss: 0.0826 - accuracy: 1.0000 - val_loss: 0.0908 - val_accuracy: 1.0000 - 84ms/epoch - 5ms/step
Epoch 258/400
16/16 - 0s - loss: 0.0819 - accuracy: 1.0000 - val_loss: 0.0902 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 259/400
16/16 - 0s - loss: 0.0812 - accuracy: 1.0000 - val_loss: 0.0895 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 260/400
16/16 - 0s - loss: 0.0806 - accuracy: 1.0000 - val_loss: 0.0889 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 261/400
16/16 - 0s - loss: 0.0799 - accuracy: 1.0000 - val_loss: 0.0883 - val_accuracy: 1.0000 - 65ms/epoch - 4ms/step
Epoch 262/400
16/16 - 0s - loss: 0.0793 - accuracy: 1.0000 - val_loss: 0.0877 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 263/400
16/16 - 0s - loss: 0.0786 - accuracy: 1.0000 - val_loss: 0.0871 - val_accuracy: 1.0000 - 66ms/epoch - 4ms/step
Epoch 264/400
16/16 - 0s - loss: 0.0780 - accuracy: 1.0000 - val_loss: 0.0865 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 265/400
16/16 - 0s - loss: 0.0774 - accuracy: 1.0000 - val_loss: 0.0859 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 266/400
16/16 - 0s - loss: 0.0768 - accuracy: 1.0000 - val_loss: 0.0853 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 267/400
16/16 - 0s - loss: 0.0762 - accuracy: 1.0000 - val_loss: 0.0848 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 268/400
16/16 - 0s - loss: 0.0756 - accuracy: 1.0000 - val_loss: 0.0842 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 269/400
16/16 - 0s - loss: 0.0750 - accuracy: 1.0000 - val_loss: 0.0836 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 270/400
16/16 - 0s - loss: 0.0744 - accuracy: 1.0000 - val_loss: 0.0831 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 271/400
16/16 - 0s - loss: 0.0738 - accuracy: 1.0000 - val_loss: 0.0825 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 272/400
16/16 - 0s - loss: 0.0732 - accuracy: 1.0000 - val_loss: 0.0820 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 273/400
16/16 - 0s - loss: 0.0726 - accuracy: 1.0000 - val_loss: 0.0814 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 274/400
16/16 - 0s - loss: 0.0721 - accuracy: 1.0000 - val_loss: 0.0809 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 275/400
16/16 - 0s - loss: 0.0715 - accuracy: 1.0000 - val_loss: 0.0804 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 276/400
16/16 - 0s - loss: 0.0709 - accuracy: 1.0000 - val_loss: 0.0798 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 277/400
16/16 - 0s - loss: 0.0703 - accuracy: 1.0000 - val_loss: 0.0793 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 278/400
16/16 - 0s - loss: 0.0698 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 279/400
16/16 - 0s - loss: 0.0692 - accuracy: 1.0000 - val_loss: 0.0782 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 280/400
16/16 - 0s - loss: 0.0687 - accuracy: 1.0000 - val_loss: 0.0777 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 281/400
16/16 - 0s - loss: 0.0681 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 282/400
16/16 - 0s - loss: 0.0676 - accuracy: 1.0000 - val_loss: 0.0766 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 283/400
16/16 - 0s - loss: 0.0671 - accuracy: 1.0000 - val_loss: 0.0761 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 284/400
16/16 - 0s - loss: 0.0665 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 285/400
16/16 - 0s - loss: 0.0660 - accuracy: 1.0000 - val_loss: 0.0752 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 286/400
16/16 - 0s - loss: 0.0655 - accuracy: 1.0000 - val_loss: 0.0746 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 287/400
16/16 - 0s - loss: 0.0650 - accuracy: 1.0000 - val_loss: 0.0741 - val_accuracy: 1.0000 - 66ms/epoch - 4ms/step
Epoch 288/400
16/16 - 0s - loss: 0.0645 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 1.0000 - 121ms/epoch - 8ms/step
Epoch 289/400
16/16 - 0s - loss: 0.0640 - accuracy: 1.0000 - val_loss: 0.0732 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 290/400
16/16 - 0s - loss: 0.0635 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 291/400
16/16 - 0s - loss: 0.0630 - accuracy: 1.0000 - val_loss: 0.0722 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 292/400
16/16 - 0s - loss: 0.0625 - accuracy: 1.0000 - val_loss: 0.0717 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 293/400
16/16 - 0s - loss: 0.0620 - accuracy: 1.0000 - val_loss: 0.0712 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 294/400
16/16 - 0s - loss: 0.0616 - accuracy: 1.0000 - val_loss: 0.0708 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
Epoch 295/400
16/16 - 0s - loss: 0.0611 - accuracy: 1.0000 - val_loss: 0.0703 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 296/400
16/16 - 0s - loss: 0.0606 - accuracy: 1.0000 - val_loss: 0.0699 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 297/400
16/16 - 0s - loss: 0.0601 - accuracy: 1.0000 - val_loss: 0.0694 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 298/400
16/16 - 0s - loss: 0.0597 - accuracy: 1.0000 - val_loss: 0.0690 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 299/400
16/16 - 0s - loss: 0.0592 - accuracy: 1.0000 - val_loss: 0.0685 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 300/400
16/16 - 0s - loss: 0.0588 - accuracy: 1.0000 - val_loss: 0.0680 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 301/400
16/16 - 0s - loss: 0.0583 - accuracy: 1.0000 - val_loss: 0.0676 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 302/400
16/16 - 0s - loss: 0.0579 - accuracy: 1.0000 - val_loss: 0.0671 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 303/400
16/16 - 0s - loss: 0.0574 - accuracy: 1.0000 - val_loss: 0.0667 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 304/400
16/16 - 0s - loss: 0.0570 - accuracy: 1.0000 - val_loss: 0.0663 - val_accuracy: 1.0000 - 108ms/epoch - 7ms/step
Epoch 305/400
16/16 - 0s - loss: 0.0566 - accuracy: 1.0000 - val_loss: 0.0659 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 306/400
16/16 - 0s - loss: 0.0562 - accuracy: 1.0000 - val_loss: 0.0655 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 307/400
16/16 - 0s - loss: 0.0558 - accuracy: 1.0000 - val_loss: 0.0651 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 308/400
16/16 - 0s - loss: 0.0553 - accuracy: 1.0000 - val_loss: 0.0647 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 309/400
16/16 - 0s - loss: 0.0549 - accuracy: 1.0000 - val_loss: 0.0642 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 310/400
16/16 - 0s - loss: 0.0545 - accuracy: 1.0000 - val_loss: 0.0638 - val_accuracy: 1.0000 - 121ms/epoch - 8ms/step
Epoch 311/400
16/16 - 0s - loss: 0.0541 - accuracy: 1.0000 - val_loss: 0.0634 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 312/400
16/16 - 0s - loss: 0.0537 - accuracy: 1.0000 - val_loss: 0.0630 - val_accuracy: 1.0000 - 66ms/epoch - 4ms/step
Epoch 313/400
16/16 - 0s - loss: 0.0534 - accuracy: 1.0000 - val_loss: 0.0626 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 314/400
16/16 - 0s - loss: 0.0530 - accuracy: 1.0000 - val_loss: 0.0623 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 315/400
16/16 - 0s - loss: 0.0526 - accuracy: 1.0000 - val_loss: 0.0619 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 316/400
16/16 - 0s - loss: 0.0522 - accuracy: 1.0000 - val_loss: 0.0615 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 317/400
16/16 - 0s - loss: 0.0518 - accuracy: 1.0000 - val_loss: 0.0612 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 318/400
16/16 - 0s - loss: 0.0515 - accuracy: 1.0000 - val_loss: 0.0608 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 319/400
16/16 - 0s - loss: 0.0511 - accuracy: 1.0000 - val_loss: 0.0605 - val_accuracy: 1.0000 - 66ms/epoch - 4ms/step
Epoch 320/400
16/16 - 0s - loss: 0.0508 - accuracy: 1.0000 - val_loss: 0.0601 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 321/400
16/16 - 0s - loss: 0.0504 - accuracy: 1.0000 - val_loss: 0.0597 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 322/400
16/16 - 0s - loss: 0.0500 - accuracy: 1.0000 - val_loss: 0.0594 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 323/400
16/16 - 0s - loss: 0.0497 - accuracy: 1.0000 - val_loss: 0.0591 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 324/400
16/16 - 0s - loss: 0.0494 - accuracy: 1.0000 - val_loss: 0.0587 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 325/400
16/16 - 0s - loss: 0.0490 - accuracy: 1.0000 - val_loss: 0.0583 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 326/400
16/16 - 0s - loss: 0.0487 - accuracy: 1.0000 - val_loss: 0.0580 - val_accuracy: 0.9980 - 83ms/epoch - 5ms/step
Epoch 327/400
16/16 - 0s - loss: 0.0483 - accuracy: 1.0000 - val_loss: 0.0577 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 328/400
16/16 - 0s - loss: 0.0480 - accuracy: 1.0000 - val_loss: 0.0573 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 329/400
16/16 - 0s - loss: 0.0477 - accuracy: 1.0000 - val_loss: 0.0570 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 330/400
16/16 - 0s - loss: 0.0474 - accuracy: 1.0000 - val_loss: 0.0567 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 331/400
16/16 - 0s - loss: 0.0470 - accuracy: 1.0000 - val_loss: 0.0563 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 332/400
16/16 - 0s - loss: 0.0467 - accuracy: 1.0000 - val_loss: 0.0561 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 333/400
16/16 - 0s - loss: 0.0464 - accuracy: 1.0000 - val_loss: 0.0557 - val_accuracy: 0.9980 - 67ms/epoch - 4ms/step
Epoch 334/400
16/16 - 0s - loss: 0.0461 - accuracy: 1.0000 - val_loss: 0.0554 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 335/400
16/16 - 0s - loss: 0.0458 - accuracy: 1.0000 - val_loss: 0.0551 - val_accuracy: 0.9980 - 94ms/epoch - 6ms/step
Epoch 336/400
16/16 - 0s - loss: 0.0455 - accuracy: 1.0000 - val_loss: 0.0547 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 337/400
16/16 - 0s - loss: 0.0452 - accuracy: 1.0000 - val_loss: 0.0544 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 338/400
16/16 - 0s - loss: 0.0449 - accuracy: 1.0000 - val_loss: 0.0541 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 339/400
16/16 - 0s - loss: 0.0446 - accuracy: 1.0000 - val_loss: 0.0538 - val_accuracy: 0.9980 - 66ms/epoch - 4ms/step
Epoch 340/400
16/16 - 0s - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.0535 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 341/400
16/16 - 0s - loss: 0.0440 - accuracy: 1.0000 - val_loss: 0.0532 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 342/400
16/16 - 0s - loss: 0.0437 - accuracy: 1.0000 - val_loss: 0.0529 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 343/400
16/16 - 0s - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.0527 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 344/400
16/16 - 0s - loss: 0.0431 - accuracy: 1.0000 - val_loss: 0.0524 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 345/400
16/16 - 0s - loss: 0.0428 - accuracy: 1.0000 - val_loss: 0.0521 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 346/400
16/16 - 0s - loss: 0.0426 - accuracy: 1.0000 - val_loss: 0.0518 - val_accuracy: 0.9980 - 66ms/epoch - 4ms/step
Epoch 347/400
16/16 - 0s - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.0515 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 348/400
16/16 - 0s - loss: 0.0420 - accuracy: 1.0000 - val_loss: 0.0512 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 349/400
16/16 - 0s - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.0509 - val_accuracy: 0.9980 - 77ms/epoch - 5ms/step
Epoch 350/400
16/16 - 0s - loss: 0.0415 - accuracy: 1.0000 - val_loss: 0.0506 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 351/400
16/16 - 0s - loss: 0.0412 - accuracy: 1.0000 - val_loss: 0.0503 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 352/400
16/16 - 0s - loss: 0.0409 - accuracy: 1.0000 - val_loss: 0.0500 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 353/400
16/16 - 0s - loss: 0.0407 - accuracy: 1.0000 - val_loss: 0.0498 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 354/400
16/16 - 0s - loss: 0.0404 - accuracy: 1.0000 - val_loss: 0.0495 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 355/400
16/16 - 0s - loss: 0.0401 - accuracy: 1.0000 - val_loss: 0.0493 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 356/400
16/16 - 0s - loss: 0.0399 - accuracy: 1.0000 - val_loss: 0.0490 - val_accuracy: 0.9980 - 110ms/epoch - 7ms/step
Epoch 357/400
16/16 - 0s - loss: 0.0396 - accuracy: 1.0000 - val_loss: 0.0488 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 358/400
16/16 - 0s - loss: 0.0394 - accuracy: 1.0000 - val_loss: 0.0485 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 359/400
16/16 - 0s - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.0482 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 360/400
16/16 - 0s - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.0479 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 361/400
16/16 - 0s - loss: 0.0386 - accuracy: 1.0000 - val_loss: 0.0477 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 362/400
16/16 - 0s - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.0475 - val_accuracy: 0.9980 - 126ms/epoch - 8ms/step
Epoch 363/400
16/16 - 0s - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.0472 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 364/400
16/16 - 0s - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.0469 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 365/400
16/16 - 0s - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.0467 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 366/400
16/16 - 0s - loss: 0.0374 - accuracy: 1.0000 - val_loss: 0.0465 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 367/400
16/16 - 0s - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.0462 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 368/400
16/16 - 0s - loss: 0.0370 - accuracy: 1.0000 - val_loss: 0.0460 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 369/400
16/16 - 0s - loss: 0.0367 - accuracy: 1.0000 - val_loss: 0.0457 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 370/400
16/16 - 0s - loss: 0.0365 - accuracy: 1.0000 - val_loss: 0.0455 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 371/400
16/16 - 0s - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.0453 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 372/400
16/16 - 0s - loss: 0.0360 - accuracy: 1.0000 - val_loss: 0.0450 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 373/400
16/16 - 0s - loss: 0.0358 - accuracy: 1.0000 - val_loss: 0.0448 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 374/400
16/16 - 0s - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.0445 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 375/400
16/16 - 0s - loss: 0.0354 - accuracy: 1.0000 - val_loss: 0.0442 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 376/400
16/16 - 0s - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.0440 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 377/400
16/16 - 0s - loss: 0.0349 - accuracy: 1.0000 - val_loss: 0.0437 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 378/400
16/16 - 0s - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.0434 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 379/400
16/16 - 0s - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.0431 - val_accuracy: 0.9980 - 116ms/epoch - 7ms/step
Epoch 380/400
16/16 - 0s - loss: 0.0342 - accuracy: 1.0000 - val_loss: 0.0428 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 381/400
16/16 - 0s - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.0425 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 382/400
16/16 - 0s - loss: 0.0337 - accuracy: 1.0000 - val_loss: 0.0422 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 383/400
16/16 - 0s - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.0420 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 384/400
16/16 - 0s - loss: 0.0333 - accuracy: 1.0000 - val_loss: 0.0417 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 385/400
16/16 - 0s - loss: 0.0330 - accuracy: 1.0000 - val_loss: 0.0414 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 386/400
16/16 - 0s - loss: 0.0328 - accuracy: 1.0000 - val_loss: 0.0412 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 387/400
16/16 - 0s - loss: 0.0326 - accuracy: 1.0000 - val_loss: 0.0409 - val_accuracy: 0.9980 - 110ms/epoch - 7ms/step
Epoch 388/400
16/16 - 0s - loss: 0.0324 - accuracy: 1.0000 - val_loss: 0.0407 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 389/400
16/16 - 0s - loss: 0.0322 - accuracy: 1.0000 - val_loss: 0.0404 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 390/400
16/16 - 0s - loss: 0.0320 - accuracy: 1.0000 - val_loss: 0.0402 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 391/400
16/16 - 0s - loss: 0.0318 - accuracy: 1.0000 - val_loss: 0.0400 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 392/400
16/16 - 0s - loss: 0.0316 - accuracy: 1.0000 - val_loss: 0.0397 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 393/400
16/16 - 0s - loss: 0.0314 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 394/400
16/16 - 0s - loss: 0.0312 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 0.9980 - 89ms/epoch - 6ms/step
Epoch 395/400
16/16 - 0s - loss: 0.0310 - accuracy: 1.0000 - val_loss: 0.0391 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 396/400
16/16 - 0s - loss: 0.0308 - accuracy: 1.0000 - val_loss: 0.0388 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 397/400
16/16 - 0s - loss: 0.0306 - accuracy: 1.0000 - val_loss: 0.0386 - val_accuracy: 0.9980 - 80ms/epoch - 5ms/step
Epoch 398/400
16/16 - 0s - loss: 0.0304 - accuracy: 1.0000 - val_loss: 0.0384 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 399/400
16/16 - 0s - loss: 0.0302 - accuracy: 1.0000 - val_loss: 0.0382 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 400/400
16/16 - 0s - loss: 0.0300 - accuracy: 1.0000 - val_loss: 0.0379 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
第2个弱分类器训练完毕
Epoch 1/400
16/16 - 1s - loss: 0.5794 - accuracy: 0.7400 - val_loss: 0.6263 - val_accuracy: 0.6960 - 812ms/epoch - 51ms/step
Epoch 2/400
16/16 - 0s - loss: 0.5671 - accuracy: 0.7400 - val_loss: 0.6141 - val_accuracy: 0.7040 - 71ms/epoch - 4ms/step
Epoch 3/400
16/16 - 0s - loss: 0.5552 - accuracy: 0.7440 - val_loss: 0.6023 - val_accuracy: 0.7040 - 122ms/epoch - 8ms/step
Epoch 4/400
16/16 - 0s - loss: 0.5438 - accuracy: 0.7440 - val_loss: 0.5907 - val_accuracy: 0.7040 - 67ms/epoch - 4ms/step
Epoch 5/400
16/16 - 0s - loss: 0.5325 - accuracy: 0.7460 - val_loss: 0.5796 - val_accuracy: 0.7080 - 87ms/epoch - 5ms/step
Epoch 6/400
16/16 - 0s - loss: 0.5217 - accuracy: 0.7460 - val_loss: 0.5685 - val_accuracy: 0.7100 - 72ms/epoch - 4ms/step
Epoch 7/400
16/16 - 0s - loss: 0.5112 - accuracy: 0.7500 - val_loss: 0.5578 - val_accuracy: 0.7160 - 71ms/epoch - 4ms/step
Epoch 8/400
16/16 - 0s - loss: 0.5008 - accuracy: 0.7520 - val_loss: 0.5475 - val_accuracy: 0.7240 - 124ms/epoch - 8ms/step
Epoch 9/400
16/16 - 0s - loss: 0.4908 - accuracy: 0.7560 - val_loss: 0.5377 - val_accuracy: 0.7260 - 113ms/epoch - 7ms/step
Epoch 10/400
16/16 - 0s - loss: 0.4811 - accuracy: 0.7600 - val_loss: 0.5281 - val_accuracy: 0.7320 - 73ms/epoch - 5ms/step
Epoch 11/400
16/16 - 0s - loss: 0.4720 - accuracy: 0.7620 - val_loss: 0.5179 - val_accuracy: 0.7420 - 108ms/epoch - 7ms/step
Epoch 12/400
16/16 - 0s - loss: 0.4622 - accuracy: 0.7640 - val_loss: 0.5087 - val_accuracy: 0.7440 - 112ms/epoch - 7ms/step
Epoch 13/400
16/16 - 0s - loss: 0.4535 - accuracy: 0.7660 - val_loss: 0.4993 - val_accuracy: 0.7440 - 72ms/epoch - 5ms/step
Epoch 14/400
16/16 - 0s - loss: 0.4447 - accuracy: 0.7680 - val_loss: 0.4902 - val_accuracy: 0.7480 - 113ms/epoch - 7ms/step
Epoch 15/400
16/16 - 0s - loss: 0.4362 - accuracy: 0.7680 - val_loss: 0.4815 - val_accuracy: 0.7540 - 70ms/epoch - 4ms/step
Epoch 16/400
16/16 - 0s - loss: 0.4277 - accuracy: 0.7720 - val_loss: 0.4735 - val_accuracy: 0.7560 - 85ms/epoch - 5ms/step
Epoch 17/400
16/16 - 0s - loss: 0.4199 - accuracy: 0.7780 - val_loss: 0.4653 - val_accuracy: 0.7600 - 111ms/epoch - 7ms/step
Epoch 18/400
16/16 - 0s - loss: 0.4122 - accuracy: 0.7800 - val_loss: 0.4572 - val_accuracy: 0.7600 - 71ms/epoch - 4ms/step
Epoch 19/400
16/16 - 0s - loss: 0.4046 - accuracy: 0.7800 - val_loss: 0.4495 - val_accuracy: 0.7620 - 70ms/epoch - 4ms/step
Epoch 20/400
16/16 - 0s - loss: 0.3971 - accuracy: 0.7840 - val_loss: 0.4419 - val_accuracy: 0.7640 - 113ms/epoch - 7ms/step
Epoch 21/400
16/16 - 0s - loss: 0.3899 - accuracy: 0.7900 - val_loss: 0.4345 - val_accuracy: 0.7720 - 111ms/epoch - 7ms/step
Epoch 22/400
16/16 - 0s - loss: 0.3829 - accuracy: 0.7960 - val_loss: 0.4272 - val_accuracy: 0.7720 - 119ms/epoch - 7ms/step
Epoch 23/400
16/16 - 0s - loss: 0.3761 - accuracy: 0.7960 - val_loss: 0.4201 - val_accuracy: 0.7780 - 77ms/epoch - 5ms/step
Epoch 24/400
16/16 - 0s - loss: 0.3695 - accuracy: 0.8000 - val_loss: 0.4132 - val_accuracy: 0.7780 - 76ms/epoch - 5ms/step
Epoch 25/400
16/16 - 0s - loss: 0.3631 - accuracy: 0.8080 - val_loss: 0.4066 - val_accuracy: 0.7780 - 81ms/epoch - 5ms/step
Epoch 26/400
16/16 - 0s - loss: 0.3568 - accuracy: 0.8100 - val_loss: 0.4002 - val_accuracy: 0.7820 - 112ms/epoch - 7ms/step
Epoch 27/400
16/16 - 0s - loss: 0.3506 - accuracy: 0.8140 - val_loss: 0.3940 - val_accuracy: 0.7860 - 70ms/epoch - 4ms/step
Epoch 28/400
16/16 - 0s - loss: 0.3448 - accuracy: 0.8220 - val_loss: 0.3876 - val_accuracy: 0.7860 - 108ms/epoch - 7ms/step
Epoch 29/400
16/16 - 0s - loss: 0.3390 - accuracy: 0.8240 - val_loss: 0.3817 - val_accuracy: 0.7880 - 117ms/epoch - 7ms/step
Epoch 30/400
16/16 - 0s - loss: 0.3333 - accuracy: 0.8300 - val_loss: 0.3761 - val_accuracy: 0.8020 - 73ms/epoch - 5ms/step
Epoch 31/400
16/16 - 0s - loss: 0.3281 - accuracy: 0.8320 - val_loss: 0.3702 - val_accuracy: 0.8040 - 74ms/epoch - 5ms/step
Epoch 32/400
16/16 - 0s - loss: 0.3228 - accuracy: 0.8360 - val_loss: 0.3645 - val_accuracy: 0.8060 - 68ms/epoch - 4ms/step
Epoch 33/400
16/16 - 0s - loss: 0.3175 - accuracy: 0.8380 - val_loss: 0.3593 - val_accuracy: 0.8120 - 68ms/epoch - 4ms/step
Epoch 34/400
16/16 - 0s - loss: 0.3125 - accuracy: 0.8460 - val_loss: 0.3541 - val_accuracy: 0.8180 - 67ms/epoch - 4ms/step
Epoch 35/400
16/16 - 0s - loss: 0.3077 - accuracy: 0.8500 - val_loss: 0.3489 - val_accuracy: 0.8180 - 73ms/epoch - 5ms/step
Epoch 36/400
16/16 - 0s - loss: 0.3031 - accuracy: 0.8580 - val_loss: 0.3439 - val_accuracy: 0.8220 - 74ms/epoch - 5ms/step
Epoch 37/400
16/16 - 0s - loss: 0.2984 - accuracy: 0.8600 - val_loss: 0.3391 - val_accuracy: 0.8260 - 110ms/epoch - 7ms/step
Epoch 38/400
16/16 - 0s - loss: 0.2941 - accuracy: 0.8660 - val_loss: 0.3343 - val_accuracy: 0.8300 - 82ms/epoch - 5ms/step
Epoch 39/400
16/16 - 0s - loss: 0.2897 - accuracy: 0.8680 - val_loss: 0.3298 - val_accuracy: 0.8340 - 74ms/epoch - 5ms/step
Epoch 40/400
16/16 - 0s - loss: 0.2855 - accuracy: 0.8680 - val_loss: 0.3253 - val_accuracy: 0.8380 - 111ms/epoch - 7ms/step
Epoch 41/400
16/16 - 0s - loss: 0.2815 - accuracy: 0.8720 - val_loss: 0.3209 - val_accuracy: 0.8400 - 74ms/epoch - 5ms/step
Epoch 42/400
16/16 - 0s - loss: 0.2774 - accuracy: 0.8740 - val_loss: 0.3168 - val_accuracy: 0.8440 - 69ms/epoch - 4ms/step
Epoch 43/400
16/16 - 0s - loss: 0.2737 - accuracy: 0.8760 - val_loss: 0.3126 - val_accuracy: 0.8520 - 68ms/epoch - 4ms/step
Epoch 44/400
16/16 - 0s - loss: 0.2700 - accuracy: 0.8780 - val_loss: 0.3084 - val_accuracy: 0.8580 - 111ms/epoch - 7ms/step
Epoch 45/400
16/16 - 0s - loss: 0.2661 - accuracy: 0.8880 - val_loss: 0.3048 - val_accuracy: 0.8620 - 73ms/epoch - 5ms/step
Epoch 46/400
16/16 - 0s - loss: 0.2627 - accuracy: 0.8900 - val_loss: 0.3009 - val_accuracy: 0.8660 - 110ms/epoch - 7ms/step
Epoch 47/400
16/16 - 0s - loss: 0.2592 - accuracy: 0.8920 - val_loss: 0.2972 - val_accuracy: 0.8680 - 107ms/epoch - 7ms/step
Epoch 48/400
16/16 - 0s - loss: 0.2560 - accuracy: 0.8960 - val_loss: 0.2935 - val_accuracy: 0.8680 - 76ms/epoch - 5ms/step
Epoch 49/400
16/16 - 0s - loss: 0.2526 - accuracy: 0.8960 - val_loss: 0.2898 - val_accuracy: 0.8680 - 129ms/epoch - 8ms/step
Epoch 50/400
16/16 - 0s - loss: 0.2494 - accuracy: 0.8980 - val_loss: 0.2866 - val_accuracy: 0.8680 - 68ms/epoch - 4ms/step
Epoch 51/400
16/16 - 0s - loss: 0.2463 - accuracy: 0.9000 - val_loss: 0.2833 - val_accuracy: 0.8700 - 105ms/epoch - 7ms/step
Epoch 52/400
16/16 - 0s - loss: 0.2434 - accuracy: 0.9020 - val_loss: 0.2799 - val_accuracy: 0.8700 - 108ms/epoch - 7ms/step
Epoch 53/400
16/16 - 0s - loss: 0.2404 - accuracy: 0.9040 - val_loss: 0.2766 - val_accuracy: 0.8700 - 72ms/epoch - 4ms/step
Epoch 54/400
16/16 - 0s - loss: 0.2376 - accuracy: 0.9040 - val_loss: 0.2734 - val_accuracy: 0.8720 - 73ms/epoch - 5ms/step
Epoch 55/400
16/16 - 0s - loss: 0.2348 - accuracy: 0.9120 - val_loss: 0.2705 - val_accuracy: 0.8720 - 74ms/epoch - 5ms/step
Epoch 56/400
16/16 - 0s - loss: 0.2322 - accuracy: 0.9140 - val_loss: 0.2675 - val_accuracy: 0.8740 - 71ms/epoch - 4ms/step
Epoch 57/400
16/16 - 0s - loss: 0.2295 - accuracy: 0.9160 - val_loss: 0.2647 - val_accuracy: 0.8780 - 76ms/epoch - 5ms/step
Epoch 58/400
16/16 - 0s - loss: 0.2270 - accuracy: 0.9160 - val_loss: 0.2619 - val_accuracy: 0.8780 - 79ms/epoch - 5ms/step
Epoch 59/400
16/16 - 0s - loss: 0.2246 - accuracy: 0.9160 - val_loss: 0.2590 - val_accuracy: 0.8820 - 77ms/epoch - 5ms/step
Epoch 60/400
16/16 - 0s - loss: 0.2221 - accuracy: 0.9160 - val_loss: 0.2563 - val_accuracy: 0.8860 - 82ms/epoch - 5ms/step
Epoch 61/400
16/16 - 0s - loss: 0.2198 - accuracy: 0.9160 - val_loss: 0.2537 - val_accuracy: 0.8920 - 114ms/epoch - 7ms/step
Epoch 62/400
16/16 - 0s - loss: 0.2176 - accuracy: 0.9180 - val_loss: 0.2512 - val_accuracy: 0.8920 - 82ms/epoch - 5ms/step
Epoch 63/400
16/16 - 0s - loss: 0.2153 - accuracy: 0.9200 - val_loss: 0.2488 - val_accuracy: 0.8920 - 73ms/epoch - 5ms/step
Epoch 64/400
16/16 - 0s - loss: 0.2132 - accuracy: 0.9240 - val_loss: 0.2462 - val_accuracy: 0.8920 - 70ms/epoch - 4ms/step
Epoch 65/400
16/16 - 0s - loss: 0.2111 - accuracy: 0.9240 - val_loss: 0.2439 - val_accuracy: 0.8960 - 76ms/epoch - 5ms/step
Epoch 66/400
16/16 - 0s - loss: 0.2091 - accuracy: 0.9320 - val_loss: 0.2417 - val_accuracy: 0.8980 - 115ms/epoch - 7ms/step
Epoch 67/400
16/16 - 0s - loss: 0.2071 - accuracy: 0.9340 - val_loss: 0.2394 - val_accuracy: 0.9020 - 76ms/epoch - 5ms/step
Epoch 68/400
16/16 - 0s - loss: 0.2051 - accuracy: 0.9360 - val_loss: 0.2372 - val_accuracy: 0.9020 - 81ms/epoch - 5ms/step
Epoch 69/400
16/16 - 0s - loss: 0.2033 - accuracy: 0.9380 - val_loss: 0.2350 - val_accuracy: 0.9020 - 74ms/epoch - 5ms/step
Epoch 70/400
16/16 - 0s - loss: 0.2015 - accuracy: 0.9400 - val_loss: 0.2328 - val_accuracy: 0.9100 - 69ms/epoch - 4ms/step
Epoch 71/400
16/16 - 0s - loss: 0.1996 - accuracy: 0.9440 - val_loss: 0.2309 - val_accuracy: 0.9100 - 70ms/epoch - 4ms/step
Epoch 72/400
16/16 - 0s - loss: 0.1979 - accuracy: 0.9460 - val_loss: 0.2288 - val_accuracy: 0.9140 - 125ms/epoch - 8ms/step
Epoch 73/400
16/16 - 0s - loss: 0.1961 - accuracy: 0.9460 - val_loss: 0.2268 - val_accuracy: 0.9180 - 110ms/epoch - 7ms/step
Epoch 74/400
16/16 - 0s - loss: 0.1945 - accuracy: 0.9460 - val_loss: 0.2248 - val_accuracy: 0.9240 - 69ms/epoch - 4ms/step
Epoch 75/400
16/16 - 0s - loss: 0.1928 - accuracy: 0.9460 - val_loss: 0.2231 - val_accuracy: 0.9240 - 109ms/epoch - 7ms/step
Epoch 76/400
16/16 - 0s - loss: 0.1913 - accuracy: 0.9480 - val_loss: 0.2211 - val_accuracy: 0.9280 - 109ms/epoch - 7ms/step
Epoch 77/400
16/16 - 0s - loss: 0.1897 - accuracy: 0.9480 - val_loss: 0.2193 - val_accuracy: 0.9320 - 74ms/epoch - 5ms/step
Epoch 78/400
16/16 - 0s - loss: 0.1882 - accuracy: 0.9480 - val_loss: 0.2174 - val_accuracy: 0.9320 - 114ms/epoch - 7ms/step
Epoch 79/400
16/16 - 0s - loss: 0.1866 - accuracy: 0.9480 - val_loss: 0.2158 - val_accuracy: 0.9380 - 73ms/epoch - 5ms/step
Epoch 80/400
16/16 - 0s - loss: 0.1852 - accuracy: 0.9480 - val_loss: 0.2140 - val_accuracy: 0.9380 - 110ms/epoch - 7ms/step
Epoch 81/400
16/16 - 0s - loss: 0.1838 - accuracy: 0.9500 - val_loss: 0.2123 - val_accuracy: 0.9380 - 112ms/epoch - 7ms/step
Epoch 82/400
16/16 - 0s - loss: 0.1824 - accuracy: 0.9500 - val_loss: 0.2107 - val_accuracy: 0.9400 - 69ms/epoch - 4ms/step
Epoch 83/400
16/16 - 0s - loss: 0.1810 - accuracy: 0.9540 - val_loss: 0.2090 - val_accuracy: 0.9400 - 69ms/epoch - 4ms/step
Epoch 84/400
16/16 - 0s - loss: 0.1796 - accuracy: 0.9580 - val_loss: 0.2074 - val_accuracy: 0.9440 - 107ms/epoch - 7ms/step
Epoch 85/400
16/16 - 0s - loss: 0.1783 - accuracy: 0.9600 - val_loss: 0.2058 - val_accuracy: 0.9460 - 122ms/epoch - 8ms/step
Epoch 86/400
16/16 - 0s - loss: 0.1770 - accuracy: 0.9600 - val_loss: 0.2043 - val_accuracy: 0.9460 - 76ms/epoch - 5ms/step
Epoch 87/400
16/16 - 0s - loss: 0.1757 - accuracy: 0.9600 - val_loss: 0.2027 - val_accuracy: 0.9460 - 78ms/epoch - 5ms/step
Epoch 88/400
16/16 - 0s - loss: 0.1744 - accuracy: 0.9620 - val_loss: 0.2013 - val_accuracy: 0.9460 - 74ms/epoch - 5ms/step
Epoch 89/400
16/16 - 0s - loss: 0.1732 - accuracy: 0.9640 - val_loss: 0.1997 - val_accuracy: 0.9480 - 73ms/epoch - 5ms/step
Epoch 90/400
16/16 - 0s - loss: 0.1719 - accuracy: 0.9640 - val_loss: 0.1982 - val_accuracy: 0.9480 - 68ms/epoch - 4ms/step
Epoch 91/400
16/16 - 0s - loss: 0.1707 - accuracy: 0.9640 - val_loss: 0.1966 - val_accuracy: 0.9480 - 110ms/epoch - 7ms/step
Epoch 92/400
16/16 - 0s - loss: 0.1695 - accuracy: 0.9640 - val_loss: 0.1953 - val_accuracy: 0.9480 - 76ms/epoch - 5ms/step
Epoch 93/400
16/16 - 0s - loss: 0.1683 - accuracy: 0.9640 - val_loss: 0.1938 - val_accuracy: 0.9520 - 110ms/epoch - 7ms/step
Epoch 94/400
16/16 - 0s - loss: 0.1671 - accuracy: 0.9640 - val_loss: 0.1924 - val_accuracy: 0.9540 - 114ms/epoch - 7ms/step
Epoch 95/400
16/16 - 0s - loss: 0.1659 - accuracy: 0.9640 - val_loss: 0.1911 - val_accuracy: 0.9540 - 67ms/epoch - 4ms/step
Epoch 96/400
16/16 - 0s - loss: 0.1648 - accuracy: 0.9680 - val_loss: 0.1896 - val_accuracy: 0.9540 - 67ms/epoch - 4ms/step
Epoch 97/400
16/16 - 0s - loss: 0.1636 - accuracy: 0.9680 - val_loss: 0.1882 - val_accuracy: 0.9540 - 68ms/epoch - 4ms/step
Epoch 98/400
16/16 - 0s - loss: 0.1625 - accuracy: 0.9700 - val_loss: 0.1868 - val_accuracy: 0.9540 - 71ms/epoch - 4ms/step
Epoch 99/400
16/16 - 0s - loss: 0.1613 - accuracy: 0.9700 - val_loss: 0.1855 - val_accuracy: 0.9560 - 72ms/epoch - 5ms/step
Epoch 100/400
16/16 - 0s - loss: 0.1602 - accuracy: 0.9700 - val_loss: 0.1841 - val_accuracy: 0.9560 - 115ms/epoch - 7ms/step
Epoch 101/400
16/16 - 0s - loss: 0.1591 - accuracy: 0.9700 - val_loss: 0.1827 - val_accuracy: 0.9560 - 70ms/epoch - 4ms/step
Epoch 102/400
16/16 - 0s - loss: 0.1579 - accuracy: 0.9720 - val_loss: 0.1814 - val_accuracy: 0.9560 - 117ms/epoch - 7ms/step
Epoch 103/400
16/16 - 0s - loss: 0.1568 - accuracy: 0.9720 - val_loss: 0.1799 - val_accuracy: 0.9560 - 113ms/epoch - 7ms/step
Epoch 104/400
16/16 - 0s - loss: 0.1557 - accuracy: 0.9720 - val_loss: 0.1785 - val_accuracy: 0.9560 - 124ms/epoch - 8ms/step
Epoch 105/400
16/16 - 0s - loss: 0.1545 - accuracy: 0.9760 - val_loss: 0.1771 - val_accuracy: 0.9580 - 73ms/epoch - 5ms/step
Epoch 106/400
16/16 - 0s - loss: 0.1534 - accuracy: 0.9760 - val_loss: 0.1758 - val_accuracy: 0.9580 - 71ms/epoch - 4ms/step
Epoch 107/400
16/16 - 0s - loss: 0.1523 - accuracy: 0.9760 - val_loss: 0.1744 - val_accuracy: 0.9600 - 116ms/epoch - 7ms/step
Epoch 108/400
16/16 - 0s - loss: 0.1512 - accuracy: 0.9760 - val_loss: 0.1730 - val_accuracy: 0.9600 - 119ms/epoch - 7ms/step
Epoch 109/400
16/16 - 0s - loss: 0.1500 - accuracy: 0.9780 - val_loss: 0.1717 - val_accuracy: 0.9620 - 112ms/epoch - 7ms/step
Epoch 110/400
16/16 - 0s - loss: 0.1489 - accuracy: 0.9780 - val_loss: 0.1703 - val_accuracy: 0.9680 - 69ms/epoch - 4ms/step
Epoch 111/400
16/16 - 0s - loss: 0.1478 - accuracy: 0.9780 - val_loss: 0.1689 - val_accuracy: 0.9680 - 111ms/epoch - 7ms/step
Epoch 112/400
16/16 - 0s - loss: 0.1467 - accuracy: 0.9780 - val_loss: 0.1676 - val_accuracy: 0.9680 - 116ms/epoch - 7ms/step
Epoch 113/400
16/16 - 0s - loss: 0.1455 - accuracy: 0.9800 - val_loss: 0.1662 - val_accuracy: 0.9720 - 110ms/epoch - 7ms/step
Epoch 114/400
16/16 - 0s - loss: 0.1444 - accuracy: 0.9800 - val_loss: 0.1649 - val_accuracy: 0.9720 - 65ms/epoch - 4ms/step
Epoch 115/400
16/16 - 0s - loss: 0.1433 - accuracy: 0.9800 - val_loss: 0.1635 - val_accuracy: 0.9720 - 69ms/epoch - 4ms/step
Epoch 116/400
16/16 - 0s - loss: 0.1422 - accuracy: 0.9800 - val_loss: 0.1620 - val_accuracy: 0.9720 - 71ms/epoch - 4ms/step
Epoch 117/400
16/16 - 0s - loss: 0.1410 - accuracy: 0.9800 - val_loss: 0.1607 - val_accuracy: 0.9740 - 75ms/epoch - 5ms/step
Epoch 118/400
16/16 - 0s - loss: 0.1399 - accuracy: 0.9800 - val_loss: 0.1594 - val_accuracy: 0.9760 - 109ms/epoch - 7ms/step
Epoch 119/400
16/16 - 0s - loss: 0.1388 - accuracy: 0.9800 - val_loss: 0.1580 - val_accuracy: 0.9760 - 87ms/epoch - 5ms/step
Epoch 120/400
16/16 - 0s - loss: 0.1376 - accuracy: 0.9800 - val_loss: 0.1567 - val_accuracy: 0.9760 - 115ms/epoch - 7ms/step
Epoch 121/400
16/16 - 0s - loss: 0.1365 - accuracy: 0.9800 - val_loss: 0.1554 - val_accuracy: 0.9760 - 74ms/epoch - 5ms/step
Epoch 122/400
16/16 - 0s - loss: 0.1354 - accuracy: 0.9800 - val_loss: 0.1541 - val_accuracy: 0.9760 - 73ms/epoch - 5ms/step
Epoch 123/400
16/16 - 0s - loss: 0.1343 - accuracy: 0.9820 - val_loss: 0.1527 - val_accuracy: 0.9760 - 69ms/epoch - 4ms/step
Epoch 124/400
16/16 - 0s - loss: 0.1331 - accuracy: 0.9820 - val_loss: 0.1514 - val_accuracy: 0.9760 - 75ms/epoch - 5ms/step
Epoch 125/400
16/16 - 0s - loss: 0.1320 - accuracy: 0.9820 - val_loss: 0.1500 - val_accuracy: 0.9760 - 123ms/epoch - 8ms/step
Epoch 126/400
16/16 - 0s - loss: 0.1309 - accuracy: 0.9820 - val_loss: 0.1486 - val_accuracy: 0.9760 - 111ms/epoch - 7ms/step
Epoch 127/400
16/16 - 0s - loss: 0.1297 - accuracy: 0.9820 - val_loss: 0.1473 - val_accuracy: 0.9760 - 70ms/epoch - 4ms/step
Epoch 128/400
16/16 - 0s - loss: 0.1287 - accuracy: 0.9820 - val_loss: 0.1459 - val_accuracy: 0.9760 - 69ms/epoch - 4ms/step
Epoch 129/400
16/16 - 0s - loss: 0.1275 - accuracy: 0.9820 - val_loss: 0.1446 - val_accuracy: 0.9760 - 118ms/epoch - 7ms/step
Epoch 130/400
16/16 - 0s - loss: 0.1264 - accuracy: 0.9820 - val_loss: 0.1433 - val_accuracy: 0.9760 - 74ms/epoch - 5ms/step
Epoch 131/400
16/16 - 0s - loss: 0.1253 - accuracy: 0.9820 - val_loss: 0.1420 - val_accuracy: 0.9780 - 120ms/epoch - 8ms/step
Epoch 132/400
16/16 - 0s - loss: 0.1242 - accuracy: 0.9820 - val_loss: 0.1407 - val_accuracy: 0.9780 - 74ms/epoch - 5ms/step
Epoch 133/400
16/16 - 0s - loss: 0.1231 - accuracy: 0.9840 - val_loss: 0.1393 - val_accuracy: 0.9780 - 74ms/epoch - 5ms/step
Epoch 134/400
16/16 - 0s - loss: 0.1219 - accuracy: 0.9840 - val_loss: 0.1381 - val_accuracy: 0.9780 - 73ms/epoch - 5ms/step
Epoch 135/400
16/16 - 0s - loss: 0.1209 - accuracy: 0.9840 - val_loss: 0.1367 - val_accuracy: 0.9800 - 79ms/epoch - 5ms/step
Epoch 136/400
16/16 - 0s - loss: 0.1198 - accuracy: 0.9860 - val_loss: 0.1355 - val_accuracy: 0.9800 - 81ms/epoch - 5ms/step
Epoch 137/400
16/16 - 0s - loss: 0.1187 - accuracy: 0.9860 - val_loss: 0.1343 - val_accuracy: 0.9800 - 71ms/epoch - 4ms/step
Epoch 138/400
16/16 - 0s - loss: 0.1176 - accuracy: 0.9860 - val_loss: 0.1330 - val_accuracy: 0.9820 - 74ms/epoch - 5ms/step
Epoch 139/400
16/16 - 0s - loss: 0.1166 - accuracy: 0.9860 - val_loss: 0.1318 - val_accuracy: 0.9820 - 111ms/epoch - 7ms/step
Epoch 140/400
16/16 - 0s - loss: 0.1155 - accuracy: 0.9860 - val_loss: 0.1307 - val_accuracy: 0.9820 - 70ms/epoch - 4ms/step
Epoch 141/400
16/16 - 0s - loss: 0.1144 - accuracy: 0.9860 - val_loss: 0.1295 - val_accuracy: 0.9840 - 73ms/epoch - 5ms/step
Epoch 142/400
16/16 - 0s - loss: 0.1134 - accuracy: 0.9860 - val_loss: 0.1282 - val_accuracy: 0.9840 - 114ms/epoch - 7ms/step
Epoch 143/400
16/16 - 0s - loss: 0.1124 - accuracy: 0.9880 - val_loss: 0.1271 - val_accuracy: 0.9840 - 70ms/epoch - 4ms/step
Epoch 144/400
16/16 - 0s - loss: 0.1114 - accuracy: 0.9880 - val_loss: 0.1259 - val_accuracy: 0.9840 - 72ms/epoch - 5ms/step
Epoch 145/400
16/16 - 0s - loss: 0.1103 - accuracy: 0.9880 - val_loss: 0.1247 - val_accuracy: 0.9840 - 74ms/epoch - 5ms/step
Epoch 146/400
16/16 - 0s - loss: 0.1093 - accuracy: 0.9880 - val_loss: 0.1236 - val_accuracy: 0.9860 - 71ms/epoch - 4ms/step
Epoch 147/400
16/16 - 0s - loss: 0.1083 - accuracy: 0.9880 - val_loss: 0.1224 - val_accuracy: 0.9860 - 80ms/epoch - 5ms/step
Epoch 148/400
16/16 - 0s - loss: 0.1073 - accuracy: 0.9880 - val_loss: 0.1212 - val_accuracy: 0.9860 - 79ms/epoch - 5ms/step
Epoch 149/400
16/16 - 0s - loss: 0.1063 - accuracy: 0.9880 - val_loss: 0.1202 - val_accuracy: 0.9860 - 72ms/epoch - 4ms/step
Epoch 150/400
16/16 - 0s - loss: 0.1054 - accuracy: 0.9860 - val_loss: 0.1191 - val_accuracy: 0.9860 - 70ms/epoch - 4ms/step
Epoch 151/400
16/16 - 0s - loss: 0.1044 - accuracy: 0.9860 - val_loss: 0.1180 - val_accuracy: 0.9860 - 114ms/epoch - 7ms/step
Epoch 152/400
16/16 - 0s - loss: 0.1034 - accuracy: 0.9860 - val_loss: 0.1168 - val_accuracy: 0.9860 - 73ms/epoch - 5ms/step
Epoch 153/400
16/16 - 0s - loss: 0.1025 - accuracy: 0.9860 - val_loss: 0.1158 - val_accuracy: 0.9860 - 76ms/epoch - 5ms/step
Epoch 154/400
16/16 - 0s - loss: 0.1016 - accuracy: 0.9860 - val_loss: 0.1147 - val_accuracy: 0.9860 - 75ms/epoch - 5ms/step
Epoch 155/400
16/16 - 0s - loss: 0.1006 - accuracy: 0.9860 - val_loss: 0.1136 - val_accuracy: 0.9860 - 74ms/epoch - 5ms/step
Epoch 156/400
16/16 - 0s - loss: 0.0997 - accuracy: 0.9860 - val_loss: 0.1126 - val_accuracy: 0.9860 - 115ms/epoch - 7ms/step
Epoch 157/400
16/16 - 0s - loss: 0.0988 - accuracy: 0.9860 - val_loss: 0.1116 - val_accuracy: 0.9860 - 73ms/epoch - 5ms/step
Epoch 158/400
16/16 - 0s - loss: 0.0979 - accuracy: 0.9860 - val_loss: 0.1106 - val_accuracy: 0.9860 - 223ms/epoch - 14ms/step
Epoch 159/400
16/16 - 0s - loss: 0.0970 - accuracy: 0.9860 - val_loss: 0.1097 - val_accuracy: 0.9860 - 287ms/epoch - 18ms/step
Epoch 160/400
16/16 - 0s - loss: 0.0962 - accuracy: 0.9860 - val_loss: 0.1086 - val_accuracy: 0.9860 - 209ms/epoch - 13ms/step
Epoch 161/400
16/16 - 0s - loss: 0.0953 - accuracy: 0.9860 - val_loss: 0.1076 - val_accuracy: 0.9880 - 80ms/epoch - 5ms/step
Epoch 162/400
16/16 - 0s - loss: 0.0944 - accuracy: 0.9860 - val_loss: 0.1067 - val_accuracy: 0.9900 - 109ms/epoch - 7ms/step
Epoch 163/400
16/16 - 0s - loss: 0.0936 - accuracy: 0.9860 - val_loss: 0.1057 - val_accuracy: 0.9900 - 70ms/epoch - 4ms/step
Epoch 164/400
16/16 - 0s - loss: 0.0927 - accuracy: 0.9860 - val_loss: 0.1048 - val_accuracy: 0.9920 - 71ms/epoch - 4ms/step
Epoch 165/400
16/16 - 0s - loss: 0.0919 - accuracy: 0.9860 - val_loss: 0.1039 - val_accuracy: 0.9920 - 77ms/epoch - 5ms/step
Epoch 166/400
16/16 - 0s - loss: 0.0911 - accuracy: 0.9860 - val_loss: 0.1030 - val_accuracy: 0.9920 - 226ms/epoch - 14ms/step
Epoch 167/400
16/16 - 0s - loss: 0.0903 - accuracy: 0.9860 - val_loss: 0.1021 - val_accuracy: 0.9920 - 332ms/epoch - 21ms/step
Epoch 168/400
16/16 - 0s - loss: 0.0894 - accuracy: 0.9880 - val_loss: 0.1012 - val_accuracy: 0.9920 - 179ms/epoch - 11ms/step
Epoch 169/400
16/16 - 0s - loss: 0.0887 - accuracy: 0.9900 - val_loss: 0.1003 - val_accuracy: 0.9920 - 69ms/epoch - 4ms/step
Epoch 170/400
16/16 - 0s - loss: 0.0879 - accuracy: 0.9900 - val_loss: 0.0995 - val_accuracy: 0.9920 - 76ms/epoch - 5ms/step
Epoch 171/400
16/16 - 0s - loss: 0.0871 - accuracy: 0.9900 - val_loss: 0.0986 - val_accuracy: 0.9920 - 68ms/epoch - 4ms/step
Epoch 172/400
16/16 - 0s - loss: 0.0863 - accuracy: 0.9920 - val_loss: 0.0978 - val_accuracy: 0.9920 - 70ms/epoch - 4ms/step
Epoch 173/400
16/16 - 0s - loss: 0.0856 - accuracy: 0.9920 - val_loss: 0.0969 - val_accuracy: 0.9940 - 76ms/epoch - 5ms/step
Epoch 174/400
16/16 - 0s - loss: 0.0849 - accuracy: 0.9920 - val_loss: 0.0961 - val_accuracy: 0.9940 - 352ms/epoch - 22ms/step
Epoch 175/400
16/16 - 0s - loss: 0.0841 - accuracy: 0.9920 - val_loss: 0.0953 - val_accuracy: 0.9940 - 228ms/epoch - 14ms/step
Epoch 176/400
16/16 - 0s - loss: 0.0834 - accuracy: 0.9920 - val_loss: 0.0944 - val_accuracy: 0.9940 - 98ms/epoch - 6ms/step
Epoch 177/400
16/16 - 0s - loss: 0.0826 - accuracy: 0.9920 - val_loss: 0.0937 - val_accuracy: 0.9940 - 69ms/epoch - 4ms/step
Epoch 178/400
16/16 - 0s - loss: 0.0819 - accuracy: 0.9920 - val_loss: 0.0929 - val_accuracy: 0.9940 - 71ms/epoch - 4ms/step
Epoch 179/400
16/16 - 0s - loss: 0.0812 - accuracy: 0.9920 - val_loss: 0.0920 - val_accuracy: 0.9940 - 73ms/epoch - 5ms/step
Epoch 180/400
16/16 - 0s - loss: 0.0805 - accuracy: 0.9920 - val_loss: 0.0913 - val_accuracy: 0.9940 - 124ms/epoch - 8ms/step
Epoch 181/400
16/16 - 0s - loss: 0.0799 - accuracy: 0.9920 - val_loss: 0.0905 - val_accuracy: 0.9940 - 115ms/epoch - 7ms/step
Epoch 182/400
16/16 - 0s - loss: 0.0792 - accuracy: 0.9920 - val_loss: 0.0899 - val_accuracy: 0.9940 - 68ms/epoch - 4ms/step
Epoch 183/400
16/16 - 0s - loss: 0.0785 - accuracy: 0.9920 - val_loss: 0.0891 - val_accuracy: 0.9940 - 112ms/epoch - 7ms/step
Epoch 184/400
16/16 - 0s - loss: 0.0779 - accuracy: 0.9920 - val_loss: 0.0884 - val_accuracy: 0.9940 - 113ms/epoch - 7ms/step
Epoch 185/400
16/16 - 0s - loss: 0.0772 - accuracy: 0.9900 - val_loss: 0.0877 - val_accuracy: 0.9940 - 71ms/epoch - 4ms/step
Epoch 186/400
16/16 - 0s - loss: 0.0766 - accuracy: 0.9900 - val_loss: 0.0870 - val_accuracy: 0.9940 - 75ms/epoch - 5ms/step
Epoch 187/400
16/16 - 0s - loss: 0.0760 - accuracy: 0.9900 - val_loss: 0.0863 - val_accuracy: 0.9920 - 69ms/epoch - 4ms/step
Epoch 188/400
16/16 - 0s - loss: 0.0754 - accuracy: 0.9900 - val_loss: 0.0856 - val_accuracy: 0.9920 - 69ms/epoch - 4ms/step
Epoch 189/400
16/16 - 0s - loss: 0.0748 - accuracy: 0.9900 - val_loss: 0.0849 - val_accuracy: 0.9920 - 71ms/epoch - 4ms/step
Epoch 190/400
16/16 - 0s - loss: 0.0742 - accuracy: 0.9900 - val_loss: 0.0843 - val_accuracy: 0.9920 - 73ms/epoch - 5ms/step
Epoch 191/400
16/16 - 0s - loss: 0.0736 - accuracy: 0.9900 - val_loss: 0.0837 - val_accuracy: 0.9920 - 119ms/epoch - 7ms/step
Epoch 192/400
16/16 - 0s - loss: 0.0730 - accuracy: 0.9900 - val_loss: 0.0830 - val_accuracy: 0.9920 - 69ms/epoch - 4ms/step
Epoch 193/400
16/16 - 0s - loss: 0.0724 - accuracy: 0.9940 - val_loss: 0.0824 - val_accuracy: 0.9920 - 76ms/epoch - 5ms/step
Epoch 194/400
16/16 - 0s - loss: 0.0718 - accuracy: 0.9940 - val_loss: 0.0818 - val_accuracy: 0.9920 - 69ms/epoch - 4ms/step
Epoch 195/400
16/16 - 0s - loss: 0.0713 - accuracy: 0.9940 - val_loss: 0.0811 - val_accuracy: 0.9940 - 71ms/epoch - 4ms/step
Epoch 196/400
16/16 - 0s - loss: 0.0707 - accuracy: 0.9940 - val_loss: 0.0805 - val_accuracy: 0.9940 - 70ms/epoch - 4ms/step
Epoch 197/400
16/16 - 0s - loss: 0.0702 - accuracy: 0.9940 - val_loss: 0.0799 - val_accuracy: 0.9940 - 72ms/epoch - 4ms/step
Epoch 198/400
16/16 - 0s - loss: 0.0696 - accuracy: 0.9940 - val_loss: 0.0793 - val_accuracy: 0.9940 - 73ms/epoch - 5ms/step
Epoch 199/400
16/16 - 0s - loss: 0.0691 - accuracy: 0.9940 - val_loss: 0.0787 - val_accuracy: 0.9940 - 109ms/epoch - 7ms/step
Epoch 200/400
16/16 - 0s - loss: 0.0686 - accuracy: 0.9940 - val_loss: 0.0782 - val_accuracy: 0.9960 - 110ms/epoch - 7ms/step
Epoch 201/400
16/16 - 0s - loss: 0.0681 - accuracy: 0.9940 - val_loss: 0.0776 - val_accuracy: 0.9960 - 77ms/epoch - 5ms/step
Epoch 202/400
16/16 - 0s - loss: 0.0676 - accuracy: 0.9940 - val_loss: 0.0770 - val_accuracy: 0.9960 - 66ms/epoch - 4ms/step
Epoch 203/400
16/16 - 0s - loss: 0.0671 - accuracy: 0.9940 - val_loss: 0.0765 - val_accuracy: 0.9960 - 77ms/epoch - 5ms/step
Epoch 204/400
16/16 - 0s - loss: 0.0666 - accuracy: 0.9940 - val_loss: 0.0759 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 205/400
16/16 - 0s - loss: 0.0661 - accuracy: 0.9940 - val_loss: 0.0753 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 206/400
16/16 - 0s - loss: 0.0656 - accuracy: 0.9940 - val_loss: 0.0749 - val_accuracy: 0.9960 - 72ms/epoch - 5ms/step
Epoch 207/400
16/16 - 0s - loss: 0.0652 - accuracy: 0.9940 - val_loss: 0.0743 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 208/400
16/16 - 0s - loss: 0.0647 - accuracy: 0.9940 - val_loss: 0.0738 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 209/400
16/16 - 0s - loss: 0.0642 - accuracy: 0.9940 - val_loss: 0.0733 - val_accuracy: 0.9960 - 67ms/epoch - 4ms/step
Epoch 210/400
16/16 - 0s - loss: 0.0638 - accuracy: 0.9940 - val_loss: 0.0728 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 211/400
16/16 - 0s - loss: 0.0633 - accuracy: 0.9940 - val_loss: 0.0723 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 212/400
16/16 - 0s - loss: 0.0629 - accuracy: 0.9940 - val_loss: 0.0718 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 213/400
16/16 - 0s - loss: 0.0624 - accuracy: 0.9940 - val_loss: 0.0713 - val_accuracy: 0.9960 - 128ms/epoch - 8ms/step
Epoch 214/400
16/16 - 0s - loss: 0.0620 - accuracy: 0.9940 - val_loss: 0.0708 - val_accuracy: 0.9960 - 88ms/epoch - 6ms/step
Epoch 215/400
16/16 - 0s - loss: 0.0616 - accuracy: 0.9940 - val_loss: 0.0703 - val_accuracy: 0.9960 - 79ms/epoch - 5ms/step
Epoch 216/400
16/16 - 0s - loss: 0.0611 - accuracy: 0.9940 - val_loss: 0.0698 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 217/400
16/16 - 0s - loss: 0.0607 - accuracy: 0.9940 - val_loss: 0.0694 - val_accuracy: 0.9960 - 76ms/epoch - 5ms/step
Epoch 218/400
16/16 - 0s - loss: 0.0603 - accuracy: 0.9940 - val_loss: 0.0689 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 219/400
16/16 - 0s - loss: 0.0599 - accuracy: 0.9940 - val_loss: 0.0685 - val_accuracy: 0.9960 - 118ms/epoch - 7ms/step
Epoch 220/400
16/16 - 0s - loss: 0.0595 - accuracy: 0.9940 - val_loss: 0.0680 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 221/400
16/16 - 0s - loss: 0.0591 - accuracy: 0.9940 - val_loss: 0.0676 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 222/400
16/16 - 0s - loss: 0.0587 - accuracy: 0.9940 - val_loss: 0.0672 - val_accuracy: 0.9960 - 113ms/epoch - 7ms/step
Epoch 223/400
16/16 - 0s - loss: 0.0583 - accuracy: 0.9940 - val_loss: 0.0668 - val_accuracy: 0.9960 - 72ms/epoch - 4ms/step
Epoch 224/400
16/16 - 0s - loss: 0.0580 - accuracy: 0.9940 - val_loss: 0.0663 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 225/400
16/16 - 0s - loss: 0.0576 - accuracy: 0.9940 - val_loss: 0.0659 - val_accuracy: 0.9960 - 124ms/epoch - 8ms/step
Epoch 226/400
16/16 - 0s - loss: 0.0572 - accuracy: 0.9940 - val_loss: 0.0655 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 227/400
16/16 - 0s - loss: 0.0568 - accuracy: 0.9940 - val_loss: 0.0650 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 228/400
16/16 - 0s - loss: 0.0565 - accuracy: 0.9940 - val_loss: 0.0647 - val_accuracy: 0.9960 - 110ms/epoch - 7ms/step
Epoch 229/400
16/16 - 0s - loss: 0.0561 - accuracy: 0.9940 - val_loss: 0.0642 - val_accuracy: 0.9960 - 112ms/epoch - 7ms/step
Epoch 230/400
16/16 - 0s - loss: 0.0558 - accuracy: 0.9940 - val_loss: 0.0639 - val_accuracy: 0.9960 - 77ms/epoch - 5ms/step
Epoch 231/400
16/16 - 0s - loss: 0.0554 - accuracy: 0.9940 - val_loss: 0.0635 - val_accuracy: 0.9940 - 68ms/epoch - 4ms/step
Epoch 232/400
16/16 - 0s - loss: 0.0550 - accuracy: 0.9940 - val_loss: 0.0630 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 233/400
16/16 - 0s - loss: 0.0547 - accuracy: 0.9940 - val_loss: 0.0626 - val_accuracy: 0.9940 - 70ms/epoch - 4ms/step
Epoch 234/400
16/16 - 0s - loss: 0.0543 - accuracy: 0.9940 - val_loss: 0.0623 - val_accuracy: 0.9940 - 111ms/epoch - 7ms/step
Epoch 235/400
16/16 - 0s - loss: 0.0540 - accuracy: 0.9940 - val_loss: 0.0619 - val_accuracy: 0.9940 - 118ms/epoch - 7ms/step
Epoch 236/400
16/16 - 0s - loss: 0.0537 - accuracy: 0.9940 - val_loss: 0.0615 - val_accuracy: 0.9940 - 80ms/epoch - 5ms/step
Epoch 237/400
16/16 - 0s - loss: 0.0534 - accuracy: 0.9940 - val_loss: 0.0611 - val_accuracy: 0.9940 - 108ms/epoch - 7ms/step
Epoch 238/400
16/16 - 0s - loss: 0.0530 - accuracy: 0.9940 - val_loss: 0.0608 - val_accuracy: 0.9940 - 72ms/epoch - 5ms/step
Epoch 239/400
16/16 - 0s - loss: 0.0527 - accuracy: 0.9940 - val_loss: 0.0604 - val_accuracy: 0.9940 - 112ms/epoch - 7ms/step
Epoch 240/400
16/16 - 0s - loss: 0.0524 - accuracy: 0.9940 - val_loss: 0.0600 - val_accuracy: 0.9940 - 109ms/epoch - 7ms/step
Epoch 241/400
16/16 - 0s - loss: 0.0521 - accuracy: 0.9940 - val_loss: 0.0597 - val_accuracy: 0.9940 - 68ms/epoch - 4ms/step
Epoch 242/400
16/16 - 0s - loss: 0.0518 - accuracy: 0.9940 - val_loss: 0.0593 - val_accuracy: 0.9940 - 71ms/epoch - 4ms/step
Epoch 243/400
16/16 - 0s - loss: 0.0515 - accuracy: 0.9940 - val_loss: 0.0590 - val_accuracy: 0.9940 - 68ms/epoch - 4ms/step
Epoch 244/400
16/16 - 0s - loss: 0.0511 - accuracy: 0.9940 - val_loss: 0.0587 - val_accuracy: 0.9940 - 79ms/epoch - 5ms/step
Epoch 245/400
16/16 - 0s - loss: 0.0508 - accuracy: 0.9940 - val_loss: 0.0583 - val_accuracy: 0.9940 - 77ms/epoch - 5ms/step
Epoch 246/400
16/16 - 0s - loss: 0.0505 - accuracy: 0.9940 - val_loss: 0.0579 - val_accuracy: 0.9940 - 113ms/epoch - 7ms/step
Epoch 247/400
16/16 - 0s - loss: 0.0502 - accuracy: 0.9940 - val_loss: 0.0576 - val_accuracy: 0.9940 - 113ms/epoch - 7ms/step
Epoch 248/400
16/16 - 0s - loss: 0.0500 - accuracy: 0.9940 - val_loss: 0.0573 - val_accuracy: 0.9940 - 109ms/epoch - 7ms/step
Epoch 249/400
16/16 - 0s - loss: 0.0496 - accuracy: 0.9940 - val_loss: 0.0570 - val_accuracy: 0.9940 - 68ms/epoch - 4ms/step
Epoch 250/400
16/16 - 0s - loss: 0.0494 - accuracy: 0.9940 - val_loss: 0.0567 - val_accuracy: 0.9940 - 74ms/epoch - 5ms/step
Epoch 251/400
16/16 - 0s - loss: 0.0491 - accuracy: 0.9940 - val_loss: 0.0563 - val_accuracy: 0.9940 - 70ms/epoch - 4ms/step
Epoch 252/400
16/16 - 0s - loss: 0.0488 - accuracy: 0.9940 - val_loss: 0.0560 - val_accuracy: 0.9960 - 116ms/epoch - 7ms/step
Epoch 253/400
16/16 - 0s - loss: 0.0485 - accuracy: 0.9940 - val_loss: 0.0556 - val_accuracy: 0.9960 - 72ms/epoch - 4ms/step
Epoch 254/400
16/16 - 0s - loss: 0.0482 - accuracy: 0.9940 - val_loss: 0.0553 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 255/400
16/16 - 0s - loss: 0.0480 - accuracy: 0.9940 - val_loss: 0.0550 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 256/400
16/16 - 0s - loss: 0.0477 - accuracy: 0.9940 - val_loss: 0.0547 - val_accuracy: 0.9960 - 116ms/epoch - 7ms/step
Epoch 257/400
16/16 - 0s - loss: 0.0474 - accuracy: 0.9940 - val_loss: 0.0544 - val_accuracy: 0.9960 - 82ms/epoch - 5ms/step
Epoch 258/400
16/16 - 0s - loss: 0.0472 - accuracy: 0.9940 - val_loss: 0.0541 - val_accuracy: 0.9960 - 76ms/epoch - 5ms/step
Epoch 259/400
16/16 - 0s - loss: 0.0469 - accuracy: 0.9940 - val_loss: 0.0538 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 260/400
16/16 - 0s - loss: 0.0466 - accuracy: 0.9940 - val_loss: 0.0534 - val_accuracy: 0.9960 - 71ms/epoch - 4ms/step
Epoch 261/400
16/16 - 0s - loss: 0.0463 - accuracy: 0.9940 - val_loss: 0.0532 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 262/400
16/16 - 0s - loss: 0.0461 - accuracy: 0.9940 - val_loss: 0.0529 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 263/400
16/16 - 0s - loss: 0.0458 - accuracy: 0.9940 - val_loss: 0.0526 - val_accuracy: 0.9960 - 72ms/epoch - 4ms/step
Epoch 264/400
16/16 - 0s - loss: 0.0456 - accuracy: 0.9940 - val_loss: 0.0524 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 265/400
16/16 - 0s - loss: 0.0453 - accuracy: 0.9940 - val_loss: 0.0520 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 266/400
16/16 - 0s - loss: 0.0450 - accuracy: 0.9940 - val_loss: 0.0518 - val_accuracy: 0.9960 - 67ms/epoch - 4ms/step
Epoch 267/400
16/16 - 0s - loss: 0.0448 - accuracy: 0.9940 - val_loss: 0.0515 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 268/400
16/16 - 0s - loss: 0.0445 - accuracy: 0.9940 - val_loss: 0.0512 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 269/400
16/16 - 0s - loss: 0.0443 - accuracy: 0.9940 - val_loss: 0.0510 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 270/400
16/16 - 0s - loss: 0.0441 - accuracy: 0.9940 - val_loss: 0.0506 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 271/400
16/16 - 0s - loss: 0.0438 - accuracy: 0.9940 - val_loss: 0.0503 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 272/400
16/16 - 0s - loss: 0.0436 - accuracy: 0.9940 - val_loss: 0.0501 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 273/400
16/16 - 0s - loss: 0.0433 - accuracy: 0.9940 - val_loss: 0.0498 - val_accuracy: 0.9960 - 76ms/epoch - 5ms/step
Epoch 274/400
16/16 - 0s - loss: 0.0431 - accuracy: 0.9940 - val_loss: 0.0495 - val_accuracy: 0.9960 - 121ms/epoch - 8ms/step
Epoch 275/400
16/16 - 0s - loss: 0.0429 - accuracy: 0.9940 - val_loss: 0.0493 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 276/400
16/16 - 0s - loss: 0.0426 - accuracy: 0.9940 - val_loss: 0.0490 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 277/400
16/16 - 0s - loss: 0.0424 - accuracy: 0.9940 - val_loss: 0.0487 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 278/400
16/16 - 0s - loss: 0.0422 - accuracy: 0.9940 - val_loss: 0.0484 - val_accuracy: 0.9960 - 116ms/epoch - 7ms/step
Epoch 279/400
16/16 - 0s - loss: 0.0419 - accuracy: 0.9940 - val_loss: 0.0481 - val_accuracy: 0.9960 - 71ms/epoch - 4ms/step
Epoch 280/400
16/16 - 0s - loss: 0.0417 - accuracy: 0.9940 - val_loss: 0.0479 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 281/400
16/16 - 0s - loss: 0.0415 - accuracy: 0.9940 - val_loss: 0.0476 - val_accuracy: 0.9960 - 80ms/epoch - 5ms/step
Epoch 282/400
16/16 - 0s - loss: 0.0413 - accuracy: 0.9940 - val_loss: 0.0473 - val_accuracy: 0.9960 - 108ms/epoch - 7ms/step
Epoch 283/400
16/16 - 0s - loss: 0.0410 - accuracy: 0.9940 - val_loss: 0.0471 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 284/400
16/16 - 0s - loss: 0.0408 - accuracy: 0.9940 - val_loss: 0.0469 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 285/400
16/16 - 0s - loss: 0.0406 - accuracy: 0.9940 - val_loss: 0.0466 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 286/400
16/16 - 0s - loss: 0.0404 - accuracy: 0.9940 - val_loss: 0.0464 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 287/400
16/16 - 0s - loss: 0.0402 - accuracy: 0.9940 - val_loss: 0.0461 - val_accuracy: 0.9960 - 71ms/epoch - 4ms/step
Epoch 288/400
16/16 - 0s - loss: 0.0399 - accuracy: 0.9940 - val_loss: 0.0459 - val_accuracy: 0.9960 - 118ms/epoch - 7ms/step
Epoch 289/400
16/16 - 0s - loss: 0.0397 - accuracy: 0.9940 - val_loss: 0.0456 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 290/400
16/16 - 0s - loss: 0.0395 - accuracy: 0.9940 - val_loss: 0.0454 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 291/400
16/16 - 0s - loss: 0.0393 - accuracy: 0.9940 - val_loss: 0.0451 - val_accuracy: 0.9960 - 79ms/epoch - 5ms/step
Epoch 292/400
16/16 - 0s - loss: 0.0391 - accuracy: 0.9940 - val_loss: 0.0449 - val_accuracy: 0.9960 - 68ms/epoch - 4ms/step
Epoch 293/400
16/16 - 0s - loss: 0.0389 - accuracy: 0.9940 - val_loss: 0.0446 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 294/400
16/16 - 0s - loss: 0.0386 - accuracy: 0.9940 - val_loss: 0.0444 - val_accuracy: 0.9960 - 111ms/epoch - 7ms/step
Epoch 295/400
16/16 - 0s - loss: 0.0385 - accuracy: 0.9940 - val_loss: 0.0442 - val_accuracy: 0.9960 - 116ms/epoch - 7ms/step
Epoch 296/400
16/16 - 0s - loss: 0.0382 - accuracy: 0.9940 - val_loss: 0.0439 - val_accuracy: 0.9960 - 76ms/epoch - 5ms/step
Epoch 297/400
16/16 - 0s - loss: 0.0380 - accuracy: 0.9940 - val_loss: 0.0436 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 298/400
16/16 - 0s - loss: 0.0378 - accuracy: 0.9940 - val_loss: 0.0434 - val_accuracy: 0.9960 - 69ms/epoch - 4ms/step
Epoch 299/400
16/16 - 0s - loss: 0.0376 - accuracy: 0.9940 - val_loss: 0.0432 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 300/400
16/16 - 0s - loss: 0.0374 - accuracy: 0.9940 - val_loss: 0.0429 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 301/400
16/16 - 0s - loss: 0.0372 - accuracy: 0.9940 - val_loss: 0.0427 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 302/400
16/16 - 0s - loss: 0.0370 - accuracy: 0.9940 - val_loss: 0.0424 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 303/400
16/16 - 0s - loss: 0.0368 - accuracy: 0.9940 - val_loss: 0.0422 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 304/400
16/16 - 0s - loss: 0.0366 - accuracy: 0.9940 - val_loss: 0.0420 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 305/400
16/16 - 0s - loss: 0.0364 - accuracy: 0.9940 - val_loss: 0.0417 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 306/400
16/16 - 0s - loss: 0.0362 - accuracy: 0.9940 - val_loss: 0.0415 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 307/400
16/16 - 0s - loss: 0.0360 - accuracy: 0.9940 - val_loss: 0.0413 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 308/400
16/16 - 0s - loss: 0.0358 - accuracy: 0.9940 - val_loss: 0.0411 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 309/400
16/16 - 0s - loss: 0.0356 - accuracy: 0.9940 - val_loss: 0.0409 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 310/400
16/16 - 0s - loss: 0.0354 - accuracy: 0.9940 - val_loss: 0.0406 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 311/400
16/16 - 0s - loss: 0.0352 - accuracy: 0.9940 - val_loss: 0.0403 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 312/400
16/16 - 0s - loss: 0.0350 - accuracy: 0.9940 - val_loss: 0.0401 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 313/400
16/16 - 0s - loss: 0.0348 - accuracy: 0.9940 - val_loss: 0.0399 - val_accuracy: 0.9980 - 120ms/epoch - 8ms/step
Epoch 314/400
16/16 - 0s - loss: 0.0346 - accuracy: 0.9940 - val_loss: 0.0397 - val_accuracy: 0.9980 - 80ms/epoch - 5ms/step
Epoch 315/400
16/16 - 0s - loss: 0.0344 - accuracy: 0.9940 - val_loss: 0.0394 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 316/400
16/16 - 0s - loss: 0.0342 - accuracy: 0.9940 - val_loss: 0.0392 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 317/400
16/16 - 0s - loss: 0.0340 - accuracy: 0.9940 - val_loss: 0.0390 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 318/400
16/16 - 0s - loss: 0.0338 - accuracy: 0.9940 - val_loss: 0.0387 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 319/400
16/16 - 0s - loss: 0.0336 - accuracy: 0.9940 - val_loss: 0.0386 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 320/400
16/16 - 0s - loss: 0.0334 - accuracy: 0.9940 - val_loss: 0.0383 - val_accuracy: 0.9980 - 69ms/epoch - 4ms/step
Epoch 321/400
16/16 - 0s - loss: 0.0332 - accuracy: 0.9940 - val_loss: 0.0381 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 322/400
16/16 - 0s - loss: 0.0330 - accuracy: 0.9940 - val_loss: 0.0379 - val_accuracy: 0.9980 - 124ms/epoch - 8ms/step
Epoch 323/400
16/16 - 0s - loss: 0.0328 - accuracy: 0.9940 - val_loss: 0.0376 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 324/400
16/16 - 0s - loss: 0.0326 - accuracy: 0.9940 - val_loss: 0.0374 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 325/400
16/16 - 0s - loss: 0.0324 - accuracy: 0.9940 - val_loss: 0.0372 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 326/400
16/16 - 0s - loss: 0.0322 - accuracy: 0.9940 - val_loss: 0.0370 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 327/400
16/16 - 0s - loss: 0.0320 - accuracy: 0.9940 - val_loss: 0.0368 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 328/400
16/16 - 0s - loss: 0.0319 - accuracy: 0.9940 - val_loss: 0.0366 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 329/400
16/16 - 0s - loss: 0.0317 - accuracy: 0.9940 - val_loss: 0.0364 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 330/400
16/16 - 0s - loss: 0.0315 - accuracy: 0.9940 - val_loss: 0.0361 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 331/400
16/16 - 0s - loss: 0.0313 - accuracy: 0.9940 - val_loss: 0.0359 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 332/400
16/16 - 0s - loss: 0.0311 - accuracy: 0.9940 - val_loss: 0.0357 - val_accuracy: 0.9980 - 120ms/epoch - 7ms/step
Epoch 333/400
16/16 - 0s - loss: 0.0309 - accuracy: 0.9960 - val_loss: 0.0355 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 334/400
16/16 - 0s - loss: 0.0307 - accuracy: 0.9960 - val_loss: 0.0353 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 335/400
16/16 - 0s - loss: 0.0305 - accuracy: 0.9960 - val_loss: 0.0351 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 336/400
16/16 - 0s - loss: 0.0304 - accuracy: 0.9960 - val_loss: 0.0349 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 337/400
16/16 - 0s - loss: 0.0302 - accuracy: 0.9960 - val_loss: 0.0347 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 338/400
16/16 - 0s - loss: 0.0300 - accuracy: 0.9960 - val_loss: 0.0345 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 339/400
16/16 - 0s - loss: 0.0298 - accuracy: 0.9960 - val_loss: 0.0343 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 340/400
16/16 - 0s - loss: 0.0296 - accuracy: 0.9960 - val_loss: 0.0340 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 341/400
16/16 - 0s - loss: 0.0295 - accuracy: 0.9960 - val_loss: 0.0338 - val_accuracy: 0.9980 - 71ms/epoch - 4ms/step
Epoch 342/400
16/16 - 0s - loss: 0.0293 - accuracy: 0.9960 - val_loss: 0.0336 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 343/400
16/16 - 0s - loss: 0.0291 - accuracy: 0.9960 - val_loss: 0.0334 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 344/400
16/16 - 0s - loss: 0.0289 - accuracy: 0.9960 - val_loss: 0.0332 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 345/400
16/16 - 0s - loss: 0.0288 - accuracy: 0.9960 - val_loss: 0.0330 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 346/400
16/16 - 0s - loss: 0.0286 - accuracy: 0.9960 - val_loss: 0.0328 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 347/400
16/16 - 0s - loss: 0.0284 - accuracy: 0.9960 - val_loss: 0.0326 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 348/400
16/16 - 0s - loss: 0.0283 - accuracy: 0.9960 - val_loss: 0.0324 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 349/400
16/16 - 0s - loss: 0.0281 - accuracy: 0.9960 - val_loss: 0.0322 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 350/400
16/16 - 0s - loss: 0.0279 - accuracy: 0.9960 - val_loss: 0.0321 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 351/400
16/16 - 0s - loss: 0.0278 - accuracy: 0.9960 - val_loss: 0.0319 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 352/400
16/16 - 0s - loss: 0.0276 - accuracy: 0.9960 - val_loss: 0.0317 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 353/400
16/16 - 0s - loss: 0.0274 - accuracy: 0.9960 - val_loss: 0.0315 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 354/400
16/16 - 0s - loss: 0.0273 - accuracy: 0.9960 - val_loss: 0.0314 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 355/400
16/16 - 0s - loss: 0.0271 - accuracy: 0.9960 - val_loss: 0.0312 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 356/400
16/16 - 0s - loss: 0.0269 - accuracy: 0.9960 - val_loss: 0.0310 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 357/400
16/16 - 0s - loss: 0.0268 - accuracy: 0.9960 - val_loss: 0.0308 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 358/400
16/16 - 0s - loss: 0.0266 - accuracy: 0.9960 - val_loss: 0.0307 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 359/400
16/16 - 0s - loss: 0.0265 - accuracy: 0.9960 - val_loss: 0.0305 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 360/400
16/16 - 0s - loss: 0.0263 - accuracy: 0.9960 - val_loss: 0.0303 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 361/400
16/16 - 0s - loss: 0.0262 - accuracy: 0.9960 - val_loss: 0.0302 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 362/400
16/16 - 0s - loss: 0.0260 - accuracy: 0.9960 - val_loss: 0.0300 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 363/400
16/16 - 0s - loss: 0.0259 - accuracy: 0.9960 - val_loss: 0.0298 - val_accuracy: 1.0000 - 73ms/epoch - 5ms/step
Epoch 364/400
16/16 - 0s - loss: 0.0257 - accuracy: 0.9960 - val_loss: 0.0296 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 365/400
16/16 - 0s - loss: 0.0256 - accuracy: 0.9980 - val_loss: 0.0295 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 366/400
16/16 - 0s - loss: 0.0254 - accuracy: 0.9980 - val_loss: 0.0293 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 367/400
16/16 - 0s - loss: 0.0253 - accuracy: 0.9980 - val_loss: 0.0291 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 368/400
16/16 - 0s - loss: 0.0251 - accuracy: 0.9980 - val_loss: 0.0290 - val_accuracy: 1.0000 - 122ms/epoch - 8ms/step
Epoch 369/400
16/16 - 0s - loss: 0.0250 - accuracy: 0.9980 - val_loss: 0.0288 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 370/400
16/16 - 0s - loss: 0.0248 - accuracy: 0.9980 - val_loss: 0.0286 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 371/400
16/16 - 0s - loss: 0.0247 - accuracy: 0.9980 - val_loss: 0.0285 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 372/400
16/16 - 0s - loss: 0.0245 - accuracy: 0.9980 - val_loss: 0.0283 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 373/400
16/16 - 0s - loss: 0.0244 - accuracy: 0.9980 - val_loss: 0.0282 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 374/400
16/16 - 0s - loss: 0.0243 - accuracy: 0.9980 - val_loss: 0.0280 - val_accuracy: 1.0000 - 67ms/epoch - 4ms/step
Epoch 375/400
16/16 - 0s - loss: 0.0241 - accuracy: 0.9980 - val_loss: 0.0279 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 376/400
16/16 - 0s - loss: 0.0240 - accuracy: 0.9980 - val_loss: 0.0277 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 377/400
16/16 - 0s - loss: 0.0238 - accuracy: 0.9980 - val_loss: 0.0276 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 378/400
16/16 - 0s - loss: 0.0237 - accuracy: 0.9980 - val_loss: 0.0275 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 379/400
16/16 - 0s - loss: 0.0236 - accuracy: 0.9980 - val_loss: 0.0273 - val_accuracy: 1.0000 - 89ms/epoch - 6ms/step
Epoch 380/400
16/16 - 0s - loss: 0.0234 - accuracy: 0.9980 - val_loss: 0.0272 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 381/400
16/16 - 0s - loss: 0.0233 - accuracy: 0.9980 - val_loss: 0.0271 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 382/400
16/16 - 0s - loss: 0.0232 - accuracy: 0.9980 - val_loss: 0.0270 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 383/400
16/16 - 0s - loss: 0.0230 - accuracy: 0.9980 - val_loss: 0.0268 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 384/400
16/16 - 0s - loss: 0.0229 - accuracy: 0.9980 - val_loss: 0.0267 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 385/400
16/16 - 0s - loss: 0.0227 - accuracy: 0.9980 - val_loss: 0.0266 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 386/400
16/16 - 0s - loss: 0.0226 - accuracy: 0.9980 - val_loss: 0.0264 - val_accuracy: 1.0000 - 68ms/epoch - 4ms/step
Epoch 387/400
16/16 - 0s - loss: 0.0225 - accuracy: 0.9980 - val_loss: 0.0263 - val_accuracy: 1.0000 - 73ms/epoch - 5ms/step
Epoch 388/400
16/16 - 0s - loss: 0.0223 - accuracy: 0.9980 - val_loss: 0.0262 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 389/400
16/16 - 0s - loss: 0.0222 - accuracy: 0.9980 - val_loss: 0.0261 - val_accuracy: 1.0000 - 91ms/epoch - 6ms/step
Epoch 390/400
16/16 - 0s - loss: 0.0221 - accuracy: 0.9980 - val_loss: 0.0260 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 391/400
16/16 - 0s - loss: 0.0220 - accuracy: 0.9980 - val_loss: 0.0259 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 392/400
16/16 - 0s - loss: 0.0218 - accuracy: 0.9980 - val_loss: 0.0257 - val_accuracy: 1.0000 - 73ms/epoch - 5ms/step
Epoch 393/400
16/16 - 0s - loss: 0.0217 - accuracy: 0.9980 - val_loss: 0.0256 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 394/400
16/16 - 0s - loss: 0.0216 - accuracy: 0.9980 - val_loss: 0.0255 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 395/400
16/16 - 0s - loss: 0.0215 - accuracy: 0.9980 - val_loss: 0.0253 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 396/400
16/16 - 0s - loss: 0.0213 - accuracy: 0.9980 - val_loss: 0.0252 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 397/400
16/16 - 0s - loss: 0.0212 - accuracy: 0.9980 - val_loss: 0.0251 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 398/400
16/16 - 0s - loss: 0.0211 - accuracy: 0.9980 - val_loss: 0.0250 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 399/400
16/16 - 0s - loss: 0.0210 - accuracy: 0.9980 - val_loss: 0.0248 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 400/400
16/16 - 0s - loss: 0.0209 - accuracy: 0.9980 - val_loss: 0.0247 - val_accuracy: 1.0000 - 69ms/epoch - 4ms/step
第3个弱分类器训练完毕
Epoch 1/400
16/16 - 1s - loss: 0.3360 - accuracy: 0.8420 - val_loss: 0.3349 - val_accuracy: 0.8280 - 880ms/epoch - 55ms/step
Epoch 2/400
16/16 - 0s - loss: 0.3268 - accuracy: 0.8480 - val_loss: 0.3262 - val_accuracy: 0.8340 - 69ms/epoch - 4ms/step
Epoch 3/400
16/16 - 0s - loss: 0.3183 - accuracy: 0.8520 - val_loss: 0.3177 - val_accuracy: 0.8400 - 74ms/epoch - 5ms/step
Epoch 4/400
16/16 - 0s - loss: 0.3096 - accuracy: 0.8580 - val_loss: 0.3102 - val_accuracy: 0.8440 - 110ms/epoch - 7ms/step
Epoch 5/400
16/16 - 0s - loss: 0.3022 - accuracy: 0.8620 - val_loss: 0.3027 - val_accuracy: 0.8540 - 68ms/epoch - 4ms/step
Epoch 6/400
16/16 - 0s - loss: 0.2944 - accuracy: 0.8680 - val_loss: 0.2959 - val_accuracy: 0.8560 - 109ms/epoch - 7ms/step
Epoch 7/400
16/16 - 0s - loss: 0.2875 - accuracy: 0.8720 - val_loss: 0.2895 - val_accuracy: 0.8580 - 75ms/epoch - 5ms/step
Epoch 8/400
16/16 - 0s - loss: 0.2809 - accuracy: 0.8780 - val_loss: 0.2832 - val_accuracy: 0.8660 - 72ms/epoch - 5ms/step
Epoch 9/400
16/16 - 0s - loss: 0.2744 - accuracy: 0.8900 - val_loss: 0.2776 - val_accuracy: 0.8760 - 117ms/epoch - 7ms/step
Epoch 10/400
16/16 - 0s - loss: 0.2687 - accuracy: 0.8960 - val_loss: 0.2719 - val_accuracy: 0.8820 - 72ms/epoch - 5ms/step
Epoch 11/400
16/16 - 0s - loss: 0.2631 - accuracy: 0.9000 - val_loss: 0.2668 - val_accuracy: 0.8920 - 70ms/epoch - 4ms/step
Epoch 12/400
16/16 - 0s - loss: 0.2577 - accuracy: 0.9040 - val_loss: 0.2621 - val_accuracy: 0.9000 - 122ms/epoch - 8ms/step
Epoch 13/400
16/16 - 0s - loss: 0.2528 - accuracy: 0.9100 - val_loss: 0.2577 - val_accuracy: 0.9060 - 109ms/epoch - 7ms/step
Epoch 14/400
16/16 - 0s - loss: 0.2481 - accuracy: 0.9140 - val_loss: 0.2536 - val_accuracy: 0.9100 - 114ms/epoch - 7ms/step
Epoch 15/400
16/16 - 0s - loss: 0.2440 - accuracy: 0.9140 - val_loss: 0.2495 - val_accuracy: 0.9160 - 110ms/epoch - 7ms/step
Epoch 16/400
16/16 - 0s - loss: 0.2397 - accuracy: 0.9180 - val_loss: 0.2459 - val_accuracy: 0.9180 - 69ms/epoch - 4ms/step
Epoch 17/400
16/16 - 0s - loss: 0.2361 - accuracy: 0.9200 - val_loss: 0.2423 - val_accuracy: 0.9260 - 68ms/epoch - 4ms/step
Epoch 18/400
16/16 - 0s - loss: 0.2323 - accuracy: 0.9320 - val_loss: 0.2391 - val_accuracy: 0.9260 - 73ms/epoch - 5ms/step
Epoch 19/400
16/16 - 0s - loss: 0.2289 - accuracy: 0.9360 - val_loss: 0.2362 - val_accuracy: 0.9260 - 115ms/epoch - 7ms/step
Epoch 20/400
16/16 - 0s - loss: 0.2257 - accuracy: 0.9380 - val_loss: 0.2334 - val_accuracy: 0.9300 - 75ms/epoch - 5ms/step
Epoch 21/400
16/16 - 0s - loss: 0.2227 - accuracy: 0.9440 - val_loss: 0.2307 - val_accuracy: 0.9320 - 74ms/epoch - 5ms/step
Epoch 22/400
16/16 - 0s - loss: 0.2198 - accuracy: 0.9440 - val_loss: 0.2281 - val_accuracy: 0.9380 - 75ms/epoch - 5ms/step
Epoch 23/400
16/16 - 0s - loss: 0.2170 - accuracy: 0.9500 - val_loss: 0.2258 - val_accuracy: 0.9380 - 87ms/epoch - 5ms/step
Epoch 24/400
16/16 - 0s - loss: 0.2145 - accuracy: 0.9500 - val_loss: 0.2236 - val_accuracy: 0.9380 - 114ms/epoch - 7ms/step
Epoch 25/400
16/16 - 0s - loss: 0.2121 - accuracy: 0.9520 - val_loss: 0.2215 - val_accuracy: 0.9400 - 74ms/epoch - 5ms/step
Epoch 26/400
16/16 - 0s - loss: 0.2097 - accuracy: 0.9520 - val_loss: 0.2197 - val_accuracy: 0.9420 - 121ms/epoch - 8ms/step
Epoch 27/400
16/16 - 0s - loss: 0.2077 - accuracy: 0.9520 - val_loss: 0.2176 - val_accuracy: 0.9420 - 75ms/epoch - 5ms/step
Epoch 28/400
16/16 - 0s - loss: 0.2055 - accuracy: 0.9540 - val_loss: 0.2158 - val_accuracy: 0.9440 - 115ms/epoch - 7ms/step
Epoch 29/400
16/16 - 0s - loss: 0.2035 - accuracy: 0.9540 - val_loss: 0.2141 - val_accuracy: 0.9480 - 112ms/epoch - 7ms/step
Epoch 30/400
16/16 - 0s - loss: 0.2016 - accuracy: 0.9560 - val_loss: 0.2125 - val_accuracy: 0.9480 - 117ms/epoch - 7ms/step
Epoch 31/400
16/16 - 0s - loss: 0.1997 - accuracy: 0.9600 - val_loss: 0.2109 - val_accuracy: 0.9480 - 118ms/epoch - 7ms/step
Epoch 32/400
16/16 - 0s - loss: 0.1979 - accuracy: 0.9600 - val_loss: 0.2094 - val_accuracy: 0.9500 - 77ms/epoch - 5ms/step
Epoch 33/400
16/16 - 0s - loss: 0.1962 - accuracy: 0.9640 - val_loss: 0.2080 - val_accuracy: 0.9500 - 80ms/epoch - 5ms/step
Epoch 34/400
16/16 - 0s - loss: 0.1945 - accuracy: 0.9640 - val_loss: 0.2066 - val_accuracy: 0.9560 - 74ms/epoch - 5ms/step
Epoch 35/400
16/16 - 0s - loss: 0.1930 - accuracy: 0.9660 - val_loss: 0.2051 - val_accuracy: 0.9560 - 116ms/epoch - 7ms/step
Epoch 36/400
16/16 - 0s - loss: 0.1913 - accuracy: 0.9680 - val_loss: 0.2037 - val_accuracy: 0.9620 - 110ms/epoch - 7ms/step
Epoch 37/400
16/16 - 0s - loss: 0.1897 - accuracy: 0.9700 - val_loss: 0.2024 - val_accuracy: 0.9660 - 119ms/epoch - 7ms/step
Epoch 38/400
16/16 - 0s - loss: 0.1881 - accuracy: 0.9720 - val_loss: 0.2011 - val_accuracy: 0.9660 - 115ms/epoch - 7ms/step
Epoch 39/400
16/16 - 0s - loss: 0.1866 - accuracy: 0.9720 - val_loss: 0.1998 - val_accuracy: 0.9680 - 116ms/epoch - 7ms/step
Epoch 40/400
16/16 - 0s - loss: 0.1851 - accuracy: 0.9720 - val_loss: 0.1986 - val_accuracy: 0.9680 - 116ms/epoch - 7ms/step
Epoch 41/400
16/16 - 0s - loss: 0.1837 - accuracy: 0.9720 - val_loss: 0.1973 - val_accuracy: 0.9700 - 68ms/epoch - 4ms/step
Epoch 42/400
16/16 - 0s - loss: 0.1822 - accuracy: 0.9720 - val_loss: 0.1961 - val_accuracy: 0.9700 - 118ms/epoch - 7ms/step
Epoch 43/400
16/16 - 0s - loss: 0.1809 - accuracy: 0.9720 - val_loss: 0.1949 - val_accuracy: 0.9700 - 114ms/epoch - 7ms/step
Epoch 44/400
16/16 - 0s - loss: 0.1794 - accuracy: 0.9740 - val_loss: 0.1936 - val_accuracy: 0.9700 - 71ms/epoch - 4ms/step
Epoch 45/400
16/16 - 0s - loss: 0.1781 - accuracy: 0.9760 - val_loss: 0.1924 - val_accuracy: 0.9720 - 80ms/epoch - 5ms/step
Epoch 46/400
16/16 - 0s - loss: 0.1767 - accuracy: 0.9760 - val_loss: 0.1912 - val_accuracy: 0.9740 - 71ms/epoch - 4ms/step
Epoch 47/400
16/16 - 0s - loss: 0.1753 - accuracy: 0.9760 - val_loss: 0.1900 - val_accuracy: 0.9740 - 74ms/epoch - 5ms/step
Epoch 48/400
16/16 - 0s - loss: 0.1740 - accuracy: 0.9740 - val_loss: 0.1888 - val_accuracy: 0.9740 - 116ms/epoch - 7ms/step
Epoch 49/400
16/16 - 0s - loss: 0.1726 - accuracy: 0.9760 - val_loss: 0.1876 - val_accuracy: 0.9740 - 117ms/epoch - 7ms/step
Epoch 50/400
16/16 - 0s - loss: 0.1713 - accuracy: 0.9760 - val_loss: 0.1865 - val_accuracy: 0.9740 - 117ms/epoch - 7ms/step
Epoch 51/400
16/16 - 0s - loss: 0.1700 - accuracy: 0.9780 - val_loss: 0.1853 - val_accuracy: 0.9740 - 76ms/epoch - 5ms/step
Epoch 52/400
16/16 - 0s - loss: 0.1687 - accuracy: 0.9780 - val_loss: 0.1841 - val_accuracy: 0.9740 - 80ms/epoch - 5ms/step
Epoch 53/400
16/16 - 0s - loss: 0.1673 - accuracy: 0.9780 - val_loss: 0.1830 - val_accuracy: 0.9740 - 81ms/epoch - 5ms/step
Epoch 54/400
16/16 - 0s - loss: 0.1660 - accuracy: 0.9800 - val_loss: 0.1818 - val_accuracy: 0.9740 - 84ms/epoch - 5ms/step
Epoch 55/400
16/16 - 0s - loss: 0.1647 - accuracy: 0.9800 - val_loss: 0.1807 - val_accuracy: 0.9760 - 117ms/epoch - 7ms/step
Epoch 56/400
16/16 - 0s - loss: 0.1635 - accuracy: 0.9820 - val_loss: 0.1795 - val_accuracy: 0.9760 - 109ms/epoch - 7ms/step
Epoch 57/400
16/16 - 0s - loss: 0.1622 - accuracy: 0.9800 - val_loss: 0.1784 - val_accuracy: 0.9760 - 112ms/epoch - 7ms/step
Epoch 58/400
16/16 - 0s - loss: 0.1609 - accuracy: 0.9840 - val_loss: 0.1772 - val_accuracy: 0.9760 - 77ms/epoch - 5ms/step
Epoch 59/400
16/16 - 0s - loss: 0.1596 - accuracy: 0.9840 - val_loss: 0.1760 - val_accuracy: 0.9760 - 110ms/epoch - 7ms/step
Epoch 60/400
16/16 - 0s - loss: 0.1584 - accuracy: 0.9840 - val_loss: 0.1749 - val_accuracy: 0.9760 - 81ms/epoch - 5ms/step
Epoch 61/400
16/16 - 0s - loss: 0.1571 - accuracy: 0.9840 - val_loss: 0.1737 - val_accuracy: 0.9760 - 75ms/epoch - 5ms/step
Epoch 62/400
16/16 - 0s - loss: 0.1558 - accuracy: 0.9840 - val_loss: 0.1726 - val_accuracy: 0.9760 - 70ms/epoch - 4ms/step
Epoch 63/400
16/16 - 0s - loss: 0.1546 - accuracy: 0.9840 - val_loss: 0.1714 - val_accuracy: 0.9760 - 76ms/epoch - 5ms/step
Epoch 64/400
16/16 - 0s - loss: 0.1533 - accuracy: 0.9840 - val_loss: 0.1703 - val_accuracy: 0.9760 - 74ms/epoch - 5ms/step
Epoch 65/400
16/16 - 0s - loss: 0.1521 - accuracy: 0.9840 - val_loss: 0.1691 - val_accuracy: 0.9760 - 74ms/epoch - 5ms/step
Epoch 66/400
16/16 - 0s - loss: 0.1509 - accuracy: 0.9840 - val_loss: 0.1679 - val_accuracy: 0.9760 - 112ms/epoch - 7ms/step
Epoch 67/400
16/16 - 0s - loss: 0.1497 - accuracy: 0.9840 - val_loss: 0.1668 - val_accuracy: 0.9760 - 75ms/epoch - 5ms/step
Epoch 68/400
16/16 - 0s - loss: 0.1485 - accuracy: 0.9840 - val_loss: 0.1656 - val_accuracy: 0.9760 - 81ms/epoch - 5ms/step
Epoch 69/400
16/16 - 0s - loss: 0.1472 - accuracy: 0.9840 - val_loss: 0.1644 - val_accuracy: 0.9760 - 110ms/epoch - 7ms/step
Epoch 70/400
16/16 - 0s - loss: 0.1460 - accuracy: 0.9840 - val_loss: 0.1633 - val_accuracy: 0.9760 - 109ms/epoch - 7ms/step
Epoch 71/400
16/16 - 0s - loss: 0.1448 - accuracy: 0.9840 - val_loss: 0.1621 - val_accuracy: 0.9760 - 113ms/epoch - 7ms/step
Epoch 72/400
16/16 - 0s - loss: 0.1436 - accuracy: 0.9840 - val_loss: 0.1610 - val_accuracy: 0.9760 - 75ms/epoch - 5ms/step
Epoch 73/400
16/16 - 0s - loss: 0.1424 - accuracy: 0.9840 - val_loss: 0.1598 - val_accuracy: 0.9760 - 73ms/epoch - 5ms/step
Epoch 74/400
16/16 - 0s - loss: 0.1413 - accuracy: 0.9840 - val_loss: 0.1587 - val_accuracy: 0.9760 - 120ms/epoch - 7ms/step
Epoch 75/400
16/16 - 0s - loss: 0.1401 - accuracy: 0.9840 - val_loss: 0.1576 - val_accuracy: 0.9760 - 111ms/epoch - 7ms/step
Epoch 76/400
16/16 - 0s - loss: 0.1389 - accuracy: 0.9840 - val_loss: 0.1564 - val_accuracy: 0.9760 - 76ms/epoch - 5ms/step
Epoch 77/400
16/16 - 0s - loss: 0.1377 - accuracy: 0.9840 - val_loss: 0.1553 - val_accuracy: 0.9760 - 74ms/epoch - 5ms/step
Epoch 78/400
16/16 - 0s - loss: 0.1366 - accuracy: 0.9840 - val_loss: 0.1542 - val_accuracy: 0.9760 - 72ms/epoch - 4ms/step
Epoch 79/400
16/16 - 0s - loss: 0.1354 - accuracy: 0.9840 - val_loss: 0.1530 - val_accuracy: 0.9760 - 79ms/epoch - 5ms/step
Epoch 80/400
16/16 - 0s - loss: 0.1343 - accuracy: 0.9840 - val_loss: 0.1519 - val_accuracy: 0.9760 - 113ms/epoch - 7ms/step
Epoch 81/400
16/16 - 0s - loss: 0.1332 - accuracy: 0.9840 - val_loss: 0.1508 - val_accuracy: 0.9760 - 71ms/epoch - 4ms/step
Epoch 82/400
16/16 - 0s - loss: 0.1320 - accuracy: 0.9840 - val_loss: 0.1497 - val_accuracy: 0.9760 - 116ms/epoch - 7ms/step
Epoch 83/400
16/16 - 0s - loss: 0.1309 - accuracy: 0.9840 - val_loss: 0.1486 - val_accuracy: 0.9760 - 76ms/epoch - 5ms/step
Epoch 84/400
16/16 - 0s - loss: 0.1298 - accuracy: 0.9840 - val_loss: 0.1475 - val_accuracy: 0.9780 - 115ms/epoch - 7ms/step
Epoch 85/400
16/16 - 0s - loss: 0.1287 - accuracy: 0.9840 - val_loss: 0.1463 - val_accuracy: 0.9780 - 76ms/epoch - 5ms/step
Epoch 86/400
16/16 - 0s - loss: 0.1276 - accuracy: 0.9840 - val_loss: 0.1453 - val_accuracy: 0.9780 - 80ms/epoch - 5ms/step
Epoch 87/400
16/16 - 0s - loss: 0.1265 - accuracy: 0.9840 - val_loss: 0.1442 - val_accuracy: 0.9780 - 78ms/epoch - 5ms/step
Epoch 88/400
16/16 - 0s - loss: 0.1254 - accuracy: 0.9840 - val_loss: 0.1431 - val_accuracy: 0.9780 - 90ms/epoch - 6ms/step
Epoch 89/400
16/16 - 0s - loss: 0.1243 - accuracy: 0.9840 - val_loss: 0.1420 - val_accuracy: 0.9780 - 77ms/epoch - 5ms/step
Epoch 90/400
16/16 - 0s - loss: 0.1233 - accuracy: 0.9840 - val_loss: 0.1409 - val_accuracy: 0.9780 - 90ms/epoch - 6ms/step
Epoch 91/400
16/16 - 0s - loss: 0.1222 - accuracy: 0.9840 - val_loss: 0.1398 - val_accuracy: 0.9780 - 112ms/epoch - 7ms/step
Epoch 92/400
16/16 - 0s - loss: 0.1211 - accuracy: 0.9840 - val_loss: 0.1388 - val_accuracy: 0.9780 - 111ms/epoch - 7ms/step
Epoch 93/400
16/16 - 0s - loss: 0.1201 - accuracy: 0.9840 - val_loss: 0.1377 - val_accuracy: 0.9780 - 74ms/epoch - 5ms/step
Epoch 94/400
16/16 - 0s - loss: 0.1190 - accuracy: 0.9860 - val_loss: 0.1366 - val_accuracy: 0.9780 - 75ms/epoch - 5ms/step
Epoch 95/400
16/16 - 0s - loss: 0.1180 - accuracy: 0.9860 - val_loss: 0.1356 - val_accuracy: 0.9780 - 113ms/epoch - 7ms/step
Epoch 96/400
16/16 - 0s - loss: 0.1170 - accuracy: 0.9860 - val_loss: 0.1345 - val_accuracy: 0.9780 - 74ms/epoch - 5ms/step
Epoch 97/400
16/16 - 0s - loss: 0.1160 - accuracy: 0.9880 - val_loss: 0.1335 - val_accuracy: 0.9780 - 112ms/epoch - 7ms/step
Epoch 98/400
16/16 - 0s - loss: 0.1149 - accuracy: 0.9880 - val_loss: 0.1324 - val_accuracy: 0.9800 - 114ms/epoch - 7ms/step
Epoch 99/400
16/16 - 0s - loss: 0.1140 - accuracy: 0.9880 - val_loss: 0.1313 - val_accuracy: 0.9800 - 113ms/epoch - 7ms/step
Epoch 100/400
16/16 - 0s - loss: 0.1129 - accuracy: 0.9880 - val_loss: 0.1303 - val_accuracy: 0.9800 - 74ms/epoch - 5ms/step
Epoch 101/400
16/16 - 0s - loss: 0.1120 - accuracy: 0.9880 - val_loss: 0.1293 - val_accuracy: 0.9800 - 111ms/epoch - 7ms/step
Epoch 102/400
16/16 - 0s - loss: 0.1110 - accuracy: 0.9900 - val_loss: 0.1283 - val_accuracy: 0.9800 - 70ms/epoch - 4ms/step
Epoch 103/400
16/16 - 0s - loss: 0.1100 - accuracy: 0.9900 - val_loss: 0.1272 - val_accuracy: 0.9800 - 110ms/epoch - 7ms/step
Epoch 104/400
16/16 - 0s - loss: 0.1090 - accuracy: 0.9900 - val_loss: 0.1263 - val_accuracy: 0.9800 - 116ms/epoch - 7ms/step
Epoch 105/400
16/16 - 0s - loss: 0.1081 - accuracy: 0.9900 - val_loss: 0.1252 - val_accuracy: 0.9800 - 80ms/epoch - 5ms/step
Epoch 106/400
16/16 - 0s - loss: 0.1071 - accuracy: 0.9920 - val_loss: 0.1243 - val_accuracy: 0.9820 - 110ms/epoch - 7ms/step
Epoch 107/400
16/16 - 0s - loss: 0.1062 - accuracy: 0.9920 - val_loss: 0.1233 - val_accuracy: 0.9840 - 78ms/epoch - 5ms/step
Epoch 108/400
16/16 - 0s - loss: 0.1053 - accuracy: 0.9940 - val_loss: 0.1224 - val_accuracy: 0.9840 - 80ms/epoch - 5ms/step
Epoch 109/400
16/16 - 0s - loss: 0.1044 - accuracy: 0.9940 - val_loss: 0.1214 - val_accuracy: 0.9840 - 68ms/epoch - 4ms/step
Epoch 110/400
16/16 - 0s - loss: 0.1035 - accuracy: 0.9940 - val_loss: 0.1204 - val_accuracy: 0.9840 - 112ms/epoch - 7ms/step
Epoch 111/400
16/16 - 0s - loss: 0.1026 - accuracy: 0.9940 - val_loss: 0.1195 - val_accuracy: 0.9840 - 77ms/epoch - 5ms/step
Epoch 112/400
16/16 - 0s - loss: 0.1017 - accuracy: 0.9940 - val_loss: 0.1186 - val_accuracy: 0.9840 - 113ms/epoch - 7ms/step
Epoch 113/400
16/16 - 0s - loss: 0.1008 - accuracy: 0.9940 - val_loss: 0.1176 - val_accuracy: 0.9840 - 73ms/epoch - 5ms/step
Epoch 114/400
16/16 - 0s - loss: 0.1000 - accuracy: 0.9940 - val_loss: 0.1168 - val_accuracy: 0.9840 - 74ms/epoch - 5ms/step
Epoch 115/400
16/16 - 0s - loss: 0.0991 - accuracy: 0.9940 - val_loss: 0.1158 - val_accuracy: 0.9860 - 74ms/epoch - 5ms/step
Epoch 116/400
16/16 - 0s - loss: 0.0983 - accuracy: 0.9940 - val_loss: 0.1149 - val_accuracy: 0.9860 - 79ms/epoch - 5ms/step
Epoch 117/400
16/16 - 0s - loss: 0.0975 - accuracy: 0.9940 - val_loss: 0.1141 - val_accuracy: 0.9860 - 80ms/epoch - 5ms/step
Epoch 118/400
16/16 - 0s - loss: 0.0967 - accuracy: 0.9940 - val_loss: 0.1132 - val_accuracy: 0.9860 - 74ms/epoch - 5ms/step
Epoch 119/400
16/16 - 0s - loss: 0.0958 - accuracy: 0.9940 - val_loss: 0.1124 - val_accuracy: 0.9860 - 119ms/epoch - 7ms/step
Epoch 120/400
16/16 - 0s - loss: 0.0951 - accuracy: 0.9940 - val_loss: 0.1114 - val_accuracy: 0.9860 - 113ms/epoch - 7ms/step
Epoch 121/400
16/16 - 0s - loss: 0.0942 - accuracy: 0.9940 - val_loss: 0.1107 - val_accuracy: 0.9860 - 116ms/epoch - 7ms/step
Epoch 122/400
16/16 - 0s - loss: 0.0935 - accuracy: 0.9940 - val_loss: 0.1098 - val_accuracy: 0.9860 - 74ms/epoch - 5ms/step
Epoch 123/400
16/16 - 0s - loss: 0.0927 - accuracy: 0.9940 - val_loss: 0.1090 - val_accuracy: 0.9860 - 109ms/epoch - 7ms/step
Epoch 124/400
16/16 - 0s - loss: 0.0919 - accuracy: 0.9940 - val_loss: 0.1081 - val_accuracy: 0.9860 - 115ms/epoch - 7ms/step
Epoch 125/400
16/16 - 0s - loss: 0.0912 - accuracy: 0.9940 - val_loss: 0.1073 - val_accuracy: 0.9860 - 71ms/epoch - 4ms/step
Epoch 126/400
16/16 - 0s - loss: 0.0904 - accuracy: 0.9940 - val_loss: 0.1065 - val_accuracy: 0.9860 - 82ms/epoch - 5ms/step
Epoch 127/400
16/16 - 0s - loss: 0.0897 - accuracy: 0.9940 - val_loss: 0.1057 - val_accuracy: 0.9860 - 110ms/epoch - 7ms/step
Epoch 128/400
16/16 - 0s - loss: 0.0890 - accuracy: 0.9940 - val_loss: 0.1049 - val_accuracy: 0.9860 - 72ms/epoch - 5ms/step
Epoch 129/400
16/16 - 0s - loss: 0.0882 - accuracy: 0.9940 - val_loss: 0.1042 - val_accuracy: 0.9860 - 112ms/epoch - 7ms/step
Epoch 130/400
16/16 - 0s - loss: 0.0875 - accuracy: 0.9940 - val_loss: 0.1034 - val_accuracy: 0.9860 - 82ms/epoch - 5ms/step
Epoch 131/400
16/16 - 0s - loss: 0.0868 - accuracy: 0.9940 - val_loss: 0.1027 - val_accuracy: 0.9880 - 73ms/epoch - 5ms/step
Epoch 132/400
16/16 - 0s - loss: 0.0861 - accuracy: 0.9940 - val_loss: 0.1019 - val_accuracy: 0.9880 - 111ms/epoch - 7ms/step
Epoch 133/400
16/16 - 0s - loss: 0.0854 - accuracy: 0.9940 - val_loss: 0.1012 - val_accuracy: 0.9880 - 116ms/epoch - 7ms/step
Epoch 134/400
16/16 - 0s - loss: 0.0847 - accuracy: 0.9940 - val_loss: 0.1005 - val_accuracy: 0.9880 - 72ms/epoch - 4ms/step
Epoch 135/400
16/16 - 0s - loss: 0.0841 - accuracy: 0.9940 - val_loss: 0.0998 - val_accuracy: 0.9880 - 86ms/epoch - 5ms/step
Epoch 136/400
16/16 - 0s - loss: 0.0834 - accuracy: 0.9940 - val_loss: 0.0991 - val_accuracy: 0.9880 - 74ms/epoch - 5ms/step
Epoch 137/400
16/16 - 0s - loss: 0.0828 - accuracy: 0.9940 - val_loss: 0.0984 - val_accuracy: 0.9880 - 121ms/epoch - 8ms/step
Epoch 138/400
16/16 - 0s - loss: 0.0821 - accuracy: 0.9940 - val_loss: 0.0977 - val_accuracy: 0.9880 - 72ms/epoch - 5ms/step
Epoch 139/400
16/16 - 0s - loss: 0.0815 - accuracy: 0.9940 - val_loss: 0.0970 - val_accuracy: 0.9880 - 72ms/epoch - 4ms/step
Epoch 140/400
16/16 - 0s - loss: 0.0809 - accuracy: 0.9940 - val_loss: 0.0963 - val_accuracy: 0.9880 - 75ms/epoch - 5ms/step
Epoch 141/400
16/16 - 0s - loss: 0.0803 - accuracy: 0.9940 - val_loss: 0.0956 - val_accuracy: 0.9880 - 72ms/epoch - 4ms/step
Epoch 142/400
16/16 - 0s - loss: 0.0797 - accuracy: 0.9940 - val_loss: 0.0950 - val_accuracy: 0.9880 - 79ms/epoch - 5ms/step
Epoch 143/400
16/16 - 0s - loss: 0.0791 - accuracy: 0.9940 - val_loss: 0.0943 - val_accuracy: 0.9880 - 112ms/epoch - 7ms/step
Epoch 144/400
16/16 - 0s - loss: 0.0785 - accuracy: 0.9940 - val_loss: 0.0936 - val_accuracy: 0.9900 - 111ms/epoch - 7ms/step
Epoch 145/400
16/16 - 0s - loss: 0.0779 - accuracy: 0.9940 - val_loss: 0.0930 - val_accuracy: 0.9900 - 71ms/epoch - 4ms/step
Epoch 146/400
16/16 - 0s - loss: 0.0773 - accuracy: 0.9940 - val_loss: 0.0924 - val_accuracy: 0.9900 - 72ms/epoch - 4ms/step
Epoch 147/400
16/16 - 0s - loss: 0.0768 - accuracy: 0.9940 - val_loss: 0.0917 - val_accuracy: 0.9920 - 117ms/epoch - 7ms/step
Epoch 148/400
16/16 - 0s - loss: 0.0762 - accuracy: 0.9940 - val_loss: 0.0911 - val_accuracy: 0.9920 - 97ms/epoch - 6ms/step
Epoch 149/400
16/16 - 0s - loss: 0.0756 - accuracy: 0.9940 - val_loss: 0.0905 - val_accuracy: 0.9920 - 77ms/epoch - 5ms/step
Epoch 150/400
16/16 - 0s - loss: 0.0751 - accuracy: 0.9940 - val_loss: 0.0899 - val_accuracy: 0.9920 - 73ms/epoch - 5ms/step
Epoch 151/400
16/16 - 0s - loss: 0.0745 - accuracy: 0.9940 - val_loss: 0.0893 - val_accuracy: 0.9920 - 118ms/epoch - 7ms/step
Epoch 152/400
16/16 - 0s - loss: 0.0740 - accuracy: 0.9940 - val_loss: 0.0887 - val_accuracy: 0.9920 - 72ms/epoch - 5ms/step
Epoch 153/400
16/16 - 0s - loss: 0.0735 - accuracy: 0.9940 - val_loss: 0.0881 - val_accuracy: 0.9920 - 75ms/epoch - 5ms/step
Epoch 154/400
16/16 - 0s - loss: 0.0730 - accuracy: 0.9940 - val_loss: 0.0876 - val_accuracy: 0.9920 - 75ms/epoch - 5ms/step
Epoch 155/400
16/16 - 0s - loss: 0.0724 - accuracy: 0.9940 - val_loss: 0.0869 - val_accuracy: 0.9920 - 72ms/epoch - 4ms/step
Epoch 156/400
16/16 - 0s - loss: 0.0719 - accuracy: 0.9960 - val_loss: 0.0863 - val_accuracy: 0.9920 - 72ms/epoch - 4ms/step
Epoch 157/400
16/16 - 0s - loss: 0.0714 - accuracy: 0.9960 - val_loss: 0.0858 - val_accuracy: 0.9920 - 72ms/epoch - 4ms/step
Epoch 158/400
16/16 - 0s - loss: 0.0709 - accuracy: 0.9960 - val_loss: 0.0852 - val_accuracy: 0.9920 - 115ms/epoch - 7ms/step
Epoch 159/400
16/16 - 0s - loss: 0.0704 - accuracy: 0.9960 - val_loss: 0.0846 - val_accuracy: 0.9920 - 91ms/epoch - 6ms/step
Epoch 160/400
16/16 - 0s - loss: 0.0700 - accuracy: 0.9960 - val_loss: 0.0841 - val_accuracy: 0.9920 - 119ms/epoch - 7ms/step
Epoch 161/400
16/16 - 0s - loss: 0.0695 - accuracy: 0.9960 - val_loss: 0.0835 - val_accuracy: 0.9940 - 78ms/epoch - 5ms/step
Epoch 162/400
16/16 - 0s - loss: 0.0690 - accuracy: 0.9960 - val_loss: 0.0830 - val_accuracy: 0.9940 - 117ms/epoch - 7ms/step
Epoch 163/400
16/16 - 0s - loss: 0.0685 - accuracy: 0.9960 - val_loss: 0.0825 - val_accuracy: 0.9940 - 74ms/epoch - 5ms/step
Epoch 164/400
16/16 - 0s - loss: 0.0681 - accuracy: 0.9960 - val_loss: 0.0820 - val_accuracy: 0.9940 - 78ms/epoch - 5ms/step
Epoch 165/400
16/16 - 0s - loss: 0.0676 - accuracy: 0.9960 - val_loss: 0.0814 - val_accuracy: 0.9940 - 75ms/epoch - 5ms/step
Epoch 166/400
16/16 - 0s - loss: 0.0671 - accuracy: 0.9960 - val_loss: 0.0809 - val_accuracy: 0.9940 - 74ms/epoch - 5ms/step
Epoch 167/400
16/16 - 0s - loss: 0.0667 - accuracy: 0.9960 - val_loss: 0.0805 - val_accuracy: 0.9940 - 75ms/epoch - 5ms/step
Epoch 168/400
16/16 - 0s - loss: 0.0663 - accuracy: 0.9960 - val_loss: 0.0800 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 169/400
16/16 - 0s - loss: 0.0658 - accuracy: 0.9960 - val_loss: 0.0795 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 170/400
16/16 - 0s - loss: 0.0654 - accuracy: 0.9960 - val_loss: 0.0790 - val_accuracy: 0.9960 - 67ms/epoch - 4ms/step
Epoch 171/400
16/16 - 0s - loss: 0.0650 - accuracy: 0.9960 - val_loss: 0.0785 - val_accuracy: 0.9960 - 125ms/epoch - 8ms/step
Epoch 172/400
16/16 - 0s - loss: 0.0645 - accuracy: 0.9960 - val_loss: 0.0780 - val_accuracy: 0.9960 - 112ms/epoch - 7ms/step
Epoch 173/400
16/16 - 0s - loss: 0.0641 - accuracy: 0.9960 - val_loss: 0.0775 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 174/400
16/16 - 0s - loss: 0.0637 - accuracy: 0.9960 - val_loss: 0.0770 - val_accuracy: 0.9960 - 118ms/epoch - 7ms/step
Epoch 175/400
16/16 - 0s - loss: 0.0633 - accuracy: 0.9960 - val_loss: 0.0766 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 176/400
16/16 - 0s - loss: 0.0629 - accuracy: 0.9960 - val_loss: 0.0761 - val_accuracy: 0.9960 - 112ms/epoch - 7ms/step
Epoch 177/400
16/16 - 0s - loss: 0.0625 - accuracy: 0.9960 - val_loss: 0.0756 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 178/400
16/16 - 0s - loss: 0.0621 - accuracy: 0.9960 - val_loss: 0.0752 - val_accuracy: 0.9960 - 71ms/epoch - 4ms/step
Epoch 179/400
16/16 - 0s - loss: 0.0617 - accuracy: 0.9960 - val_loss: 0.0747 - val_accuracy: 0.9960 - 113ms/epoch - 7ms/step
Epoch 180/400
16/16 - 0s - loss: 0.0614 - accuracy: 0.9960 - val_loss: 0.0743 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 181/400
16/16 - 0s - loss: 0.0610 - accuracy: 0.9960 - val_loss: 0.0738 - val_accuracy: 0.9960 - 84ms/epoch - 5ms/step
Epoch 182/400
16/16 - 0s - loss: 0.0606 - accuracy: 0.9960 - val_loss: 0.0733 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 183/400
16/16 - 0s - loss: 0.0602 - accuracy: 0.9960 - val_loss: 0.0729 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 184/400
16/16 - 0s - loss: 0.0598 - accuracy: 0.9960 - val_loss: 0.0724 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 185/400
16/16 - 0s - loss: 0.0595 - accuracy: 0.9960 - val_loss: 0.0721 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 186/400
16/16 - 0s - loss: 0.0591 - accuracy: 0.9960 - val_loss: 0.0717 - val_accuracy: 0.9960 - 72ms/epoch - 4ms/step
Epoch 187/400
16/16 - 0s - loss: 0.0587 - accuracy: 0.9960 - val_loss: 0.0712 - val_accuracy: 0.9960 - 119ms/epoch - 7ms/step
Epoch 188/400
16/16 - 0s - loss: 0.0584 - accuracy: 0.9960 - val_loss: 0.0708 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 189/400
16/16 - 0s - loss: 0.0580 - accuracy: 0.9960 - val_loss: 0.0704 - val_accuracy: 0.9960 - 76ms/epoch - 5ms/step
Epoch 190/400
16/16 - 0s - loss: 0.0577 - accuracy: 0.9960 - val_loss: 0.0700 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 191/400
16/16 - 0s - loss: 0.0573 - accuracy: 0.9960 - val_loss: 0.0696 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 192/400
16/16 - 0s - loss: 0.0570 - accuracy: 0.9960 - val_loss: 0.0692 - val_accuracy: 0.9960 - 77ms/epoch - 5ms/step
Epoch 193/400
16/16 - 0s - loss: 0.0566 - accuracy: 0.9960 - val_loss: 0.0688 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 194/400
16/16 - 0s - loss: 0.0563 - accuracy: 0.9960 - val_loss: 0.0684 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 195/400
16/16 - 0s - loss: 0.0560 - accuracy: 0.9960 - val_loss: 0.0681 - val_accuracy: 0.9960 - 78ms/epoch - 5ms/step
Epoch 196/400
16/16 - 0s - loss: 0.0556 - accuracy: 0.9960 - val_loss: 0.0676 - val_accuracy: 0.9960 - 74ms/epoch - 5ms/step
Epoch 197/400
16/16 - 0s - loss: 0.0553 - accuracy: 0.9960 - val_loss: 0.0672 - val_accuracy: 0.9960 - 112ms/epoch - 7ms/step
Epoch 198/400
16/16 - 0s - loss: 0.0550 - accuracy: 0.9960 - val_loss: 0.0668 - val_accuracy: 0.9960 - 112ms/epoch - 7ms/step
Epoch 199/400
16/16 - 0s - loss: 0.0547 - accuracy: 0.9960 - val_loss: 0.0664 - val_accuracy: 0.9960 - 117ms/epoch - 7ms/step
Epoch 200/400
16/16 - 0s - loss: 0.0544 - accuracy: 0.9960 - val_loss: 0.0661 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 201/400
16/16 - 0s - loss: 0.0540 - accuracy: 0.9960 - val_loss: 0.0658 - val_accuracy: 0.9960 - 115ms/epoch - 7ms/step
Epoch 202/400
16/16 - 0s - loss: 0.0537 - accuracy: 0.9960 - val_loss: 0.0654 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 203/400
16/16 - 0s - loss: 0.0534 - accuracy: 0.9960 - val_loss: 0.0650 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 204/400
16/16 - 0s - loss: 0.0531 - accuracy: 0.9960 - val_loss: 0.0645 - val_accuracy: 0.9960 - 118ms/epoch - 7ms/step
Epoch 205/400
16/16 - 0s - loss: 0.0528 - accuracy: 0.9960 - val_loss: 0.0643 - val_accuracy: 0.9960 - 114ms/epoch - 7ms/step
Epoch 206/400
16/16 - 0s - loss: 0.0525 - accuracy: 0.9960 - val_loss: 0.0639 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 207/400
16/16 - 0s - loss: 0.0522 - accuracy: 0.9980 - val_loss: 0.0635 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 208/400
16/16 - 0s - loss: 0.0519 - accuracy: 0.9980 - val_loss: 0.0631 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 209/400
16/16 - 0s - loss: 0.0516 - accuracy: 1.0000 - val_loss: 0.0628 - val_accuracy: 0.9980 - 119ms/epoch - 7ms/step
Epoch 210/400
16/16 - 0s - loss: 0.0513 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9980 - 83ms/epoch - 5ms/step
Epoch 211/400
16/16 - 0s - loss: 0.0510 - accuracy: 1.0000 - val_loss: 0.0621 - val_accuracy: 0.9980 - 83ms/epoch - 5ms/step
Epoch 212/400
16/16 - 0s - loss: 0.0508 - accuracy: 1.0000 - val_loss: 0.0618 - val_accuracy: 0.9980 - 100ms/epoch - 6ms/step
Epoch 213/400
16/16 - 0s - loss: 0.0505 - accuracy: 1.0000 - val_loss: 0.0615 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 214/400
16/16 - 0s - loss: 0.0502 - accuracy: 1.0000 - val_loss: 0.0611 - val_accuracy: 0.9980 - 127ms/epoch - 8ms/step
Epoch 215/400
16/16 - 0s - loss: 0.0499 - accuracy: 1.0000 - val_loss: 0.0607 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 216/400
16/16 - 0s - loss: 0.0496 - accuracy: 1.0000 - val_loss: 0.0605 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 217/400
16/16 - 0s - loss: 0.0494 - accuracy: 1.0000 - val_loss: 0.0601 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 218/400
16/16 - 0s - loss: 0.0491 - accuracy: 1.0000 - val_loss: 0.0598 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 219/400
16/16 - 0s - loss: 0.0488 - accuracy: 1.0000 - val_loss: 0.0595 - val_accuracy: 0.9980 - 85ms/epoch - 5ms/step
Epoch 220/400
16/16 - 0s - loss: 0.0486 - accuracy: 1.0000 - val_loss: 0.0592 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 221/400
16/16 - 0s - loss: 0.0483 - accuracy: 1.0000 - val_loss: 0.0588 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 222/400
16/16 - 0s - loss: 0.0480 - accuracy: 1.0000 - val_loss: 0.0585 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 223/400
16/16 - 0s - loss: 0.0478 - accuracy: 1.0000 - val_loss: 0.0582 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 224/400
16/16 - 0s - loss: 0.0475 - accuracy: 1.0000 - val_loss: 0.0579 - val_accuracy: 0.9980 - 77ms/epoch - 5ms/step
Epoch 225/400
16/16 - 0s - loss: 0.0473 - accuracy: 1.0000 - val_loss: 0.0575 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 226/400
16/16 - 0s - loss: 0.0470 - accuracy: 1.0000 - val_loss: 0.0572 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 227/400
16/16 - 0s - loss: 0.0468 - accuracy: 1.0000 - val_loss: 0.0570 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 228/400
16/16 - 0s - loss: 0.0465 - accuracy: 1.0000 - val_loss: 0.0567 - val_accuracy: 0.9980 - 116ms/epoch - 7ms/step
Epoch 229/400
16/16 - 0s - loss: 0.0463 - accuracy: 1.0000 - val_loss: 0.0564 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 230/400
16/16 - 0s - loss: 0.0460 - accuracy: 1.0000 - val_loss: 0.0561 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 231/400
16/16 - 0s - loss: 0.0458 - accuracy: 1.0000 - val_loss: 0.0558 - val_accuracy: 0.9980 - 121ms/epoch - 8ms/step
Epoch 232/400
16/16 - 0s - loss: 0.0455 - accuracy: 1.0000 - val_loss: 0.0554 - val_accuracy: 0.9980 - 119ms/epoch - 7ms/step
Epoch 233/400
16/16 - 0s - loss: 0.0453 - accuracy: 1.0000 - val_loss: 0.0552 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 234/400
16/16 - 0s - loss: 0.0450 - accuracy: 1.0000 - val_loss: 0.0548 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 235/400
16/16 - 0s - loss: 0.0448 - accuracy: 1.0000 - val_loss: 0.0546 - val_accuracy: 0.9980 - 125ms/epoch - 8ms/step
Epoch 236/400
16/16 - 0s - loss: 0.0446 - accuracy: 1.0000 - val_loss: 0.0543 - val_accuracy: 0.9980 - 123ms/epoch - 8ms/step
Epoch 237/400
16/16 - 0s - loss: 0.0443 - accuracy: 1.0000 - val_loss: 0.0540 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 238/400
16/16 - 0s - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.0537 - val_accuracy: 0.9980 - 116ms/epoch - 7ms/step
Epoch 239/400
16/16 - 0s - loss: 0.0439 - accuracy: 1.0000 - val_loss: 0.0535 - val_accuracy: 0.9980 - 80ms/epoch - 5ms/step
Epoch 240/400
16/16 - 0s - loss: 0.0437 - accuracy: 1.0000 - val_loss: 0.0533 - val_accuracy: 0.9980 - 84ms/epoch - 5ms/step
Epoch 241/400
16/16 - 0s - loss: 0.0434 - accuracy: 1.0000 - val_loss: 0.0530 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 242/400
16/16 - 0s - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.0527 - val_accuracy: 0.9980 - 116ms/epoch - 7ms/step
Epoch 243/400
16/16 - 0s - loss: 0.0430 - accuracy: 1.0000 - val_loss: 0.0525 - val_accuracy: 0.9980 - 82ms/epoch - 5ms/step
Epoch 244/400
16/16 - 0s - loss: 0.0428 - accuracy: 1.0000 - val_loss: 0.0522 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 245/400
16/16 - 0s - loss: 0.0426 - accuracy: 1.0000 - val_loss: 0.0520 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 246/400
16/16 - 0s - loss: 0.0423 - accuracy: 1.0000 - val_loss: 0.0516 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 247/400
16/16 - 0s - loss: 0.0421 - accuracy: 1.0000 - val_loss: 0.0515 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 248/400
16/16 - 0s - loss: 0.0419 - accuracy: 1.0000 - val_loss: 0.0512 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 249/400
16/16 - 0s - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.0509 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 250/400
16/16 - 0s - loss: 0.0415 - accuracy: 1.0000 - val_loss: 0.0506 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 251/400
16/16 - 0s - loss: 0.0413 - accuracy: 1.0000 - val_loss: 0.0503 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 252/400
16/16 - 0s - loss: 0.0410 - accuracy: 1.0000 - val_loss: 0.0501 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 253/400
16/16 - 0s - loss: 0.0408 - accuracy: 1.0000 - val_loss: 0.0499 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 254/400
16/16 - 0s - loss: 0.0406 - accuracy: 1.0000 - val_loss: 0.0496 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 255/400
16/16 - 0s - loss: 0.0404 - accuracy: 1.0000 - val_loss: 0.0494 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 256/400
16/16 - 0s - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.0491 - val_accuracy: 0.9980 - 111ms/epoch - 7ms/step
Epoch 257/400
16/16 - 0s - loss: 0.0400 - accuracy: 1.0000 - val_loss: 0.0489 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 258/400
16/16 - 0s - loss: 0.0398 - accuracy: 1.0000 - val_loss: 0.0486 - val_accuracy: 0.9980 - 83ms/epoch - 5ms/step
Epoch 259/400
16/16 - 0s - loss: 0.0396 - accuracy: 1.0000 - val_loss: 0.0484 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 260/400
16/16 - 0s - loss: 0.0394 - accuracy: 1.0000 - val_loss: 0.0482 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 261/400
16/16 - 0s - loss: 0.0392 - accuracy: 1.0000 - val_loss: 0.0479 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 262/400
16/16 - 0s - loss: 0.0390 - accuracy: 1.0000 - val_loss: 0.0476 - val_accuracy: 0.9980 - 84ms/epoch - 5ms/step
Epoch 263/400
16/16 - 0s - loss: 0.0389 - accuracy: 1.0000 - val_loss: 0.0474 - val_accuracy: 0.9980 - 80ms/epoch - 5ms/step
Epoch 264/400
16/16 - 0s - loss: 0.0386 - accuracy: 1.0000 - val_loss: 0.0472 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 265/400
16/16 - 0s - loss: 0.0385 - accuracy: 1.0000 - val_loss: 0.0470 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 266/400
16/16 - 0s - loss: 0.0383 - accuracy: 1.0000 - val_loss: 0.0468 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 267/400
16/16 - 0s - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.0465 - val_accuracy: 0.9980 - 110ms/epoch - 7ms/step
Epoch 268/400
16/16 - 0s - loss: 0.0379 - accuracy: 1.0000 - val_loss: 0.0463 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 269/400
16/16 - 0s - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.0460 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 270/400
16/16 - 0s - loss: 0.0375 - accuracy: 1.0000 - val_loss: 0.0458 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 271/400
16/16 - 0s - loss: 0.0373 - accuracy: 1.0000 - val_loss: 0.0456 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 272/400
16/16 - 0s - loss: 0.0372 - accuracy: 1.0000 - val_loss: 0.0454 - val_accuracy: 0.9980 - 116ms/epoch - 7ms/step
Epoch 273/400
16/16 - 0s - loss: 0.0370 - accuracy: 1.0000 - val_loss: 0.0451 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 274/400
16/16 - 0s - loss: 0.0368 - accuracy: 1.0000 - val_loss: 0.0450 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 275/400
16/16 - 0s - loss: 0.0366 - accuracy: 1.0000 - val_loss: 0.0447 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 276/400
16/16 - 0s - loss: 0.0364 - accuracy: 1.0000 - val_loss: 0.0445 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 277/400
16/16 - 0s - loss: 0.0363 - accuracy: 1.0000 - val_loss: 0.0443 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 278/400
16/16 - 0s - loss: 0.0361 - accuracy: 1.0000 - val_loss: 0.0441 - val_accuracy: 0.9980 - 109ms/epoch - 7ms/step
Epoch 279/400
16/16 - 0s - loss: 0.0359 - accuracy: 1.0000 - val_loss: 0.0439 - val_accuracy: 0.9980 - 81ms/epoch - 5ms/step
Epoch 280/400
16/16 - 0s - loss: 0.0358 - accuracy: 1.0000 - val_loss: 0.0436 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 281/400
16/16 - 0s - loss: 0.0356 - accuracy: 1.0000 - val_loss: 0.0433 - val_accuracy: 0.9980 - 68ms/epoch - 4ms/step
Epoch 282/400
16/16 - 0s - loss: 0.0354 - accuracy: 1.0000 - val_loss: 0.0432 - val_accuracy: 0.9980 - 81ms/epoch - 5ms/step
Epoch 283/400
16/16 - 0s - loss: 0.0352 - accuracy: 1.0000 - val_loss: 0.0430 - val_accuracy: 0.9980 - 112ms/epoch - 7ms/step
Epoch 284/400
16/16 - 0s - loss: 0.0351 - accuracy: 1.0000 - val_loss: 0.0429 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 285/400
16/16 - 0s - loss: 0.0349 - accuracy: 1.0000 - val_loss: 0.0426 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 286/400
16/16 - 0s - loss: 0.0347 - accuracy: 1.0000 - val_loss: 0.0424 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 287/400
16/16 - 0s - loss: 0.0346 - accuracy: 1.0000 - val_loss: 0.0422 - val_accuracy: 0.9980 - 73ms/epoch - 5ms/step
Epoch 288/400
16/16 - 0s - loss: 0.0344 - accuracy: 1.0000 - val_loss: 0.0421 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 289/400
16/16 - 0s - loss: 0.0343 - accuracy: 1.0000 - val_loss: 0.0418 - val_accuracy: 0.9980 - 115ms/epoch - 7ms/step
Epoch 290/400
16/16 - 0s - loss: 0.0341 - accuracy: 1.0000 - val_loss: 0.0416 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 291/400
16/16 - 0s - loss: 0.0339 - accuracy: 1.0000 - val_loss: 0.0415 - val_accuracy: 0.9980 - 113ms/epoch - 7ms/step
Epoch 292/400
16/16 - 0s - loss: 0.0338 - accuracy: 1.0000 - val_loss: 0.0412 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 293/400
16/16 - 0s - loss: 0.0336 - accuracy: 1.0000 - val_loss: 0.0411 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 294/400
16/16 - 0s - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.0409 - val_accuracy: 0.9980 - 123ms/epoch - 8ms/step
Epoch 295/400
16/16 - 0s - loss: 0.0333 - accuracy: 1.0000 - val_loss: 0.0407 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 296/400
16/16 - 0s - loss: 0.0331 - accuracy: 1.0000 - val_loss: 0.0405 - val_accuracy: 0.9980 - 118ms/epoch - 7ms/step
Epoch 297/400
16/16 - 0s - loss: 0.0330 - accuracy: 1.0000 - val_loss: 0.0403 - val_accuracy: 0.9980 - 70ms/epoch - 4ms/step
Epoch 298/400
16/16 - 0s - loss: 0.0328 - accuracy: 1.0000 - val_loss: 0.0401 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 299/400
16/16 - 0s - loss: 0.0327 - accuracy: 1.0000 - val_loss: 0.0399 - val_accuracy: 0.9980 - 123ms/epoch - 8ms/step
Epoch 300/400
16/16 - 0s - loss: 0.0325 - accuracy: 1.0000 - val_loss: 0.0397 - val_accuracy: 0.9980 - 72ms/epoch - 4ms/step
Epoch 301/400
16/16 - 0s - loss: 0.0324 - accuracy: 1.0000 - val_loss: 0.0395 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 302/400
16/16 - 0s - loss: 0.0322 - accuracy: 1.0000 - val_loss: 0.0393 - val_accuracy: 1.0000 - 124ms/epoch - 8ms/step
Epoch 303/400
16/16 - 0s - loss: 0.0321 - accuracy: 1.0000 - val_loss: 0.0392 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 304/400
16/16 - 0s - loss: 0.0319 - accuracy: 1.0000 - val_loss: 0.0391 - val_accuracy: 1.0000 - 84ms/epoch - 5ms/step
Epoch 305/400
16/16 - 0s - loss: 0.0318 - accuracy: 1.0000 - val_loss: 0.0389 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 306/400
16/16 - 0s - loss: 0.0316 - accuracy: 1.0000 - val_loss: 0.0387 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 307/400
16/16 - 0s - loss: 0.0315 - accuracy: 1.0000 - val_loss: 0.0384 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 308/400
16/16 - 0s - loss: 0.0313 - accuracy: 1.0000 - val_loss: 0.0383 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 309/400
16/16 - 0s - loss: 0.0312 - accuracy: 1.0000 - val_loss: 0.0381 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 310/400
16/16 - 0s - loss: 0.0310 - accuracy: 1.0000 - val_loss: 0.0379 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 311/400
16/16 - 0s - loss: 0.0309 - accuracy: 1.0000 - val_loss: 0.0378 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 312/400
16/16 - 0s - loss: 0.0308 - accuracy: 1.0000 - val_loss: 0.0376 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 313/400
16/16 - 0s - loss: 0.0306 - accuracy: 1.0000 - val_loss: 0.0375 - val_accuracy: 1.0000 - 120ms/epoch - 7ms/step
Epoch 314/400
16/16 - 0s - loss: 0.0305 - accuracy: 1.0000 - val_loss: 0.0373 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 315/400
16/16 - 0s - loss: 0.0304 - accuracy: 1.0000 - val_loss: 0.0371 - val_accuracy: 1.0000 - 89ms/epoch - 6ms/step
Epoch 316/400
16/16 - 0s - loss: 0.0302 - accuracy: 1.0000 - val_loss: 0.0369 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 317/400
16/16 - 0s - loss: 0.0301 - accuracy: 1.0000 - val_loss: 0.0368 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 318/400
16/16 - 0s - loss: 0.0299 - accuracy: 1.0000 - val_loss: 0.0366 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 319/400
16/16 - 0s - loss: 0.0298 - accuracy: 1.0000 - val_loss: 0.0365 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 320/400
16/16 - 0s - loss: 0.0297 - accuracy: 1.0000 - val_loss: 0.0363 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 321/400
16/16 - 0s - loss: 0.0295 - accuracy: 1.0000 - val_loss: 0.0361 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 322/400
16/16 - 0s - loss: 0.0294 - accuracy: 1.0000 - val_loss: 0.0359 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 323/400
16/16 - 0s - loss: 0.0293 - accuracy: 1.0000 - val_loss: 0.0358 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 324/400
16/16 - 0s - loss: 0.0292 - accuracy: 1.0000 - val_loss: 0.0357 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 325/400
16/16 - 0s - loss: 0.0290 - accuracy: 1.0000 - val_loss: 0.0355 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 326/400
16/16 - 0s - loss: 0.0289 - accuracy: 1.0000 - val_loss: 0.0354 - val_accuracy: 1.0000 - 125ms/epoch - 8ms/step
Epoch 327/400
16/16 - 0s - loss: 0.0288 - accuracy: 1.0000 - val_loss: 0.0352 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 328/400
16/16 - 0s - loss: 0.0287 - accuracy: 1.0000 - val_loss: 0.0350 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 329/400
16/16 - 0s - loss: 0.0285 - accuracy: 1.0000 - val_loss: 0.0349 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 330/400
16/16 - 0s - loss: 0.0284 - accuracy: 1.0000 - val_loss: 0.0348 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 331/400
16/16 - 0s - loss: 0.0283 - accuracy: 1.0000 - val_loss: 0.0346 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 332/400
16/16 - 0s - loss: 0.0281 - accuracy: 1.0000 - val_loss: 0.0344 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 333/400
16/16 - 0s - loss: 0.0280 - accuracy: 1.0000 - val_loss: 0.0343 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 334/400
16/16 - 0s - loss: 0.0279 - accuracy: 1.0000 - val_loss: 0.0342 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 335/400
16/16 - 0s - loss: 0.0278 - accuracy: 1.0000 - val_loss: 0.0340 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 336/400
16/16 - 0s - loss: 0.0277 - accuracy: 1.0000 - val_loss: 0.0338 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 337/400
16/16 - 0s - loss: 0.0275 - accuracy: 1.0000 - val_loss: 0.0336 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 338/400
16/16 - 0s - loss: 0.0274 - accuracy: 1.0000 - val_loss: 0.0336 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 339/400
16/16 - 0s - loss: 0.0273 - accuracy: 1.0000 - val_loss: 0.0334 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 340/400
16/16 - 0s - loss: 0.0272 - accuracy: 1.0000 - val_loss: 0.0333 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 341/400
16/16 - 0s - loss: 0.0271 - accuracy: 1.0000 - val_loss: 0.0332 - val_accuracy: 1.0000 - 125ms/epoch - 8ms/step
Epoch 342/400
16/16 - 0s - loss: 0.0270 - accuracy: 1.0000 - val_loss: 0.0331 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 343/400
16/16 - 0s - loss: 0.0269 - accuracy: 1.0000 - val_loss: 0.0328 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 344/400
16/16 - 0s - loss: 0.0267 - accuracy: 1.0000 - val_loss: 0.0327 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 345/400
16/16 - 0s - loss: 0.0266 - accuracy: 1.0000 - val_loss: 0.0326 - val_accuracy: 1.0000 - 86ms/epoch - 5ms/step
Epoch 346/400
16/16 - 0s - loss: 0.0265 - accuracy: 1.0000 - val_loss: 0.0324 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 347/400
16/16 - 0s - loss: 0.0264 - accuracy: 1.0000 - val_loss: 0.0323 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 348/400
16/16 - 0s - loss: 0.0263 - accuracy: 1.0000 - val_loss: 0.0321 - val_accuracy: 1.0000 - 73ms/epoch - 5ms/step
Epoch 349/400
16/16 - 0s - loss: 0.0262 - accuracy: 1.0000 - val_loss: 0.0320 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 350/400
16/16 - 0s - loss: 0.0261 - accuracy: 1.0000 - val_loss: 0.0319 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 351/400
16/16 - 0s - loss: 0.0259 - accuracy: 1.0000 - val_loss: 0.0317 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 352/400
16/16 - 0s - loss: 0.0258 - accuracy: 1.0000 - val_loss: 0.0316 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 353/400
16/16 - 0s - loss: 0.0257 - accuracy: 1.0000 - val_loss: 0.0315 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 354/400
16/16 - 0s - loss: 0.0256 - accuracy: 1.0000 - val_loss: 0.0313 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 355/400
16/16 - 0s - loss: 0.0255 - accuracy: 1.0000 - val_loss: 0.0312 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 356/400
16/16 - 0s - loss: 0.0254 - accuracy: 1.0000 - val_loss: 0.0310 - val_accuracy: 1.0000 - 125ms/epoch - 8ms/step
Epoch 357/400
16/16 - 0s - loss: 0.0253 - accuracy: 1.0000 - val_loss: 0.0309 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 358/400
16/16 - 0s - loss: 0.0252 - accuracy: 1.0000 - val_loss: 0.0308 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 359/400
16/16 - 0s - loss: 0.0251 - accuracy: 1.0000 - val_loss: 0.0306 - val_accuracy: 1.0000 - 121ms/epoch - 8ms/step
Epoch 360/400
16/16 - 0s - loss: 0.0250 - accuracy: 1.0000 - val_loss: 0.0305 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 361/400
16/16 - 0s - loss: 0.0249 - accuracy: 1.0000 - val_loss: 0.0304 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 362/400
16/16 - 0s - loss: 0.0248 - accuracy: 1.0000 - val_loss: 0.0302 - val_accuracy: 1.0000 - 123ms/epoch - 8ms/step
Epoch 363/400
16/16 - 0s - loss: 0.0247 - accuracy: 1.0000 - val_loss: 0.0301 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 364/400
16/16 - 0s - loss: 0.0246 - accuracy: 1.0000 - val_loss: 0.0299 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 365/400
16/16 - 0s - loss: 0.0245 - accuracy: 1.0000 - val_loss: 0.0298 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 366/400
16/16 - 0s - loss: 0.0244 - accuracy: 1.0000 - val_loss: 0.0297 - val_accuracy: 1.0000 - 125ms/epoch - 8ms/step
Epoch 367/400
16/16 - 0s - loss: 0.0243 - accuracy: 1.0000 - val_loss: 0.0296 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 368/400
16/16 - 0s - loss: 0.0242 - accuracy: 1.0000 - val_loss: 0.0295 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 369/400
16/16 - 0s - loss: 0.0241 - accuracy: 1.0000 - val_loss: 0.0293 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 370/400
16/16 - 0s - loss: 0.0240 - accuracy: 1.0000 - val_loss: 0.0292 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 371/400
16/16 - 0s - loss: 0.0239 - accuracy: 1.0000 - val_loss: 0.0292 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 372/400
16/16 - 0s - loss: 0.0238 - accuracy: 1.0000 - val_loss: 0.0290 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 373/400
16/16 - 0s - loss: 0.0237 - accuracy: 1.0000 - val_loss: 0.0289 - val_accuracy: 1.0000 - 121ms/epoch - 8ms/step
Epoch 374/400
16/16 - 0s - loss: 0.0236 - accuracy: 1.0000 - val_loss: 0.0288 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 375/400
16/16 - 0s - loss: 0.0235 - accuracy: 1.0000 - val_loss: 0.0287 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 376/400
16/16 - 0s - loss: 0.0234 - accuracy: 1.0000 - val_loss: 0.0286 - val_accuracy: 1.0000 - 84ms/epoch - 5ms/step
Epoch 377/400
16/16 - 0s - loss: 0.0233 - accuracy: 1.0000 - val_loss: 0.0284 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 378/400
16/16 - 0s - loss: 0.0232 - accuracy: 1.0000 - val_loss: 0.0283 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 379/400
16/16 - 0s - loss: 0.0231 - accuracy: 1.0000 - val_loss: 0.0282 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 380/400
16/16 - 0s - loss: 0.0230 - accuracy: 1.0000 - val_loss: 0.0281 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 381/400
16/16 - 0s - loss: 0.0229 - accuracy: 1.0000 - val_loss: 0.0280 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 382/400
16/16 - 0s - loss: 0.0228 - accuracy: 1.0000 - val_loss: 0.0279 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 383/400
16/16 - 0s - loss: 0.0227 - accuracy: 1.0000 - val_loss: 0.0278 - val_accuracy: 1.0000 - 72ms/epoch - 4ms/step
Epoch 384/400
16/16 - 0s - loss: 0.0226 - accuracy: 1.0000 - val_loss: 0.0276 - val_accuracy: 1.0000 - 124ms/epoch - 8ms/step
Epoch 385/400
16/16 - 0s - loss: 0.0225 - accuracy: 1.0000 - val_loss: 0.0276 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 386/400
16/16 - 0s - loss: 0.0224 - accuracy: 1.0000 - val_loss: 0.0274 - val_accuracy: 1.0000 - 84ms/epoch - 5ms/step
Epoch 387/400
16/16 - 0s - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.0273 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 388/400
16/16 - 0s - loss: 0.0223 - accuracy: 1.0000 - val_loss: 0.0272 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 389/400
16/16 - 0s - loss: 0.0222 - accuracy: 1.0000 - val_loss: 0.0271 - val_accuracy: 1.0000 - 85ms/epoch - 5ms/step
Epoch 390/400
16/16 - 0s - loss: 0.0221 - accuracy: 1.0000 - val_loss: 0.0270 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 391/400
16/16 - 0s - loss: 0.0220 - accuracy: 1.0000 - val_loss: 0.0269 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 392/400
16/16 - 0s - loss: 0.0219 - accuracy: 1.0000 - val_loss: 0.0268 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 393/400
16/16 - 0s - loss: 0.0218 - accuracy: 1.0000 - val_loss: 0.0266 - val_accuracy: 1.0000 - 71ms/epoch - 4ms/step
Epoch 394/400
16/16 - 0s - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.0266 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 395/400
16/16 - 0s - loss: 0.0217 - accuracy: 1.0000 - val_loss: 0.0264 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 396/400
16/16 - 0s - loss: 0.0216 - accuracy: 1.0000 - val_loss: 0.0264 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 397/400
16/16 - 0s - loss: 0.0215 - accuracy: 1.0000 - val_loss: 0.0263 - val_accuracy: 1.0000 - 124ms/epoch - 8ms/step
Epoch 398/400
16/16 - 0s - loss: 0.0214 - accuracy: 1.0000 - val_loss: 0.0262 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 399/400
16/16 - 0s - loss: 0.0213 - accuracy: 1.0000 - val_loss: 0.0261 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 400/400
16/16 - 0s - loss: 0.0212 - accuracy: 1.0000 - val_loss: 0.0260 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
第4个弱分类器训练完毕
Epoch 1/400
16/16 - 1s - loss: 1.9987 - accuracy: 0.5900 - val_loss: 2.1152 - val_accuracy: 0.6080 - 964ms/epoch - 60ms/step
Epoch 2/400
16/16 - 0s - loss: 1.9738 - accuracy: 0.5900 - val_loss: 2.0892 - val_accuracy: 0.6080 - 111ms/epoch - 7ms/step
Epoch 3/400
16/16 - 0s - loss: 1.9491 - accuracy: 0.5920 - val_loss: 2.0631 - val_accuracy: 0.6100 - 115ms/epoch - 7ms/step
Epoch 4/400
16/16 - 0s - loss: 1.9249 - accuracy: 0.5940 - val_loss: 2.0369 - val_accuracy: 0.6100 - 113ms/epoch - 7ms/step
Epoch 5/400
16/16 - 0s - loss: 1.9007 - accuracy: 0.5960 - val_loss: 2.0114 - val_accuracy: 0.6140 - 79ms/epoch - 5ms/step
Epoch 6/400
16/16 - 0s - loss: 1.8767 - accuracy: 0.5980 - val_loss: 1.9863 - val_accuracy: 0.6160 - 76ms/epoch - 5ms/step
Epoch 7/400
16/16 - 0s - loss: 1.8530 - accuracy: 0.6020 - val_loss: 1.9614 - val_accuracy: 0.6160 - 86ms/epoch - 5ms/step
Epoch 8/400
16/16 - 0s - loss: 1.8294 - accuracy: 0.6040 - val_loss: 1.9370 - val_accuracy: 0.6180 - 112ms/epoch - 7ms/step
Epoch 9/400
16/16 - 0s - loss: 1.8064 - accuracy: 0.6040 - val_loss: 1.9120 - val_accuracy: 0.6200 - 119ms/epoch - 7ms/step
Epoch 10/400
16/16 - 0s - loss: 1.7831 - accuracy: 0.6080 - val_loss: 1.8876 - val_accuracy: 0.6220 - 74ms/epoch - 5ms/step
Epoch 11/400
16/16 - 0s - loss: 1.7600 - accuracy: 0.6100 - val_loss: 1.8637 - val_accuracy: 0.6220 - 72ms/epoch - 4ms/step
Epoch 12/400
16/16 - 0s - loss: 1.7379 - accuracy: 0.6120 - val_loss: 1.8387 - val_accuracy: 0.6240 - 77ms/epoch - 5ms/step
Epoch 13/400
16/16 - 0s - loss: 1.7143 - accuracy: 0.6160 - val_loss: 1.8153 - val_accuracy: 0.6240 - 122ms/epoch - 8ms/step
Epoch 14/400
16/16 - 0s - loss: 1.6920 - accuracy: 0.6160 - val_loss: 1.7913 - val_accuracy: 0.6240 - 74ms/epoch - 5ms/step
Epoch 15/400
16/16 - 0s - loss: 1.6700 - accuracy: 0.6180 - val_loss: 1.7672 - val_accuracy: 0.6240 - 73ms/epoch - 5ms/step
Epoch 16/400
16/16 - 0s - loss: 1.6472 - accuracy: 0.6180 - val_loss: 1.7445 - val_accuracy: 0.6240 - 79ms/epoch - 5ms/step
Epoch 17/400
16/16 - 0s - loss: 1.6264 - accuracy: 0.6200 - val_loss: 1.7203 - val_accuracy: 0.6260 - 113ms/epoch - 7ms/step
Epoch 18/400
16/16 - 0s - loss: 1.6041 - accuracy: 0.6200 - val_loss: 1.6976 - val_accuracy: 0.6260 - 75ms/epoch - 5ms/step
Epoch 19/400
16/16 - 0s - loss: 1.5833 - accuracy: 0.6260 - val_loss: 1.6742 - val_accuracy: 0.6280 - 72ms/epoch - 4ms/step
Epoch 20/400
16/16 - 0s - loss: 1.5618 - accuracy: 0.6280 - val_loss: 1.6519 - val_accuracy: 0.6300 - 73ms/epoch - 5ms/step
Epoch 21/400
16/16 - 0s - loss: 1.5411 - accuracy: 0.6280 - val_loss: 1.6295 - val_accuracy: 0.6320 - 110ms/epoch - 7ms/step
Epoch 22/400
16/16 - 0s - loss: 1.5201 - accuracy: 0.6340 - val_loss: 1.6077 - val_accuracy: 0.6340 - 117ms/epoch - 7ms/step
Epoch 23/400
16/16 - 0s - loss: 1.4997 - accuracy: 0.6360 - val_loss: 1.5860 - val_accuracy: 0.6340 - 72ms/epoch - 5ms/step
Epoch 24/400
16/16 - 0s - loss: 1.4796 - accuracy: 0.6380 - val_loss: 1.5634 - val_accuracy: 0.6360 - 75ms/epoch - 5ms/step
Epoch 25/400
16/16 - 0s - loss: 1.4592 - accuracy: 0.6440 - val_loss: 1.5417 - val_accuracy: 0.6420 - 77ms/epoch - 5ms/step
Epoch 26/400
16/16 - 0s - loss: 1.4392 - accuracy: 0.6440 - val_loss: 1.5199 - val_accuracy: 0.6420 - 73ms/epoch - 5ms/step
Epoch 27/400
16/16 - 0s - loss: 1.4189 - accuracy: 0.6480 - val_loss: 1.4987 - val_accuracy: 0.6440 - 82ms/epoch - 5ms/step
Epoch 28/400
16/16 - 0s - loss: 1.3993 - accuracy: 0.6520 - val_loss: 1.4773 - val_accuracy: 0.6460 - 77ms/epoch - 5ms/step
Epoch 29/400
16/16 - 0s - loss: 1.3794 - accuracy: 0.6520 - val_loss: 1.4567 - val_accuracy: 0.6460 - 80ms/epoch - 5ms/step
Epoch 30/400
16/16 - 0s - loss: 1.3603 - accuracy: 0.6540 - val_loss: 1.4353 - val_accuracy: 0.6520 - 77ms/epoch - 5ms/step
Epoch 31/400
16/16 - 0s - loss: 1.3407 - accuracy: 0.6540 - val_loss: 1.4143 - val_accuracy: 0.6540 - 73ms/epoch - 5ms/step
Epoch 32/400
16/16 - 0s - loss: 1.3217 - accuracy: 0.6560 - val_loss: 1.3935 - val_accuracy: 0.6540 - 112ms/epoch - 7ms/step
Epoch 33/400
16/16 - 0s - loss: 1.3029 - accuracy: 0.6560 - val_loss: 1.3728 - val_accuracy: 0.6540 - 116ms/epoch - 7ms/step
Epoch 34/400
16/16 - 0s - loss: 1.2841 - accuracy: 0.6560 - val_loss: 1.3529 - val_accuracy: 0.6540 - 117ms/epoch - 7ms/step
Epoch 35/400
16/16 - 0s - loss: 1.2657 - accuracy: 0.6580 - val_loss: 1.3334 - val_accuracy: 0.6540 - 117ms/epoch - 7ms/step
Epoch 36/400
16/16 - 0s - loss: 1.2477 - accuracy: 0.6580 - val_loss: 1.3137 - val_accuracy: 0.6560 - 115ms/epoch - 7ms/step
Epoch 37/400
16/16 - 0s - loss: 1.2296 - accuracy: 0.6600 - val_loss: 1.2943 - val_accuracy: 0.6560 - 76ms/epoch - 5ms/step
Epoch 38/400
16/16 - 0s - loss: 1.2124 - accuracy: 0.6620 - val_loss: 1.2743 - val_accuracy: 0.6580 - 77ms/epoch - 5ms/step
Epoch 39/400
16/16 - 0s - loss: 1.1938 - accuracy: 0.6640 - val_loss: 1.2559 - val_accuracy: 0.6660 - 84ms/epoch - 5ms/step
Epoch 40/400
16/16 - 0s - loss: 1.1764 - accuracy: 0.6640 - val_loss: 1.2373 - val_accuracy: 0.6660 - 111ms/epoch - 7ms/step
Epoch 41/400
16/16 - 0s - loss: 1.1594 - accuracy: 0.6640 - val_loss: 1.2180 - val_accuracy: 0.6660 - 72ms/epoch - 5ms/step
Epoch 42/400
16/16 - 0s - loss: 1.1421 - accuracy: 0.6640 - val_loss: 1.1992 - val_accuracy: 0.6660 - 73ms/epoch - 5ms/step
Epoch 43/400
16/16 - 0s - loss: 1.1246 - accuracy: 0.6660 - val_loss: 1.1815 - val_accuracy: 0.6660 - 74ms/epoch - 5ms/step
Epoch 44/400
16/16 - 0s - loss: 1.1085 - accuracy: 0.6660 - val_loss: 1.1628 - val_accuracy: 0.6660 - 77ms/epoch - 5ms/step
Epoch 45/400
16/16 - 0s - loss: 1.0912 - accuracy: 0.6660 - val_loss: 1.1456 - val_accuracy: 0.6660 - 118ms/epoch - 7ms/step
Epoch 46/400
16/16 - 0s - loss: 1.0753 - accuracy: 0.6700 - val_loss: 1.1278 - val_accuracy: 0.6660 - 77ms/epoch - 5ms/step
Epoch 47/400
16/16 - 0s - loss: 1.0591 - accuracy: 0.6740 - val_loss: 1.1103 - val_accuracy: 0.6680 - 70ms/epoch - 4ms/step
Epoch 48/400
16/16 - 0s - loss: 1.0433 - accuracy: 0.6780 - val_loss: 1.0929 - val_accuracy: 0.6700 - 81ms/epoch - 5ms/step
Epoch 49/400
16/16 - 0s - loss: 1.0273 - accuracy: 0.6780 - val_loss: 1.0758 - val_accuracy: 0.6720 - 76ms/epoch - 5ms/step
Epoch 50/400
16/16 - 0s - loss: 1.0117 - accuracy: 0.6780 - val_loss: 1.0589 - val_accuracy: 0.6760 - 116ms/epoch - 7ms/step
Epoch 51/400
16/16 - 0s - loss: 0.9963 - accuracy: 0.6800 - val_loss: 1.0422 - val_accuracy: 0.6780 - 83ms/epoch - 5ms/step
Epoch 52/400
16/16 - 0s - loss: 0.9810 - accuracy: 0.6840 - val_loss: 1.0259 - val_accuracy: 0.6800 - 78ms/epoch - 5ms/step
Epoch 53/400
16/16 - 0s - loss: 0.9661 - accuracy: 0.6860 - val_loss: 1.0100 - val_accuracy: 0.6800 - 78ms/epoch - 5ms/step
Epoch 54/400
16/16 - 0s - loss: 0.9511 - accuracy: 0.6880 - val_loss: 0.9943 - val_accuracy: 0.6820 - 118ms/epoch - 7ms/step
Epoch 55/400
16/16 - 0s - loss: 0.9367 - accuracy: 0.6880 - val_loss: 0.9783 - val_accuracy: 0.6880 - 75ms/epoch - 5ms/step
Epoch 56/400
16/16 - 0s - loss: 0.9222 - accuracy: 0.6920 - val_loss: 0.9626 - val_accuracy: 0.6900 - 123ms/epoch - 8ms/step
Epoch 57/400
16/16 - 0s - loss: 0.9077 - accuracy: 0.6960 - val_loss: 0.9475 - val_accuracy: 0.6940 - 78ms/epoch - 5ms/step
Epoch 58/400
16/16 - 0s - loss: 0.8941 - accuracy: 0.7000 - val_loss: 0.9321 - val_accuracy: 0.6980 - 74ms/epoch - 5ms/step
Epoch 59/400
16/16 - 0s - loss: 0.8799 - accuracy: 0.7000 - val_loss: 0.9175 - val_accuracy: 0.7000 - 122ms/epoch - 8ms/step
Epoch 60/400
16/16 - 0s - loss: 0.8663 - accuracy: 0.7020 - val_loss: 0.9029 - val_accuracy: 0.7000 - 149ms/epoch - 9ms/step
Epoch 61/400
16/16 - 0s - loss: 0.8528 - accuracy: 0.7020 - val_loss: 0.8886 - val_accuracy: 0.7000 - 143ms/epoch - 9ms/step
Epoch 62/400
16/16 - 0s - loss: 0.8397 - accuracy: 0.7020 - val_loss: 0.8742 - val_accuracy: 0.7000 - 143ms/epoch - 9ms/step
Epoch 63/400
16/16 - 0s - loss: 0.8263 - accuracy: 0.7020 - val_loss: 0.8602 - val_accuracy: 0.7040 - 125ms/epoch - 8ms/step
Epoch 64/400
16/16 - 0s - loss: 0.8135 - accuracy: 0.7040 - val_loss: 0.8465 - val_accuracy: 0.7040 - 108ms/epoch - 7ms/step
Epoch 65/400
16/16 - 0s - loss: 0.8007 - accuracy: 0.7100 - val_loss: 0.8326 - val_accuracy: 0.7060 - 146ms/epoch - 9ms/step
Epoch 66/400
16/16 - 0s - loss: 0.7879 - accuracy: 0.7120 - val_loss: 0.8193 - val_accuracy: 0.7120 - 127ms/epoch - 8ms/step
Epoch 67/400
16/16 - 0s - loss: 0.7755 - accuracy: 0.7120 - val_loss: 0.8059 - val_accuracy: 0.7160 - 111ms/epoch - 7ms/step
Epoch 68/400
16/16 - 0s - loss: 0.7634 - accuracy: 0.7160 - val_loss: 0.7926 - val_accuracy: 0.7220 - 172ms/epoch - 11ms/step
Epoch 69/400
16/16 - 0s - loss: 0.7511 - accuracy: 0.7160 - val_loss: 0.7800 - val_accuracy: 0.7260 - 164ms/epoch - 10ms/step
Epoch 70/400
16/16 - 0s - loss: 0.7395 - accuracy: 0.7160 - val_loss: 0.7674 - val_accuracy: 0.7280 - 101ms/epoch - 6ms/step
Epoch 71/400
16/16 - 0s - loss: 0.7276 - accuracy: 0.7160 - val_loss: 0.7554 - val_accuracy: 0.7320 - 104ms/epoch - 6ms/step
Epoch 72/400
16/16 - 0s - loss: 0.7163 - accuracy: 0.7200 - val_loss: 0.7431 - val_accuracy: 0.7360 - 102ms/epoch - 6ms/step
Epoch 73/400
16/16 - 0s - loss: 0.7050 - accuracy: 0.7200 - val_loss: 0.7311 - val_accuracy: 0.7360 - 113ms/epoch - 7ms/step
Epoch 74/400
16/16 - 0s - loss: 0.6939 - accuracy: 0.7240 - val_loss: 0.7189 - val_accuracy: 0.7380 - 105ms/epoch - 7ms/step
Epoch 75/400
16/16 - 0s - loss: 0.6830 - accuracy: 0.7240 - val_loss: 0.7069 - val_accuracy: 0.7420 - 96ms/epoch - 6ms/step
Epoch 76/400
16/16 - 0s - loss: 0.6718 - accuracy: 0.7340 - val_loss: 0.6959 - val_accuracy: 0.7460 - 134ms/epoch - 8ms/step
Epoch 77/400
16/16 - 0s - loss: 0.6615 - accuracy: 0.7380 - val_loss: 0.6844 - val_accuracy: 0.7460 - 143ms/epoch - 9ms/step
Epoch 78/400
16/16 - 0s - loss: 0.6514 - accuracy: 0.7420 - val_loss: 0.6731 - val_accuracy: 0.7480 - 116ms/epoch - 7ms/step
Epoch 79/400
16/16 - 0s - loss: 0.6407 - accuracy: 0.7460 - val_loss: 0.6624 - val_accuracy: 0.7520 - 103ms/epoch - 6ms/step
Epoch 80/400
16/16 - 0s - loss: 0.6306 - accuracy: 0.7600 - val_loss: 0.6522 - val_accuracy: 0.7580 - 141ms/epoch - 9ms/step
Epoch 81/400
16/16 - 0s - loss: 0.6209 - accuracy: 0.7660 - val_loss: 0.6418 - val_accuracy: 0.7600 - 86ms/epoch - 5ms/step
Epoch 82/400
16/16 - 0s - loss: 0.6114 - accuracy: 0.7680 - val_loss: 0.6316 - val_accuracy: 0.7620 - 115ms/epoch - 7ms/step
Epoch 83/400
16/16 - 0s - loss: 0.6019 - accuracy: 0.7740 - val_loss: 0.6217 - val_accuracy: 0.7660 - 115ms/epoch - 7ms/step
Epoch 84/400
16/16 - 0s - loss: 0.5925 - accuracy: 0.7820 - val_loss: 0.6121 - val_accuracy: 0.7660 - 116ms/epoch - 7ms/step
Epoch 85/400
16/16 - 0s - loss: 0.5835 - accuracy: 0.7840 - val_loss: 0.6026 - val_accuracy: 0.7680 - 83ms/epoch - 5ms/step
Epoch 86/400
16/16 - 0s - loss: 0.5747 - accuracy: 0.7860 - val_loss: 0.5930 - val_accuracy: 0.7700 - 117ms/epoch - 7ms/step
Epoch 87/400
16/16 - 0s - loss: 0.5659 - accuracy: 0.7920 - val_loss: 0.5835 - val_accuracy: 0.7700 - 72ms/epoch - 5ms/step
Epoch 88/400
16/16 - 0s - loss: 0.5574 - accuracy: 0.7980 - val_loss: 0.5744 - val_accuracy: 0.7740 - 109ms/epoch - 7ms/step
Epoch 89/400
16/16 - 0s - loss: 0.5488 - accuracy: 0.8020 - val_loss: 0.5661 - val_accuracy: 0.7820 - 114ms/epoch - 7ms/step
Epoch 90/400
16/16 - 0s - loss: 0.5407 - accuracy: 0.8020 - val_loss: 0.5580 - val_accuracy: 0.7820 - 74ms/epoch - 5ms/step
Epoch 91/400
16/16 - 0s - loss: 0.5330 - accuracy: 0.8020 - val_loss: 0.5492 - val_accuracy: 0.7840 - 120ms/epoch - 7ms/step
Epoch 92/400
16/16 - 0s - loss: 0.5251 - accuracy: 0.8020 - val_loss: 0.5408 - val_accuracy: 0.7880 - 75ms/epoch - 5ms/step
Epoch 93/400
16/16 - 0s - loss: 0.5173 - accuracy: 0.8100 - val_loss: 0.5332 - val_accuracy: 0.7880 - 73ms/epoch - 5ms/step
Epoch 94/400
16/16 - 0s - loss: 0.5101 - accuracy: 0.8080 - val_loss: 0.5252 - val_accuracy: 0.7900 - 71ms/epoch - 4ms/step
Epoch 95/400
16/16 - 0s - loss: 0.5028 - accuracy: 0.8080 - val_loss: 0.5174 - val_accuracy: 0.7940 - 116ms/epoch - 7ms/step
Epoch 96/400
16/16 - 0s - loss: 0.4956 - accuracy: 0.8120 - val_loss: 0.5103 - val_accuracy: 0.7960 - 117ms/epoch - 7ms/step
Epoch 97/400
16/16 - 0s - loss: 0.4888 - accuracy: 0.8160 - val_loss: 0.5033 - val_accuracy: 0.7960 - 114ms/epoch - 7ms/step
Epoch 98/400
16/16 - 0s - loss: 0.4823 - accuracy: 0.8180 - val_loss: 0.4964 - val_accuracy: 0.8000 - 80ms/epoch - 5ms/step
Epoch 99/400
16/16 - 0s - loss: 0.4757 - accuracy: 0.8180 - val_loss: 0.4899 - val_accuracy: 0.8040 - 116ms/epoch - 7ms/step
Epoch 100/400
16/16 - 0s - loss: 0.4699 - accuracy: 0.8200 - val_loss: 0.4826 - val_accuracy: 0.8040 - 83ms/epoch - 5ms/step
Epoch 101/400
16/16 - 0s - loss: 0.4630 - accuracy: 0.8240 - val_loss: 0.4770 - val_accuracy: 0.8080 - 119ms/epoch - 7ms/step
Epoch 102/400
16/16 - 0s - loss: 0.4575 - accuracy: 0.8280 - val_loss: 0.4707 - val_accuracy: 0.8080 - 73ms/epoch - 5ms/step
Epoch 103/400
16/16 - 0s - loss: 0.4518 - accuracy: 0.8300 - val_loss: 0.4644 - val_accuracy: 0.8140 - 110ms/epoch - 7ms/step
Epoch 104/400
16/16 - 0s - loss: 0.4459 - accuracy: 0.8300 - val_loss: 0.4589 - val_accuracy: 0.8140 - 84ms/epoch - 5ms/step
Epoch 105/400
16/16 - 0s - loss: 0.4408 - accuracy: 0.8300 - val_loss: 0.4532 - val_accuracy: 0.8140 - 124ms/epoch - 8ms/step
Epoch 106/400
16/16 - 0s - loss: 0.4354 - accuracy: 0.8340 - val_loss: 0.4482 - val_accuracy: 0.8180 - 117ms/epoch - 7ms/step
Epoch 107/400
16/16 - 0s - loss: 0.4305 - accuracy: 0.8360 - val_loss: 0.4431 - val_accuracy: 0.8220 - 72ms/epoch - 5ms/step
Epoch 108/400
16/16 - 0s - loss: 0.4255 - accuracy: 0.8360 - val_loss: 0.4383 - val_accuracy: 0.8220 - 76ms/epoch - 5ms/step
Epoch 109/400
16/16 - 0s - loss: 0.4209 - accuracy: 0.8400 - val_loss: 0.4334 - val_accuracy: 0.8240 - 111ms/epoch - 7ms/step
Epoch 110/400
16/16 - 0s - loss: 0.4164 - accuracy: 0.8400 - val_loss: 0.4288 - val_accuracy: 0.8260 - 76ms/epoch - 5ms/step
Epoch 111/400
16/16 - 0s - loss: 0.4120 - accuracy: 0.8400 - val_loss: 0.4241 - val_accuracy: 0.8280 - 116ms/epoch - 7ms/step
Epoch 112/400
16/16 - 0s - loss: 0.4077 - accuracy: 0.8420 - val_loss: 0.4197 - val_accuracy: 0.8300 - 115ms/epoch - 7ms/step
Epoch 113/400
16/16 - 0s - loss: 0.4035 - accuracy: 0.8420 - val_loss: 0.4157 - val_accuracy: 0.8320 - 116ms/epoch - 7ms/step
Epoch 114/400
16/16 - 0s - loss: 0.3998 - accuracy: 0.8420 - val_loss: 0.4115 - val_accuracy: 0.8340 - 116ms/epoch - 7ms/step
Epoch 115/400
16/16 - 0s - loss: 0.3958 - accuracy: 0.8420 - val_loss: 0.4076 - val_accuracy: 0.8360 - 81ms/epoch - 5ms/step
Epoch 116/400
16/16 - 0s - loss: 0.3920 - accuracy: 0.8460 - val_loss: 0.4040 - val_accuracy: 0.8400 - 79ms/epoch - 5ms/step
Epoch 117/400
16/16 - 0s - loss: 0.3885 - accuracy: 0.8480 - val_loss: 0.4004 - val_accuracy: 0.8400 - 116ms/epoch - 7ms/step
Epoch 118/400
16/16 - 0s - loss: 0.3850 - accuracy: 0.8480 - val_loss: 0.3970 - val_accuracy: 0.8400 - 81ms/epoch - 5ms/step
Epoch 119/400
16/16 - 0s - loss: 0.3818 - accuracy: 0.8480 - val_loss: 0.3936 - val_accuracy: 0.8420 - 72ms/epoch - 4ms/step
Epoch 120/400
16/16 - 0s - loss: 0.3785 - accuracy: 0.8480 - val_loss: 0.3905 - val_accuracy: 0.8440 - 116ms/epoch - 7ms/step
Epoch 121/400
16/16 - 0s - loss: 0.3755 - accuracy: 0.8480 - val_loss: 0.3873 - val_accuracy: 0.8440 - 70ms/epoch - 4ms/step
Epoch 122/400
16/16 - 0s - loss: 0.3725 - accuracy: 0.8480 - val_loss: 0.3845 - val_accuracy: 0.8460 - 74ms/epoch - 5ms/step
Epoch 123/400
16/16 - 0s - loss: 0.3697 - accuracy: 0.8520 - val_loss: 0.3816 - val_accuracy: 0.8500 - 116ms/epoch - 7ms/step
Epoch 124/400
16/16 - 0s - loss: 0.3670 - accuracy: 0.8520 - val_loss: 0.3788 - val_accuracy: 0.8500 - 78ms/epoch - 5ms/step
Epoch 125/400
16/16 - 0s - loss: 0.3644 - accuracy: 0.8540 - val_loss: 0.3762 - val_accuracy: 0.8520 - 83ms/epoch - 5ms/step
Epoch 126/400
16/16 - 0s - loss: 0.3617 - accuracy: 0.8540 - val_loss: 0.3737 - val_accuracy: 0.8540 - 120ms/epoch - 7ms/step
Epoch 127/400
16/16 - 0s - loss: 0.3592 - accuracy: 0.8540 - val_loss: 0.3710 - val_accuracy: 0.8560 - 84ms/epoch - 5ms/step
Epoch 128/400
16/16 - 0s - loss: 0.3567 - accuracy: 0.8520 - val_loss: 0.3687 - val_accuracy: 0.8580 - 119ms/epoch - 7ms/step
Epoch 129/400
16/16 - 0s - loss: 0.3544 - accuracy: 0.8520 - val_loss: 0.3662 - val_accuracy: 0.8580 - 78ms/epoch - 5ms/step
Epoch 130/400
16/16 - 0s - loss: 0.3521 - accuracy: 0.8540 - val_loss: 0.3638 - val_accuracy: 0.8600 - 117ms/epoch - 7ms/step
Epoch 131/400
16/16 - 0s - loss: 0.3499 - accuracy: 0.8640 - val_loss: 0.3615 - val_accuracy: 0.8600 - 114ms/epoch - 7ms/step
Epoch 132/400
16/16 - 0s - loss: 0.3477 - accuracy: 0.8640 - val_loss: 0.3594 - val_accuracy: 0.8640 - 74ms/epoch - 5ms/step
Epoch 133/400
16/16 - 0s - loss: 0.3456 - accuracy: 0.8680 - val_loss: 0.3573 - val_accuracy: 0.8700 - 114ms/epoch - 7ms/step
Epoch 134/400
16/16 - 0s - loss: 0.3436 - accuracy: 0.8680 - val_loss: 0.3552 - val_accuracy: 0.8720 - 113ms/epoch - 7ms/step
Epoch 135/400
16/16 - 0s - loss: 0.3415 - accuracy: 0.8700 - val_loss: 0.3533 - val_accuracy: 0.8700 - 76ms/epoch - 5ms/step
Epoch 136/400
16/16 - 0s - loss: 0.3397 - accuracy: 0.8700 - val_loss: 0.3513 - val_accuracy: 0.8720 - 127ms/epoch - 8ms/step
Epoch 137/400
16/16 - 0s - loss: 0.3377 - accuracy: 0.8700 - val_loss: 0.3494 - val_accuracy: 0.8720 - 119ms/epoch - 7ms/step
Epoch 138/400
16/16 - 0s - loss: 0.3359 - accuracy: 0.8720 - val_loss: 0.3475 - val_accuracy: 0.8740 - 74ms/epoch - 5ms/step
Epoch 139/400
16/16 - 0s - loss: 0.3340 - accuracy: 0.8720 - val_loss: 0.3456 - val_accuracy: 0.8780 - 77ms/epoch - 5ms/step
Epoch 140/400
16/16 - 0s - loss: 0.3322 - accuracy: 0.8740 - val_loss: 0.3439 - val_accuracy: 0.8800 - 121ms/epoch - 8ms/step
Epoch 141/400
16/16 - 0s - loss: 0.3305 - accuracy: 0.8760 - val_loss: 0.3420 - val_accuracy: 0.8880 - 76ms/epoch - 5ms/step
Epoch 142/400
16/16 - 0s - loss: 0.3287 - accuracy: 0.8780 - val_loss: 0.3404 - val_accuracy: 0.8900 - 116ms/epoch - 7ms/step
Epoch 143/400
16/16 - 0s - loss: 0.3271 - accuracy: 0.8860 - val_loss: 0.3385 - val_accuracy: 0.8940 - 119ms/epoch - 7ms/step
Epoch 144/400
16/16 - 0s - loss: 0.3252 - accuracy: 0.8900 - val_loss: 0.3369 - val_accuracy: 0.8960 - 90ms/epoch - 6ms/step
Epoch 145/400
16/16 - 0s - loss: 0.3236 - accuracy: 0.8920 - val_loss: 0.3352 - val_accuracy: 0.8980 - 111ms/epoch - 7ms/step
Epoch 146/400
16/16 - 0s - loss: 0.3220 - accuracy: 0.8980 - val_loss: 0.3334 - val_accuracy: 0.9000 - 72ms/epoch - 5ms/step
Epoch 147/400
16/16 - 0s - loss: 0.3203 - accuracy: 0.9000 - val_loss: 0.3318 - val_accuracy: 0.9020 - 82ms/epoch - 5ms/step
Epoch 148/400
16/16 - 0s - loss: 0.3188 - accuracy: 0.9000 - val_loss: 0.3301 - val_accuracy: 0.9060 - 119ms/epoch - 7ms/step
Epoch 149/400
16/16 - 0s - loss: 0.3172 - accuracy: 0.9060 - val_loss: 0.3286 - val_accuracy: 0.9080 - 75ms/epoch - 5ms/step
Epoch 150/400
16/16 - 0s - loss: 0.3156 - accuracy: 0.9100 - val_loss: 0.3270 - val_accuracy: 0.9100 - 114ms/epoch - 7ms/step
Epoch 151/400
16/16 - 0s - loss: 0.3141 - accuracy: 0.9100 - val_loss: 0.3254 - val_accuracy: 0.9140 - 115ms/epoch - 7ms/step
Epoch 152/400
16/16 - 0s - loss: 0.3125 - accuracy: 0.9100 - val_loss: 0.3238 - val_accuracy: 0.9140 - 112ms/epoch - 7ms/step
Epoch 153/400
16/16 - 0s - loss: 0.3110 - accuracy: 0.9120 - val_loss: 0.3223 - val_accuracy: 0.9180 - 134ms/epoch - 8ms/step
Epoch 154/400
16/16 - 0s - loss: 0.3095 - accuracy: 0.9140 - val_loss: 0.3207 - val_accuracy: 0.9200 - 87ms/epoch - 5ms/step
Epoch 155/400
16/16 - 0s - loss: 0.3080 - accuracy: 0.9160 - val_loss: 0.3192 - val_accuracy: 0.9240 - 116ms/epoch - 7ms/step
Epoch 156/400
16/16 - 0s - loss: 0.3065 - accuracy: 0.9200 - val_loss: 0.3177 - val_accuracy: 0.9260 - 114ms/epoch - 7ms/step
Epoch 157/400
16/16 - 0s - loss: 0.3050 - accuracy: 0.9200 - val_loss: 0.3162 - val_accuracy: 0.9260 - 71ms/epoch - 4ms/step
Epoch 158/400
16/16 - 0s - loss: 0.3035 - accuracy: 0.9200 - val_loss: 0.3146 - val_accuracy: 0.9300 - 79ms/epoch - 5ms/step
Epoch 159/400
16/16 - 0s - loss: 0.3020 - accuracy: 0.9220 - val_loss: 0.3131 - val_accuracy: 0.9320 - 70ms/epoch - 4ms/step
Epoch 160/400
16/16 - 0s - loss: 0.3005 - accuracy: 0.9240 - val_loss: 0.3115 - val_accuracy: 0.9320 - 82ms/epoch - 5ms/step
Epoch 161/400
16/16 - 0s - loss: 0.2990 - accuracy: 0.9260 - val_loss: 0.3101 - val_accuracy: 0.9320 - 117ms/epoch - 7ms/step
Epoch 162/400
16/16 - 0s - loss: 0.2976 - accuracy: 0.9260 - val_loss: 0.3085 - val_accuracy: 0.9360 - 114ms/epoch - 7ms/step
Epoch 163/400
16/16 - 0s - loss: 0.2961 - accuracy: 0.9260 - val_loss: 0.3071 - val_accuracy: 0.9360 - 115ms/epoch - 7ms/step
Epoch 164/400
16/16 - 0s - loss: 0.2946 - accuracy: 0.9260 - val_loss: 0.3056 - val_accuracy: 0.9360 - 125ms/epoch - 8ms/step
Epoch 165/400
16/16 - 0s - loss: 0.2932 - accuracy: 0.9300 - val_loss: 0.3041 - val_accuracy: 0.9380 - 75ms/epoch - 5ms/step
Epoch 166/400
16/16 - 0s - loss: 0.2917 - accuracy: 0.9320 - val_loss: 0.3027 - val_accuracy: 0.9380 - 74ms/epoch - 5ms/step
Epoch 167/400
16/16 - 0s - loss: 0.2903 - accuracy: 0.9340 - val_loss: 0.3012 - val_accuracy: 0.9380 - 113ms/epoch - 7ms/step
Epoch 168/400
16/16 - 0s - loss: 0.2888 - accuracy: 0.9360 - val_loss: 0.2997 - val_accuracy: 0.9380 - 115ms/epoch - 7ms/step
Epoch 169/400
16/16 - 0s - loss: 0.2873 - accuracy: 0.9360 - val_loss: 0.2982 - val_accuracy: 0.9380 - 114ms/epoch - 7ms/step
Epoch 170/400
16/16 - 0s - loss: 0.2859 - accuracy: 0.9380 - val_loss: 0.2968 - val_accuracy: 0.9380 - 111ms/epoch - 7ms/step
Epoch 171/400
16/16 - 0s - loss: 0.2845 - accuracy: 0.9420 - val_loss: 0.2952 - val_accuracy: 0.9420 - 112ms/epoch - 7ms/step
Epoch 172/400
16/16 - 0s - loss: 0.2830 - accuracy: 0.9460 - val_loss: 0.2938 - val_accuracy: 0.9420 - 81ms/epoch - 5ms/step
Epoch 173/400
16/16 - 0s - loss: 0.2815 - accuracy: 0.9480 - val_loss: 0.2923 - val_accuracy: 0.9440 - 117ms/epoch - 7ms/step
Epoch 174/400
16/16 - 0s - loss: 0.2801 - accuracy: 0.9480 - val_loss: 0.2907 - val_accuracy: 0.9440 - 77ms/epoch - 5ms/step
Epoch 175/400
16/16 - 0s - loss: 0.2786 - accuracy: 0.9500 - val_loss: 0.2893 - val_accuracy: 0.9440 - 116ms/epoch - 7ms/step
Epoch 176/400
16/16 - 0s - loss: 0.2772 - accuracy: 0.9500 - val_loss: 0.2878 - val_accuracy: 0.9440 - 113ms/epoch - 7ms/step
Epoch 177/400
16/16 - 0s - loss: 0.2757 - accuracy: 0.9540 - val_loss: 0.2863 - val_accuracy: 0.9460 - 118ms/epoch - 7ms/step
Epoch 178/400
16/16 - 0s - loss: 0.2743 - accuracy: 0.9560 - val_loss: 0.2848 - val_accuracy: 0.9480 - 76ms/epoch - 5ms/step
Epoch 179/400
16/16 - 0s - loss: 0.2729 - accuracy: 0.9580 - val_loss: 0.2833 - val_accuracy: 0.9480 - 114ms/epoch - 7ms/step
Epoch 180/400
16/16 - 0s - loss: 0.2714 - accuracy: 0.9580 - val_loss: 0.2820 - val_accuracy: 0.9480 - 113ms/epoch - 7ms/step
Epoch 181/400
16/16 - 0s - loss: 0.2700 - accuracy: 0.9580 - val_loss: 0.2804 - val_accuracy: 0.9480 - 76ms/epoch - 5ms/step
Epoch 182/400
16/16 - 0s - loss: 0.2685 - accuracy: 0.9620 - val_loss: 0.2789 - val_accuracy: 0.9540 - 78ms/epoch - 5ms/step
Epoch 183/400
16/16 - 0s - loss: 0.2671 - accuracy: 0.9620 - val_loss: 0.2774 - val_accuracy: 0.9540 - 83ms/epoch - 5ms/step
Epoch 184/400
16/16 - 0s - loss: 0.2657 - accuracy: 0.9660 - val_loss: 0.2759 - val_accuracy: 0.9540 - 72ms/epoch - 4ms/step
Epoch 185/400
16/16 - 0s - loss: 0.2642 - accuracy: 0.9660 - val_loss: 0.2744 - val_accuracy: 0.9540 - 77ms/epoch - 5ms/step
Epoch 186/400
16/16 - 0s - loss: 0.2628 - accuracy: 0.9660 - val_loss: 0.2729 - val_accuracy: 0.9560 - 71ms/epoch - 4ms/step
Epoch 187/400
16/16 - 0s - loss: 0.2613 - accuracy: 0.9660 - val_loss: 0.2714 - val_accuracy: 0.9580 - 73ms/epoch - 5ms/step
Epoch 188/400
16/16 - 0s - loss: 0.2599 - accuracy: 0.9680 - val_loss: 0.2699 - val_accuracy: 0.9580 - 77ms/epoch - 5ms/step
Epoch 189/400
16/16 - 0s - loss: 0.2585 - accuracy: 0.9680 - val_loss: 0.2684 - val_accuracy: 0.9580 - 75ms/epoch - 5ms/step
Epoch 190/400
16/16 - 0s - loss: 0.2570 - accuracy: 0.9680 - val_loss: 0.2669 - val_accuracy: 0.9580 - 117ms/epoch - 7ms/step
Epoch 191/400
16/16 - 0s - loss: 0.2556 - accuracy: 0.9680 - val_loss: 0.2654 - val_accuracy: 0.9620 - 111ms/epoch - 7ms/step
Epoch 192/400
16/16 - 0s - loss: 0.2542 - accuracy: 0.9680 - val_loss: 0.2638 - val_accuracy: 0.9640 - 79ms/epoch - 5ms/step
Epoch 193/400
16/16 - 0s - loss: 0.2527 - accuracy: 0.9680 - val_loss: 0.2624 - val_accuracy: 0.9640 - 80ms/epoch - 5ms/step
Epoch 194/400
16/16 - 0s - loss: 0.2512 - accuracy: 0.9680 - val_loss: 0.2609 - val_accuracy: 0.9640 - 119ms/epoch - 7ms/step
Epoch 195/400
16/16 - 0s - loss: 0.2498 - accuracy: 0.9700 - val_loss: 0.2593 - val_accuracy: 0.9640 - 122ms/epoch - 8ms/step
Epoch 196/400
16/16 - 0s - loss: 0.2483 - accuracy: 0.9720 - val_loss: 0.2578 - val_accuracy: 0.9640 - 80ms/epoch - 5ms/step
Epoch 197/400
16/16 - 0s - loss: 0.2468 - accuracy: 0.9720 - val_loss: 0.2562 - val_accuracy: 0.9660 - 74ms/epoch - 5ms/step
Epoch 198/400
16/16 - 0s - loss: 0.2454 - accuracy: 0.9760 - val_loss: 0.2548 - val_accuracy: 0.9660 - 113ms/epoch - 7ms/step
Epoch 199/400
16/16 - 0s - loss: 0.2440 - accuracy: 0.9760 - val_loss: 0.2530 - val_accuracy: 0.9680 - 74ms/epoch - 5ms/step
Epoch 200/400
16/16 - 0s - loss: 0.2424 - accuracy: 0.9780 - val_loss: 0.2516 - val_accuracy: 0.9680 - 70ms/epoch - 4ms/step
Epoch 201/400
16/16 - 0s - loss: 0.2410 - accuracy: 0.9780 - val_loss: 0.2500 - val_accuracy: 0.9680 - 71ms/epoch - 4ms/step
Epoch 202/400
16/16 - 0s - loss: 0.2395 - accuracy: 0.9780 - val_loss: 0.2485 - val_accuracy: 0.9700 - 113ms/epoch - 7ms/step
Epoch 203/400
16/16 - 0s - loss: 0.2380 - accuracy: 0.9780 - val_loss: 0.2470 - val_accuracy: 0.9720 - 112ms/epoch - 7ms/step
Epoch 204/400
16/16 - 0s - loss: 0.2366 - accuracy: 0.9800 - val_loss: 0.2455 - val_accuracy: 0.9740 - 79ms/epoch - 5ms/step
Epoch 205/400
16/16 - 0s - loss: 0.2351 - accuracy: 0.9820 - val_loss: 0.2440 - val_accuracy: 0.9740 - 84ms/epoch - 5ms/step
Epoch 206/400
16/16 - 0s - loss: 0.2336 - accuracy: 0.9820 - val_loss: 0.2425 - val_accuracy: 0.9740 - 114ms/epoch - 7ms/step
Epoch 207/400
16/16 - 0s - loss: 0.2322 - accuracy: 0.9820 - val_loss: 0.2409 - val_accuracy: 0.9740 - 115ms/epoch - 7ms/step
Epoch 208/400
16/16 - 0s - loss: 0.2307 - accuracy: 0.9820 - val_loss: 0.2394 - val_accuracy: 0.9760 - 73ms/epoch - 5ms/step
Epoch 209/400
16/16 - 0s - loss: 0.2292 - accuracy: 0.9840 - val_loss: 0.2378 - val_accuracy: 0.9760 - 85ms/epoch - 5ms/step
Epoch 210/400
16/16 - 0s - loss: 0.2277 - accuracy: 0.9840 - val_loss: 0.2363 - val_accuracy: 0.9760 - 110ms/epoch - 7ms/step
Epoch 211/400
16/16 - 0s - loss: 0.2262 - accuracy: 0.9840 - val_loss: 0.2346 - val_accuracy: 0.9760 - 73ms/epoch - 5ms/step
Epoch 212/400
16/16 - 0s - loss: 0.2246 - accuracy: 0.9880 - val_loss: 0.2331 - val_accuracy: 0.9800 - 112ms/epoch - 7ms/step
Epoch 213/400
16/16 - 0s - loss: 0.2231 - accuracy: 0.9880 - val_loss: 0.2315 - val_accuracy: 0.9820 - 75ms/epoch - 5ms/step
Epoch 214/400
16/16 - 0s - loss: 0.2216 - accuracy: 0.9880 - val_loss: 0.2300 - val_accuracy: 0.9820 - 113ms/epoch - 7ms/step
Epoch 215/400
16/16 - 0s - loss: 0.2201 - accuracy: 0.9900 - val_loss: 0.2283 - val_accuracy: 0.9820 - 95ms/epoch - 6ms/step
Epoch 216/400
16/16 - 0s - loss: 0.2185 - accuracy: 0.9900 - val_loss: 0.2268 - val_accuracy: 0.9840 - 82ms/epoch - 5ms/step
Epoch 217/400
16/16 - 0s - loss: 0.2170 - accuracy: 0.9900 - val_loss: 0.2252 - val_accuracy: 0.9840 - 79ms/epoch - 5ms/step
Epoch 218/400
16/16 - 0s - loss: 0.2155 - accuracy: 0.9920 - val_loss: 0.2237 - val_accuracy: 0.9860 - 73ms/epoch - 5ms/step
Epoch 219/400
16/16 - 0s - loss: 0.2140 - accuracy: 0.9920 - val_loss: 0.2221 - val_accuracy: 0.9880 - 74ms/epoch - 5ms/step
Epoch 220/400
16/16 - 0s - loss: 0.2125 - accuracy: 0.9920 - val_loss: 0.2205 - val_accuracy: 0.9880 - 115ms/epoch - 7ms/step
Epoch 221/400
16/16 - 0s - loss: 0.2109 - accuracy: 0.9920 - val_loss: 0.2190 - val_accuracy: 0.9880 - 118ms/epoch - 7ms/step
Epoch 222/400
16/16 - 0s - loss: 0.2094 - accuracy: 0.9920 - val_loss: 0.2175 - val_accuracy: 0.9880 - 124ms/epoch - 8ms/step
Epoch 223/400
16/16 - 0s - loss: 0.2079 - accuracy: 0.9920 - val_loss: 0.2159 - val_accuracy: 0.9880 - 75ms/epoch - 5ms/step
Epoch 224/400
16/16 - 0s - loss: 0.2064 - accuracy: 0.9920 - val_loss: 0.2144 - val_accuracy: 0.9880 - 77ms/epoch - 5ms/step
Epoch 225/400
16/16 - 0s - loss: 0.2050 - accuracy: 0.9920 - val_loss: 0.2130 - val_accuracy: 0.9880 - 73ms/epoch - 5ms/step
Epoch 226/400
16/16 - 0s - loss: 0.2034 - accuracy: 0.9940 - val_loss: 0.2114 - val_accuracy: 0.9880 - 85ms/epoch - 5ms/step
Epoch 227/400
16/16 - 0s - loss: 0.2020 - accuracy: 0.9940 - val_loss: 0.2098 - val_accuracy: 0.9880 - 119ms/epoch - 7ms/step
Epoch 228/400
16/16 - 0s - loss: 0.2005 - accuracy: 0.9940 - val_loss: 0.2082 - val_accuracy: 0.9900 - 80ms/epoch - 5ms/step
Epoch 229/400
16/16 - 0s - loss: 0.1990 - accuracy: 0.9940 - val_loss: 0.2068 - val_accuracy: 0.9900 - 79ms/epoch - 5ms/step
Epoch 230/400
16/16 - 0s - loss: 0.1975 - accuracy: 0.9940 - val_loss: 0.2052 - val_accuracy: 0.9900 - 114ms/epoch - 7ms/step
Epoch 231/400
16/16 - 0s - loss: 0.1960 - accuracy: 0.9940 - val_loss: 0.2038 - val_accuracy: 0.9920 - 112ms/epoch - 7ms/step
Epoch 232/400
16/16 - 0s - loss: 0.1945 - accuracy: 0.9940 - val_loss: 0.2022 - val_accuracy: 0.9920 - 77ms/epoch - 5ms/step
Epoch 233/400
16/16 - 0s - loss: 0.1930 - accuracy: 0.9940 - val_loss: 0.2007 - val_accuracy: 0.9920 - 116ms/epoch - 7ms/step
Epoch 234/400
16/16 - 0s - loss: 0.1915 - accuracy: 0.9960 - val_loss: 0.1992 - val_accuracy: 0.9920 - 73ms/epoch - 5ms/step
Epoch 235/400
16/16 - 0s - loss: 0.1900 - accuracy: 0.9960 - val_loss: 0.1976 - val_accuracy: 0.9920 - 115ms/epoch - 7ms/step
Epoch 236/400
16/16 - 0s - loss: 0.1885 - accuracy: 0.9960 - val_loss: 0.1961 - val_accuracy: 0.9920 - 90ms/epoch - 6ms/step
Epoch 237/400
16/16 - 0s - loss: 0.1871 - accuracy: 0.9960 - val_loss: 0.1946 - val_accuracy: 0.9920 - 73ms/epoch - 5ms/step
Epoch 238/400
16/16 - 0s - loss: 0.1856 - accuracy: 0.9960 - val_loss: 0.1931 - val_accuracy: 0.9920 - 116ms/epoch - 7ms/step
Epoch 239/400
16/16 - 0s - loss: 0.1842 - accuracy: 0.9960 - val_loss: 0.1917 - val_accuracy: 0.9920 - 76ms/epoch - 5ms/step
Epoch 240/400
16/16 - 0s - loss: 0.1828 - accuracy: 0.9960 - val_loss: 0.1902 - val_accuracy: 0.9920 - 113ms/epoch - 7ms/step
Epoch 241/400
16/16 - 0s - loss: 0.1814 - accuracy: 0.9960 - val_loss: 0.1889 - val_accuracy: 0.9920 - 72ms/epoch - 4ms/step
Epoch 242/400
16/16 - 0s - loss: 0.1800 - accuracy: 0.9960 - val_loss: 0.1874 - val_accuracy: 0.9920 - 74ms/epoch - 5ms/step
Epoch 243/400
16/16 - 0s - loss: 0.1786 - accuracy: 0.9960 - val_loss: 0.1860 - val_accuracy: 0.9920 - 76ms/epoch - 5ms/step
Epoch 244/400
16/16 - 0s - loss: 0.1772 - accuracy: 0.9960 - val_loss: 0.1846 - val_accuracy: 0.9920 - 117ms/epoch - 7ms/step
Epoch 245/400
16/16 - 0s - loss: 0.1758 - accuracy: 0.9960 - val_loss: 0.1831 - val_accuracy: 0.9920 - 111ms/epoch - 7ms/step
Epoch 246/400
16/16 - 0s - loss: 0.1744 - accuracy: 0.9960 - val_loss: 0.1816 - val_accuracy: 0.9920 - 118ms/epoch - 7ms/step
Epoch 247/400
16/16 - 0s - loss: 0.1730 - accuracy: 0.9960 - val_loss: 0.1802 - val_accuracy: 0.9920 - 116ms/epoch - 7ms/step
Epoch 248/400
16/16 - 0s - loss: 0.1716 - accuracy: 0.9960 - val_loss: 0.1789 - val_accuracy: 0.9920 - 119ms/epoch - 7ms/step
Epoch 249/400
16/16 - 0s - loss: 0.1703 - accuracy: 0.9960 - val_loss: 0.1774 - val_accuracy: 0.9920 - 115ms/epoch - 7ms/step
Epoch 250/400
16/16 - 0s - loss: 0.1688 - accuracy: 0.9960 - val_loss: 0.1761 - val_accuracy: 0.9920 - 70ms/epoch - 4ms/step
Epoch 251/400
16/16 - 0s - loss: 0.1674 - accuracy: 0.9960 - val_loss: 0.1746 - val_accuracy: 0.9920 - 113ms/epoch - 7ms/step
Epoch 252/400
16/16 - 0s - loss: 0.1661 - accuracy: 0.9960 - val_loss: 0.1732 - val_accuracy: 0.9920 - 116ms/epoch - 7ms/step
Epoch 253/400
16/16 - 0s - loss: 0.1647 - accuracy: 0.9960 - val_loss: 0.1718 - val_accuracy: 0.9920 - 115ms/epoch - 7ms/step
Epoch 254/400
16/16 - 0s - loss: 0.1633 - accuracy: 0.9960 - val_loss: 0.1705 - val_accuracy: 0.9920 - 115ms/epoch - 7ms/step
Epoch 255/400
16/16 - 0s - loss: 0.1619 - accuracy: 0.9960 - val_loss: 0.1691 - val_accuracy: 0.9920 - 88ms/epoch - 5ms/step
Epoch 256/400
16/16 - 0s - loss: 0.1605 - accuracy: 0.9960 - val_loss: 0.1676 - val_accuracy: 0.9920 - 122ms/epoch - 8ms/step
Epoch 257/400
16/16 - 0s - loss: 0.1591 - accuracy: 0.9960 - val_loss: 0.1663 - val_accuracy: 0.9940 - 113ms/epoch - 7ms/step
Epoch 258/400
16/16 - 0s - loss: 0.1578 - accuracy: 0.9960 - val_loss: 0.1649 - val_accuracy: 0.9940 - 75ms/epoch - 5ms/step
Epoch 259/400
16/16 - 0s - loss: 0.1564 - accuracy: 0.9960 - val_loss: 0.1635 - val_accuracy: 0.9940 - 74ms/epoch - 5ms/step
Epoch 260/400
16/16 - 0s - loss: 0.1550 - accuracy: 0.9960 - val_loss: 0.1622 - val_accuracy: 0.9940 - 114ms/epoch - 7ms/step
Epoch 261/400
16/16 - 0s - loss: 0.1537 - accuracy: 0.9960 - val_loss: 0.1608 - val_accuracy: 0.9940 - 118ms/epoch - 7ms/step
Epoch 262/400
16/16 - 0s - loss: 0.1524 - accuracy: 0.9960 - val_loss: 0.1594 - val_accuracy: 0.9940 - 77ms/epoch - 5ms/step
Epoch 263/400
16/16 - 0s - loss: 0.1510 - accuracy: 0.9960 - val_loss: 0.1581 - val_accuracy: 0.9940 - 114ms/epoch - 7ms/step
Epoch 264/400
16/16 - 0s - loss: 0.1497 - accuracy: 0.9960 - val_loss: 0.1567 - val_accuracy: 0.9940 - 116ms/epoch - 7ms/step
Epoch 265/400
16/16 - 0s - loss: 0.1484 - accuracy: 0.9960 - val_loss: 0.1554 - val_accuracy: 0.9940 - 86ms/epoch - 5ms/step
Epoch 266/400
16/16 - 0s - loss: 0.1470 - accuracy: 0.9960 - val_loss: 0.1539 - val_accuracy: 0.9940 - 77ms/epoch - 5ms/step
Epoch 267/400
16/16 - 0s - loss: 0.1457 - accuracy: 0.9980 - val_loss: 0.1525 - val_accuracy: 0.9940 - 121ms/epoch - 8ms/step
Epoch 268/400
16/16 - 0s - loss: 0.1444 - accuracy: 1.0000 - val_loss: 0.1512 - val_accuracy: 0.9940 - 119ms/epoch - 7ms/step
Epoch 269/400
16/16 - 0s - loss: 0.1430 - accuracy: 1.0000 - val_loss: 0.1499 - val_accuracy: 0.9940 - 113ms/epoch - 7ms/step
Epoch 270/400
16/16 - 0s - loss: 0.1417 - accuracy: 1.0000 - val_loss: 0.1486 - val_accuracy: 0.9940 - 74ms/epoch - 5ms/step
Epoch 271/400
16/16 - 0s - loss: 0.1403 - accuracy: 1.0000 - val_loss: 0.1472 - val_accuracy: 0.9940 - 79ms/epoch - 5ms/step
Epoch 272/400
16/16 - 0s - loss: 0.1390 - accuracy: 1.0000 - val_loss: 0.1459 - val_accuracy: 0.9940 - 86ms/epoch - 5ms/step
Epoch 273/400
16/16 - 0s - loss: 0.1377 - accuracy: 1.0000 - val_loss: 0.1445 - val_accuracy: 0.9940 - 117ms/epoch - 7ms/step
Epoch 274/400
16/16 - 0s - loss: 0.1363 - accuracy: 1.0000 - val_loss: 0.1431 - val_accuracy: 0.9940 - 95ms/epoch - 6ms/step
Epoch 275/400
16/16 - 0s - loss: 0.1350 - accuracy: 1.0000 - val_loss: 0.1418 - val_accuracy: 0.9940 - 94ms/epoch - 6ms/step
Epoch 276/400
16/16 - 0s - loss: 0.1337 - accuracy: 1.0000 - val_loss: 0.1405 - val_accuracy: 0.9960 - 121ms/epoch - 8ms/step
Epoch 277/400
16/16 - 0s - loss: 0.1324 - accuracy: 1.0000 - val_loss: 0.1391 - val_accuracy: 0.9960 - 119ms/epoch - 7ms/step
Epoch 278/400
16/16 - 0s - loss: 0.1311 - accuracy: 1.0000 - val_loss: 0.1378 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 279/400
16/16 - 0s - loss: 0.1298 - accuracy: 1.0000 - val_loss: 0.1364 - val_accuracy: 0.9960 - 70ms/epoch - 4ms/step
Epoch 280/400
16/16 - 0s - loss: 0.1285 - accuracy: 1.0000 - val_loss: 0.1351 - val_accuracy: 0.9960 - 121ms/epoch - 8ms/step
Epoch 281/400
16/16 - 0s - loss: 0.1271 - accuracy: 1.0000 - val_loss: 0.1338 - val_accuracy: 0.9960 - 73ms/epoch - 5ms/step
Epoch 282/400
16/16 - 0s - loss: 0.1259 - accuracy: 1.0000 - val_loss: 0.1325 - val_accuracy: 0.9960 - 80ms/epoch - 5ms/step
Epoch 283/400
16/16 - 0s - loss: 0.1246 - accuracy: 1.0000 - val_loss: 0.1312 - val_accuracy: 0.9960 - 81ms/epoch - 5ms/step
Epoch 284/400
16/16 - 0s - loss: 0.1233 - accuracy: 1.0000 - val_loss: 0.1299 - val_accuracy: 0.9960 - 75ms/epoch - 5ms/step
Epoch 285/400
16/16 - 0s - loss: 0.1221 - accuracy: 1.0000 - val_loss: 0.1286 - val_accuracy: 0.9960 - 117ms/epoch - 7ms/step
Epoch 286/400
16/16 - 0s - loss: 0.1208 - accuracy: 1.0000 - val_loss: 0.1273 - val_accuracy: 0.9960 - 116ms/epoch - 7ms/step
Epoch 287/400
16/16 - 0s - loss: 0.1195 - accuracy: 1.0000 - val_loss: 0.1260 - val_accuracy: 0.9960 - 111ms/epoch - 7ms/step
Epoch 288/400
16/16 - 0s - loss: 0.1183 - accuracy: 1.0000 - val_loss: 0.1247 - val_accuracy: 0.9980 - 120ms/epoch - 7ms/step
Epoch 289/400
16/16 - 0s - loss: 0.1170 - accuracy: 1.0000 - val_loss: 0.1235 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 290/400
16/16 - 0s - loss: 0.1158 - accuracy: 1.0000 - val_loss: 0.1223 - val_accuracy: 0.9980 - 77ms/epoch - 5ms/step
Epoch 291/400
16/16 - 0s - loss: 0.1145 - accuracy: 1.0000 - val_loss: 0.1211 - val_accuracy: 0.9980 - 74ms/epoch - 5ms/step
Epoch 292/400
16/16 - 0s - loss: 0.1133 - accuracy: 1.0000 - val_loss: 0.1198 - val_accuracy: 0.9980 - 78ms/epoch - 5ms/step
Epoch 293/400
16/16 - 0s - loss: 0.1120 - accuracy: 1.0000 - val_loss: 0.1186 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 294/400
16/16 - 0s - loss: 0.1108 - accuracy: 1.0000 - val_loss: 0.1173 - val_accuracy: 0.9980 - 82ms/epoch - 5ms/step
Epoch 295/400
16/16 - 0s - loss: 0.1096 - accuracy: 1.0000 - val_loss: 0.1162 - val_accuracy: 0.9980 - 84ms/epoch - 5ms/step
Epoch 296/400
16/16 - 0s - loss: 0.1084 - accuracy: 1.0000 - val_loss: 0.1149 - val_accuracy: 0.9980 - 124ms/epoch - 8ms/step
Epoch 297/400
16/16 - 0s - loss: 0.1071 - accuracy: 1.0000 - val_loss: 0.1137 - val_accuracy: 0.9980 - 120ms/epoch - 7ms/step
Epoch 298/400
16/16 - 0s - loss: 0.1059 - accuracy: 1.0000 - val_loss: 0.1125 - val_accuracy: 0.9980 - 75ms/epoch - 5ms/step
Epoch 299/400
16/16 - 0s - loss: 0.1047 - accuracy: 1.0000 - val_loss: 0.1113 - val_accuracy: 0.9980 - 114ms/epoch - 7ms/step
Epoch 300/400
16/16 - 0s - loss: 0.1035 - accuracy: 1.0000 - val_loss: 0.1101 - val_accuracy: 0.9980 - 76ms/epoch - 5ms/step
Epoch 301/400
16/16 - 0s - loss: 0.1023 - accuracy: 1.0000 - val_loss: 0.1090 - val_accuracy: 0.9980 - 79ms/epoch - 5ms/step
Epoch 302/400
16/16 - 0s - loss: 0.1012 - accuracy: 1.0000 - val_loss: 0.1078 - val_accuracy: 0.9980 - 117ms/epoch - 7ms/step
Epoch 303/400
16/16 - 0s - loss: 0.1000 - accuracy: 1.0000 - val_loss: 0.1067 - val_accuracy: 0.9980 - 72ms/epoch - 5ms/step
Epoch 304/400
16/16 - 0s - loss: 0.0989 - accuracy: 1.0000 - val_loss: 0.1056 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 305/400
16/16 - 0s - loss: 0.0977 - accuracy: 1.0000 - val_loss: 0.1045 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 306/400
16/16 - 0s - loss: 0.0966 - accuracy: 1.0000 - val_loss: 0.1034 - val_accuracy: 1.0000 - 88ms/epoch - 6ms/step
Epoch 307/400
16/16 - 0s - loss: 0.0955 - accuracy: 1.0000 - val_loss: 0.1023 - val_accuracy: 1.0000 - 120ms/epoch - 8ms/step
Epoch 308/400
16/16 - 0s - loss: 0.0944 - accuracy: 1.0000 - val_loss: 0.1012 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 309/400
16/16 - 0s - loss: 0.0933 - accuracy: 1.0000 - val_loss: 0.1000 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 310/400
16/16 - 0s - loss: 0.0922 - accuracy: 1.0000 - val_loss: 0.0990 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 311/400
16/16 - 0s - loss: 0.0911 - accuracy: 1.0000 - val_loss: 0.0980 - val_accuracy: 1.0000 - 111ms/epoch - 7ms/step
Epoch 312/400
16/16 - 0s - loss: 0.0901 - accuracy: 1.0000 - val_loss: 0.0969 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 313/400
16/16 - 0s - loss: 0.0890 - accuracy: 1.0000 - val_loss: 0.0959 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 314/400
16/16 - 0s - loss: 0.0879 - accuracy: 1.0000 - val_loss: 0.0948 - val_accuracy: 1.0000 - 121ms/epoch - 8ms/step
Epoch 315/400
16/16 - 0s - loss: 0.0869 - accuracy: 1.0000 - val_loss: 0.0938 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 316/400
16/16 - 0s - loss: 0.0859 - accuracy: 1.0000 - val_loss: 0.0928 - val_accuracy: 1.0000 - 87ms/epoch - 5ms/step
Epoch 317/400
16/16 - 0s - loss: 0.0849 - accuracy: 1.0000 - val_loss: 0.0918 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 318/400
16/16 - 0s - loss: 0.0840 - accuracy: 1.0000 - val_loss: 0.0909 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 319/400
16/16 - 0s - loss: 0.0830 - accuracy: 1.0000 - val_loss: 0.0899 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 320/400
16/16 - 0s - loss: 0.0820 - accuracy: 1.0000 - val_loss: 0.0890 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 321/400
16/16 - 0s - loss: 0.0811 - accuracy: 1.0000 - val_loss: 0.0880 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 322/400
16/16 - 0s - loss: 0.0802 - accuracy: 1.0000 - val_loss: 0.0872 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 323/400
16/16 - 0s - loss: 0.0792 - accuracy: 1.0000 - val_loss: 0.0863 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 324/400
16/16 - 0s - loss: 0.0783 - accuracy: 1.0000 - val_loss: 0.0854 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 325/400
16/16 - 0s - loss: 0.0774 - accuracy: 1.0000 - val_loss: 0.0845 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 326/400
16/16 - 0s - loss: 0.0765 - accuracy: 1.0000 - val_loss: 0.0836 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 327/400
16/16 - 0s - loss: 0.0756 - accuracy: 1.0000 - val_loss: 0.0828 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 328/400
16/16 - 0s - loss: 0.0748 - accuracy: 1.0000 - val_loss: 0.0819 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 329/400
16/16 - 0s - loss: 0.0739 - accuracy: 1.0000 - val_loss: 0.0811 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 330/400
16/16 - 0s - loss: 0.0730 - accuracy: 1.0000 - val_loss: 0.0803 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 331/400
16/16 - 0s - loss: 0.0722 - accuracy: 1.0000 - val_loss: 0.0795 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 332/400
16/16 - 0s - loss: 0.0714 - accuracy: 1.0000 - val_loss: 0.0787 - val_accuracy: 1.0000 - 122ms/epoch - 8ms/step
Epoch 333/400
16/16 - 0s - loss: 0.0706 - accuracy: 1.0000 - val_loss: 0.0779 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 334/400
16/16 - 0s - loss: 0.0698 - accuracy: 1.0000 - val_loss: 0.0771 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 335/400
16/16 - 0s - loss: 0.0690 - accuracy: 1.0000 - val_loss: 0.0763 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 336/400
16/16 - 0s - loss: 0.0682 - accuracy: 1.0000 - val_loss: 0.0756 - val_accuracy: 1.0000 - 86ms/epoch - 5ms/step
Epoch 337/400
16/16 - 0s - loss: 0.0675 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 1.0000 - 82ms/epoch - 5ms/step
Epoch 338/400
16/16 - 0s - loss: 0.0667 - accuracy: 1.0000 - val_loss: 0.0742 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 339/400
16/16 - 0s - loss: 0.0660 - accuracy: 1.0000 - val_loss: 0.0734 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 340/400
16/16 - 0s - loss: 0.0653 - accuracy: 1.0000 - val_loss: 0.0727 - val_accuracy: 1.0000 - 123ms/epoch - 8ms/step
Epoch 341/400
16/16 - 0s - loss: 0.0646 - accuracy: 1.0000 - val_loss: 0.0720 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 342/400
16/16 - 0s - loss: 0.0639 - accuracy: 1.0000 - val_loss: 0.0713 - val_accuracy: 1.0000 - 84ms/epoch - 5ms/step
Epoch 343/400
16/16 - 0s - loss: 0.0631 - accuracy: 1.0000 - val_loss: 0.0706 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 344/400
16/16 - 0s - loss: 0.0625 - accuracy: 1.0000 - val_loss: 0.0700 - val_accuracy: 1.0000 - 88ms/epoch - 6ms/step
Epoch 345/400
16/16 - 0s - loss: 0.0618 - accuracy: 1.0000 - val_loss: 0.0693 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 346/400
16/16 - 0s - loss: 0.0611 - accuracy: 1.0000 - val_loss: 0.0686 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 347/400
16/16 - 0s - loss: 0.0605 - accuracy: 1.0000 - val_loss: 0.0680 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 348/400
16/16 - 0s - loss: 0.0598 - accuracy: 1.0000 - val_loss: 0.0673 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 349/400
16/16 - 0s - loss: 0.0592 - accuracy: 1.0000 - val_loss: 0.0667 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 350/400
16/16 - 0s - loss: 0.0585 - accuracy: 1.0000 - val_loss: 0.0661 - val_accuracy: 1.0000 - 70ms/epoch - 4ms/step
Epoch 351/400
16/16 - 0s - loss: 0.0579 - accuracy: 1.0000 - val_loss: 0.0655 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 352/400
16/16 - 0s - loss: 0.0573 - accuracy: 1.0000 - val_loss: 0.0649 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 353/400
16/16 - 0s - loss: 0.0567 - accuracy: 1.0000 - val_loss: 0.0643 - val_accuracy: 1.0000 - 81ms/epoch - 5ms/step
Epoch 354/400
16/16 - 0s - loss: 0.0561 - accuracy: 1.0000 - val_loss: 0.0637 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 355/400
16/16 - 0s - loss: 0.0555 - accuracy: 1.0000 - val_loss: 0.0632 - val_accuracy: 1.0000 - 128ms/epoch - 8ms/step
Epoch 356/400
16/16 - 0s - loss: 0.0549 - accuracy: 1.0000 - val_loss: 0.0626 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 357/400
16/16 - 0s - loss: 0.0544 - accuracy: 1.0000 - val_loss: 0.0620 - val_accuracy: 1.0000 - 75ms/epoch - 5ms/step
Epoch 358/400
16/16 - 0s - loss: 0.0538 - accuracy: 1.0000 - val_loss: 0.0615 - val_accuracy: 1.0000 - 117ms/epoch - 7ms/step
Epoch 359/400
16/16 - 0s - loss: 0.0532 - accuracy: 1.0000 - val_loss: 0.0610 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 360/400
16/16 - 0s - loss: 0.0527 - accuracy: 1.0000 - val_loss: 0.0604 - val_accuracy: 1.0000 - 112ms/epoch - 7ms/step
Epoch 361/400
16/16 - 0s - loss: 0.0522 - accuracy: 1.0000 - val_loss: 0.0599 - val_accuracy: 1.0000 - 84ms/epoch - 5ms/step
Epoch 362/400
16/16 - 0s - loss: 0.0516 - accuracy: 1.0000 - val_loss: 0.0594 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 363/400
16/16 - 0s - loss: 0.0511 - accuracy: 1.0000 - val_loss: 0.0588 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 364/400
16/16 - 0s - loss: 0.0506 - accuracy: 1.0000 - val_loss: 0.0584 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 365/400
16/16 - 0s - loss: 0.0501 - accuracy: 1.0000 - val_loss: 0.0579 - val_accuracy: 1.0000 - 78ms/epoch - 5ms/step
Epoch 366/400
16/16 - 0s - loss: 0.0496 - accuracy: 1.0000 - val_loss: 0.0573 - val_accuracy: 1.0000 - 119ms/epoch - 7ms/step
Epoch 367/400
16/16 - 0s - loss: 0.0491 - accuracy: 1.0000 - val_loss: 0.0568 - val_accuracy: 1.0000 - 127ms/epoch - 8ms/step
Epoch 368/400
16/16 - 0s - loss: 0.0486 - accuracy: 1.0000 - val_loss: 0.0563 - val_accuracy: 1.0000 - 73ms/epoch - 5ms/step
Epoch 369/400
16/16 - 0s - loss: 0.0481 - accuracy: 1.0000 - val_loss: 0.0559 - val_accuracy: 1.0000 - 124ms/epoch - 8ms/step
Epoch 370/400
16/16 - 0s - loss: 0.0476 - accuracy: 1.0000 - val_loss: 0.0554 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 371/400
16/16 - 0s - loss: 0.0472 - accuracy: 1.0000 - val_loss: 0.0549 - val_accuracy: 1.0000 - 74ms/epoch - 5ms/step
Epoch 372/400
16/16 - 0s - loss: 0.0467 - accuracy: 1.0000 - val_loss: 0.0545 - val_accuracy: 1.0000 - 114ms/epoch - 7ms/step
Epoch 373/400
16/16 - 0s - loss: 0.0463 - accuracy: 1.0000 - val_loss: 0.0541 - val_accuracy: 1.0000 - 72ms/epoch - 5ms/step
Epoch 374/400
16/16 - 0s - loss: 0.0458 - accuracy: 1.0000 - val_loss: 0.0536 - val_accuracy: 1.0000 - 77ms/epoch - 5ms/step
Epoch 375/400
16/16 - 0s - loss: 0.0454 - accuracy: 1.0000 - val_loss: 0.0531 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 376/400
16/16 - 0s - loss: 0.0449 - accuracy: 1.0000 - val_loss: 0.0527 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 377/400
16/16 - 0s - loss: 0.0445 - accuracy: 1.0000 - val_loss: 0.0523 - val_accuracy: 1.0000 - 80ms/epoch - 5ms/step
Epoch 378/400
16/16 - 0s - loss: 0.0441 - accuracy: 1.0000 - val_loss: 0.0519 - val_accuracy: 1.0000 - 73ms/epoch - 5ms/step
Epoch 379/400
16/16 - 0s - loss: 0.0437 - accuracy: 1.0000 - val_loss: 0.0515 - val_accuracy: 1.0000 - 79ms/epoch - 5ms/step
Epoch 380/400
16/16 - 0s - loss: 0.0432 - accuracy: 1.0000 - val_loss: 0.0511 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 381/400
16/16 - 0s - loss: 0.0429 - accuracy: 1.0000 - val_loss: 0.0507 - val_accuracy: 1.0000 - 113ms/epoch - 7ms/step
Epoch 382/400
16/16 - 0s - loss: 0.0425 - accuracy: 1.0000 - val_loss: 0.0503 - val_accuracy: 1.0000 - 118ms/epoch - 7ms/step
Epoch 383/400
16/16 - 0s - loss: 0.0421 - accuracy: 1.0000 - val_loss: 0.0499 - val_accuracy: 1.0000 - 76ms/epoch - 5ms/step
Epoch 384/400
16/16 - 0s - loss: 0.0417 - accuracy: 1.0000 - val_loss: 0.0495 - val_accuracy: 1.0000 - 90ms/epoch - 6ms/step
Epoch 385/400
16/16 - 0s - loss: 0.0413 - accuracy: 1.0000 - val_loss: 0.0491 - val_accuracy: 1.0000 - 115ms/epoch - 7ms/step
Epoch 386/400
16/16 - 0s - loss: 0.0409 - accuracy: 1.0000 - val_loss: 0.0487 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 387/400
16/16 - 0s - loss: 0.0405 - accuracy: 1.0000 - val_loss: 0.0483 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 388/400
16/16 - 0s - loss: 0.0402 - accuracy: 1.0000 - val_loss: 0.0479 - val_accuracy: 1.0000 - 116ms/epoch - 7ms/step
Epoch 389/400
16/16 - 0s - loss: 0.0398 - accuracy: 1.0000 - val_loss: 0.0476 - val_accuracy: 1.0000 - 86ms/epoch - 5ms/step
Epoch 390/400
16/16 - 0s - loss: 0.0395 - accuracy: 1.0000 - val_loss: 0.0472 - val_accuracy: 1.0000 - 142ms/epoch - 9ms/step
Epoch 391/400
16/16 - 0s - loss: 0.0391 - accuracy: 1.0000 - val_loss: 0.0468 - val_accuracy: 1.0000 - 140ms/epoch - 9ms/step
Epoch 392/400
16/16 - 0s - loss: 0.0387 - accuracy: 1.0000 - val_loss: 0.0465 - val_accuracy: 1.0000 - 149ms/epoch - 9ms/step
Epoch 393/400
16/16 - 0s - loss: 0.0384 - accuracy: 1.0000 - val_loss: 0.0461 - val_accuracy: 1.0000 - 161ms/epoch - 10ms/step
Epoch 394/400
16/16 - 0s - loss: 0.0381 - accuracy: 1.0000 - val_loss: 0.0458 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 395/400
16/16 - 0s - loss: 0.0377 - accuracy: 1.0000 - val_loss: 0.0454 - val_accuracy: 1.0000 - 135ms/epoch - 8ms/step
Epoch 396/400
16/16 - 0s - loss: 0.0374 - accuracy: 1.0000 - val_loss: 0.0451 - val_accuracy: 1.0000 - 147ms/epoch - 9ms/step
Epoch 397/400
16/16 - 0s - loss: 0.0371 - accuracy: 1.0000 - val_loss: 0.0448 - val_accuracy: 1.0000 - 104ms/epoch - 6ms/step
Epoch 398/400
16/16 - 0s - loss: 0.0367 - accuracy: 1.0000 - val_loss: 0.0444 - val_accuracy: 1.0000 - 109ms/epoch - 7ms/step
Epoch 399/400
16/16 - 0s - loss: 0.0364 - accuracy: 1.0000 - val_loss: 0.0441 - val_accuracy: 1.0000 - 110ms/epoch - 7ms/step
Epoch 400/400
16/16 - 0s - loss: 0.0361 - accuracy: 1.0000 - val_loss: 0.0438 - val_accuracy: 1.0000 - 141ms/epoch - 9ms/step
第5个弱分类器训练完毕

可视化结果

绘制训练曲线

#绘制每个基分类器的训练曲线
for i in range(number_of_weak_classifiers):
    plot_loss_accuracy(history[i],
                       f'{i+1} of {number_of_weak_classifiers} weak_classifiers with {num_of_res_blocks} residual_blocks with {num_hidden_layers_of_res_block} hidden_layers with {num_neurons_of_hidden_layer} neurons',
                       f'{i+1} of {number_of_weak_classifiers} weak_classifiers with {num_of_res_blocks} residual_blocks with {num_hidden_layers_of_res_block} hidden_layers with {num_neurons_of_hidden_layer} neurons')

输出模型结构信息

#打印模型结构和参数信息
boosting_model.summary()

Model: "model_9"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to
==================================================================================================
 input_1 (InputLayer)           [(None, 2)]          0           []

 0th-clf_0th-block_0th-hidden (  (None, 2)           6           ['input_1[0][0]']
 Dense)

 1th-clf_0th-block_0th-hidden (  (None, 2)           6           ['input_1[0][0]']
 Dense)

 2th-clf_0th-block_0th-hidden (  (None, 2)           6           ['input_1[0][0]']
 Dense)

 3th-clf_0th-block_0th-hidden (  (None, 2)           6           ['input_1[0][0]']
 Dense)

 4th-clf_0th-block_0th-hidden (  (None, 2)           6           ['input_1[0][0]']
 Dense)

 0th-clf_0th-resBlock_linear (D  (None, 2)           6           ['0th-clf_0th-block_0th-hidden[0]
 ense)                                                           [0]']

 1th-clf_0th-resBlock_linear (D  (None, 2)           6           ['1th-clf_0th-block_0th-hidden[0]
 ense)                                                           [0]']

 2th-clf_0th-resBlock_linear (D  (None, 2)           6           ['2th-clf_0th-block_0th-hidden[0]
 ense)                                                           [0]']

 3th-clf_0th-resBlock_linear (D  (None, 2)           6           ['3th-clf_0th-block_0th-hidden[0]
 ense)                                                           [0]']

 4th-clf_0th-resBlock_linear (D  (None, 2)           6           ['4th-clf_0th-block_0th-hidden[0]
 ense)                                                           [0]']

 0th-clf_0th-resBlock_Add (Add)  (None, 2)           0           ['0th-clf_0th-resBlock_linear[0][
                                                                 0]',
                                                                  'input_1[0][0]']

 1th-clf_0th-resBlock_Add (Add)  (None, 2)           0           ['1th-clf_0th-resBlock_linear[0][
                                                                 0]',
                                                                  'input_1[0][0]']

 2th-clf_0th-resBlock_Add (Add)  (None, 2)           0           ['2th-clf_0th-resBlock_linear[0][
                                                                 0]',
                                                                  'input_1[0][0]']

 3th-clf_0th-resBlock_Add (Add)  (None, 2)           0           ['3th-clf_0th-resBlock_linear[0][
                                                                 0]',
                                                                  'input_1[0][0]']

 4th-clf_0th-resBlock_Add (Add)  (None, 2)           0           ['4th-clf_0th-resBlock_linear[0][
                                                                 0]',
                                                                  'input_1[0][0]']

 classifiers_Add (Add)          (None, 2)            0           ['0th-clf_0th-resBlock_Add[0][0]'
                                                                 , '1th-clf_0th-resBlock_Add[0][0]
                                                                 ',
                                                                  '2th-clf_0th-resBlock_Add[0][0]'
                                                                 , '3th-clf_0th-resBlock_Add[0][0]
                                                                 ',
                                                                  '4th-clf_0th-resBlock_Add[0][0]'
                                                                 ]

 activation (Dense)             (None, 2)            6           ['classifiers_Add[0][0]']

==================================================================================================
Total params: 66
Trainable params: 6
Non-trainable params: 60
__________________________________________________________________________________________________

对原始特征空间剖分的可视化

#对原始特征空间剖分的可视化

#可视化原始特征空间
fig, ax1= plt.subplots(1,1, figsize=(7, 4),subplot_kw = {'aspect':'equal'})
mp = ax1.scatter(*X.T,c = y,cmap = cm_bright,edgecolors='white',s = 20,linewidths = 0.5)
#plt.colorbar()
plt.colorbar(mp,ax = [ax1])
plt.title(f'Raw data')
plt.savefig(f'Raw data.png')
plt.savefig(f'Raw data.pdf')

#可视化每叠加一个基分类器后的EGB模型对原始特征空间的剖分
for i in range(number_of_weak_classifiers):
    prob = boosting_models[i].predict(p)[:,1]
    fig, ax1= plt.subplots(1,1, figsize=(7, 4),subplot_kw = {'aspect':'equal'})
    ax1.scatter(*p.T,c = prob,cmap = cm_bright)
    mp = ax1.scatter(*X.T,c = y,cmap = cm_bright,edgecolors='white',s = 20,linewidths = 0.5)
    plt.colorbar(mp,ax = [ax1]);
    plt.title(f'Outputs of {i+1} classifiers')
    plt.savefig(f'Space division of {i+1} classifiers.png')
    plt.savefig(f'Space division of {i+1} classifiers.pdf')

#生成动图
def create_gif(image_list, gif_name, duration=1):
    frames = []
    for image_name in image_list:
        frames.append(imageio.imread(image_name))
    imageio.mimsave(gif_name, frames, 'GIF', duration=duration)
    return

def main():
    image_list = ['Raw data.png']
    for i in range(number_of_weak_classifiers):
        image_list.append(f'Space division of {i+1} classifiers.png')
    gif_name = '空间剖分动图.gif'
    duration = 0.8
    create_gif(image_list, gif_name, duration)

main()

79/79 [==============================] - 0s 2ms/step
79/79 [==============================] - 0s 1ms/step
79/79 [==============================] - 0s 1ms/step
79/79 [==============================] - 0s 2ms/step
79/79 [==============================] - 0s 2ms/step

更换背景显示原始样本

#更换背景显示原始样本

mpl.style.use('ggplot')

fig = plt.figure(figsize = (9,3))
top = cm.get_cmap('Oranges_r', 512)
bottom = cm.get_cmap('Blues', 512)
newcolors = np.vstack((top(np.linspace(0.55, 1, 512)),
                       bottom(np.linspace(0, 0.75, 512))))
cm_bright = ListedColormap(newcolors, name='OrangeBlue')

fig = plt.figure(figsize = (8,6))
m3 = plt.scatter(*X.T,c = y,cmap = cm_bright,edgecolors='white',s = 20,linewidths = 0.5)
plt.title(f'Raw data ({n_samples} points)')
plt.axis('equal')
plt.colorbar()
plt.savefig(f'Raw data ({n_samples} points)')
plt.savefig(f'Raw data ({n_samples} points).pdf')
plt.axis('equal')
plt.show()

<Figure size 648x216 with 0 Axes>

特征变换过程可视化

#对样本点特征变换的可视化

clf_add_layers = []
clf_add_layers.append(boosting_models[0].get_layer('0th-clf_0th-resBlock_Add'))
for i in range(len(boosting_models)-1):
    add_layer = boosting_models[i+1].get_layer('classifiers_Add')
    clf_add_layers.append(add_layer)

inp = boosting_model.input
outputs = [layer.output for layer in clf_add_layers]
print(outputs)
functors = [K.function([inp], [out]) for out in outputs]
boosting_model_outs = [func([X]) for func in functors]

#可视化每叠加一个基分类器后的EGB模型对样本点特征变换后的状态
for idx in range(len(boosting_model_outs)):
    fig = plt.figure(figsize = (8,6))
    plt.scatter(boosting_model_outs[idx][0][:,0],boosting_model_outs[idx][0][:,1],
            c = y,cmap = cm_bright,edgecolors='white',s = 30,linewidths = 0.1)
    plt.axis('equal')
    plt.title(f'Outputs of {idx+1} classifiers')
    plt.colorbar()
    plt.savefig(f'Outputs of {idx+1} classifiers.png')
    plt.savefig(f'Outputs of {idx+1} classifiers.pdf')
    plt.show()

#生成动图
def create_gif(image_list, gif_name, duration=1):
    frames = []
    for image_name in image_list:
        frames.append(imageio.imread(image_name))
    imageio.mimsave(gif_name, frames, 'GIF', duration=duration)
    return

def main():
    image_list = [f'Raw data ({n_samples} points).png']
    for i in range(len(boosting_model_outs)):
        image_list.append(f'Outputs of {i+1} classifiers.png')
    gif_name = '特征变换动图.gif'
    duration = 0.8
    create_gif(image_list, gif_name, duration)

main()

[<KerasTensor: shape=(None, 2) dtype=float32 (created by layer '0th-clf_0th-resBlock_Add')>, <KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'classifiers_Add')>, <KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'classifiers_Add')>, <KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'classifiers_Add')>, <KerasTensor: shape=(None, 2) dtype=float32 (created by layer 'classifiers_Add')>]