二、逻辑回归
logistic回归的keras实现
导入必要的模块
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
生成数据
定义数据生成函数
def create_data(data_num=500):
np.random.seed(251)
x1 = np.random.normal(0, 0.2, data_num)
x2 = np.random.normal(1, 0.2, data_num)
x = np.append(x1,x2)
y = np.array([0] * data_num + [1] * data_num)
return x, y
生成数据
x,y = create_data()
划分训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.2, random_state=16)
画出训练集数据的散点图
plt.scatter(x_train, y_train, color='r', label='train dataset')
plt.legend()
plt.show()
plt.scatter(x_test, y_test, color='b', label='test dataset')
plt.legend()
plt.show()
模型搭建
使用tf.keras.Sequential按顺序堆叠神经网络层，添加网络只要使用.add()函数即可。
使用到的api：
全连接操作tf.keras.layers.Dense
用到的参数：
-
input_dim：如果是第一个全连接层，需要设置输入层的大小。
-
units：输入整数，全连接层神经元个数。
-
activation：激活函数，二分类的输出通常使用’sigmoid’激活函数。
-
name：输入字符串，给改层设置一个名称。
模型设置tf.keras.Sequential.compile
用到的参数：
-
loss：损失函数，二分类任务使用"binary_crossentropy"。
-
optimizer：优化器，这里选用"sgd"，更多优化器请查看https://tensorflow.google.cn/api_docs/python/tf/keras/optimizers
-
metrics：评价指标，这里选用"accuracy"，更多优化器请查看https://tensorflow.google.cn/api_docs/python/tf/keras/metrics
model = Sequential()
# 全连接层
model.add(Dense(input_dim=1, units=1, activation='sigmoid', name='dense'))
# 设置损失函数loss、优化器optimizer、评价标准metrics
model.compile(loss='binary_crossentropy', optimizer='sgd', metrics=['accuracy'])
查看模型每层输出的shape和参数量
model.summary()
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 1) 2
=================================================================
Total params: 2
Trainable params: 2
Non-trainable params: 0
_________________________________________________________________
模型训练
使用到的api：
tf.keras.Sequential.fit
用到的参数：
-
x：输入数据。
-
y：输入标签。
-
batch_size：一次梯度更新使用的数据量。
-
epochs：数据集跑多少轮模型训练，一轮表示整个数据集训练一次。
-
validation_split：验证集占总数据量的比例，取值0~1。
-
shuffle：每轮训练是否打乱数据顺序，默认True。
返回：History对象，History.history属性会记录每一轮训练集和验证集的损失函数值和评价指标。
history = model.fit(x=x_train, y=y_train, batch_size=32, epochs=1000, validation_split=0.3, shuffle=True)
Train on 560 samples, validate on 240 samples
Epoch 1/1000
560/560 [==============================] - 0s 431us/sample - loss: 1.2721 - accuracy: 0.2393 - val_loss: 1.1875 - val_accuracy: 0.2417
Epoch 2/1000
560/560 [==============================] - 0s 44us/sample - loss: 1.2306 - accuracy: 0.2107 - val_loss: 1.1543 - val_accuracy: 0.2167
Epoch 3/1000
560/560 [==============================] - 0s 43us/sample - loss: 1.1922 - accuracy: 0.1929 - val_loss: 1.1221 - val_accuracy: 0.2042
Epoch 4/1000
560/560 [==============================] - 0s 45us/sample - loss: 1.1550 - accuracy: 0.1804 - val_loss: 1.0915 - val_accuracy: 0.1667
Epoch 5/1000
560/560 [==============================] - 0s 46us/sample - loss: 1.1198 - accuracy: 0.1589 - val_loss: 1.0633 - val_accuracy: 0.1375
Epoch 6/1000
560/560 [==============================] - 0s 48us/sample - loss: 1.0874 - accuracy: 0.1250 - val_loss: 1.0364 - val_accuracy: 0.1125
Epoch 7/1000
560/560 [==============================] - 0s 48us/sample - loss: 1.0564 - accuracy: 0.1089 - val_loss: 1.0112 - val_accuracy: 0.1083
Epoch 8/1000
560/560 [==============================] - 0s 45us/sample - loss: 1.0275 - accuracy: 0.1018 - val_loss: 0.9876 - val_accuracy: 0.0875
Epoch 9/1000
560/560 [==============================] - 0s 45us/sample - loss: 1.0004 - accuracy: 0.0893 - val_loss: 0.9651 - val_accuracy: 0.0708
Epoch 10/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.9748 - accuracy: 0.0732 - val_loss: 0.9441 - val_accuracy: 0.0583
Epoch 11/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.9508 - accuracy: 0.0589 - val_loss: 0.9242 - val_accuracy: 0.0375
Epoch 12/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.9282 - accuracy: 0.0500 - val_loss: 0.9056 - val_accuracy: 0.0292
Epoch 13/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.9071 - accuracy: 0.0304 - val_loss: 0.8880 - val_accuracy: 0.0208
Epoch 14/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.8871 - accuracy: 0.0232 - val_loss: 0.8711 - val_accuracy: 0.0167
Epoch 15/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.8681 - accuracy: 0.0179 - val_loss: 0.8553 - val_accuracy: 0.0083
Epoch 16/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.8504 - accuracy: 0.0071 - val_loss: 0.8404 - val_accuracy: 0.0000e+00
Epoch 17/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.8338 - accuracy: 0.0089 - val_loss: 0.8262 - val_accuracy: 0.0000e+00
Epoch 18/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.8181 - accuracy: 0.0107 - val_loss: 0.8126 - val_accuracy: 0.0083
Epoch 19/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.8031 - accuracy: 0.0286 - val_loss: 0.7997 - val_accuracy: 0.0292
Epoch 20/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.7889 - accuracy: 0.0625 - val_loss: 0.7873 - val_accuracy: 0.0833
Epoch 21/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.7753 - accuracy: 0.1357 - val_loss: 0.7753 - val_accuracy: 0.2083
Epoch 22/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.7624 - accuracy: 0.2768 - val_loss: 0.7639 - val_accuracy: 0.3667
Epoch 23/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.7501 - accuracy: 0.4429 - val_loss: 0.7529 - val_accuracy: 0.4500
Epoch 24/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.7383 - accuracy: 0.5071 - val_loss: 0.7423 - val_accuracy: 0.4583
Epoch 25/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.7269 - accuracy: 0.5125 - val_loss: 0.7320 - val_accuracy: 0.4583
Epoch 26/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.7160 - accuracy: 0.5125 - val_loss: 0.7221 - val_accuracy: 0.4583
Epoch 27/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.7055 - accuracy: 0.5125 - val_loss: 0.7125 - val_accuracy: 0.4583
Epoch 28/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.6954 - accuracy: 0.5125 - val_loss: 0.7032 - val_accuracy: 0.4583
Epoch 29/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.6857 - accuracy: 0.5125 - val_loss: 0.6941 - val_accuracy: 0.4583
Epoch 30/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.6762 - accuracy: 0.5125 - val_loss: 0.6852 - val_accuracy: 0.4583
Epoch 31/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.6671 - accuracy: 0.5125 - val_loss: 0.6766 - val_accuracy: 0.4583
Epoch 32/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.6583 - accuracy: 0.5125 - val_loss: 0.6682 - val_accuracy: 0.4583
Epoch 33/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.6497 - accuracy: 0.5125 - val_loss: 0.6600 - val_accuracy: 0.4583
Epoch 34/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.6413 - accuracy: 0.5125 - val_loss: 0.6520 - val_accuracy: 0.4583
Epoch 35/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.6332 - accuracy: 0.5143 - val_loss: 0.6442 - val_accuracy: 0.4667
Epoch 36/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.6253 - accuracy: 0.5214 - val_loss: 0.6365 - val_accuracy: 0.4792
Epoch 37/1000
560/560 [==============================] - ETA: 0s - loss: 0.6000 - accuracy: 0.62 - 0s 46us/sample - loss: 0.6175 - accuracy: 0.5357 - val_loss: 0.6291 - val_accuracy: 0.4875
Epoch 38/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.6100 - accuracy: 0.5500 - val_loss: 0.6217 - val_accuracy: 0.5042
Epoch 39/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.6027 - accuracy: 0.5679 - val_loss: 0.6146 - val_accuracy: 0.5208
Epoch 40/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5956 - accuracy: 0.5821 - val_loss: 0.6075 - val_accuracy: 0.5458
Epoch 41/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.5886 - accuracy: 0.6071 - val_loss: 0.6007 - val_accuracy: 0.5708
Epoch 42/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5817 - accuracy: 0.6304 - val_loss: 0.5940 - val_accuracy: 0.6042
Epoch 43/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5750 - accuracy: 0.6589 - val_loss: 0.5874 - val_accuracy: 0.6250
Epoch 44/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5685 - accuracy: 0.6821 - val_loss: 0.5810 - val_accuracy: 0.6500
Epoch 45/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5622 - accuracy: 0.7107 - val_loss: 0.5746 - val_accuracy: 0.6833
Epoch 46/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.5560 - accuracy: 0.7339 - val_loss: 0.5685 - val_accuracy: 0.7000
Epoch 47/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5499 - accuracy: 0.7554 - val_loss: 0.5624 - val_accuracy: 0.7292
Epoch 48/1000
560/560 [==============================] - ETA: 0s - loss: 0.4896 - accuracy: 0.87 - 0s 45us/sample - loss: 0.5439 - accuracy: 0.7607 - val_loss: 0.5564 - val_accuracy: 0.7500
Epoch 49/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.5381 - accuracy: 0.7768 - val_loss: 0.5504 - val_accuracy: 0.7750
Epoch 50/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.5323 - accuracy: 0.7929 - val_loss: 0.5447 - val_accuracy: 0.7833
Epoch 51/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.5267 - accuracy: 0.8089 - val_loss: 0.5390 - val_accuracy: 0.7958
Epoch 52/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5212 - accuracy: 0.8179 - val_loss: 0.5334 - val_accuracy: 0.8042
Epoch 53/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5159 - accuracy: 0.8268 - val_loss: 0.5280 - val_accuracy: 0.8458
Epoch 54/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.5106 - accuracy: 0.8393 - val_loss: 0.5226 - val_accuracy: 0.8542
Epoch 55/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5054 - accuracy: 0.8500 - val_loss: 0.5174 - val_accuracy: 0.8667
Epoch 56/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.5004 - accuracy: 0.8696 - val_loss: 0.5122 - val_accuracy: 0.8833
Epoch 57/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.4954 - accuracy: 0.8821 - val_loss: 0.5072 - val_accuracy: 0.8875
Epoch 58/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.4905 - accuracy: 0.8857 - val_loss: 0.5022 - val_accuracy: 0.8875
Epoch 59/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.4858 - accuracy: 0.8911 - val_loss: 0.4973 - val_accuracy: 0.8875
Epoch 60/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.4811 - accuracy: 0.8929 - val_loss: 0.4926 - val_accuracy: 0.8875
Epoch 61/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.4765 - accuracy: 0.8964 - val_loss: 0.4878 - val_accuracy: 0.9000
Epoch 62/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.4719 - accuracy: 0.9000 - val_loss: 0.4832 - val_accuracy: 0.9000
Epoch 63/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4675 - accuracy: 0.9054 - val_loss: 0.4787 - val_accuracy: 0.9042
Epoch 64/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4632 - accuracy: 0.9054 - val_loss: 0.4742 - val_accuracy: 0.9125
Epoch 65/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4589 - accuracy: 0.9089 - val_loss: 0.4697 - val_accuracy: 0.9125
Epoch 66/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4547 - accuracy: 0.9089 - val_loss: 0.4654 - val_accuracy: 0.9167
Epoch 67/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4506 - accuracy: 0.9161 - val_loss: 0.4611 - val_accuracy: 0.9208
Epoch 68/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4465 - accuracy: 0.9214 - val_loss: 0.4570 - val_accuracy: 0.9292
Epoch 69/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4426 - accuracy: 0.9232 - val_loss: 0.4529 - val_accuracy: 0.9292
Epoch 70/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.4386 - accuracy: 0.9286 - val_loss: 0.4489 - val_accuracy: 0.9292
Epoch 71/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4348 - accuracy: 0.9304 - val_loss: 0.4449 - val_accuracy: 0.9375
Epoch 72/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4310 - accuracy: 0.9339 - val_loss: 0.4410 - val_accuracy: 0.9375
Epoch 73/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.4273 - accuracy: 0.9357 - val_loss: 0.4371 - val_accuracy: 0.9417
Epoch 74/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4237 - accuracy: 0.9375 - val_loss: 0.4333 - val_accuracy: 0.9417
Epoch 75/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4201 - accuracy: 0.9393 - val_loss: 0.4296 - val_accuracy: 0.9500
Epoch 76/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4166 - accuracy: 0.9393 - val_loss: 0.4259 - val_accuracy: 0.9542
Epoch 77/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4131 - accuracy: 0.9429 - val_loss: 0.4223 - val_accuracy: 0.9583
Epoch 78/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4097 - accuracy: 0.9446 - val_loss: 0.4188 - val_accuracy: 0.9583
Epoch 79/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.4063 - accuracy: 0.9464 - val_loss: 0.4153 - val_accuracy: 0.9583
Epoch 80/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.4031 - accuracy: 0.9464 - val_loss: 0.4119 - val_accuracy: 0.9583
Epoch 81/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3998 - accuracy: 0.9482 - val_loss: 0.4086 - val_accuracy: 0.9583
Epoch 82/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3966 - accuracy: 0.9482 - val_loss: 0.4053 - val_accuracy: 0.9625
Epoch 83/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3935 - accuracy: 0.9482 - val_loss: 0.4020 - val_accuracy: 0.9667
Epoch 84/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3904 - accuracy: 0.9482 - val_loss: 0.3988 - val_accuracy: 0.9667
Epoch 85/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3874 - accuracy: 0.9500 - val_loss: 0.3956 - val_accuracy: 0.9667
Epoch 86/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3844 - accuracy: 0.9500 - val_loss: 0.3925 - val_accuracy: 0.9667
Epoch 87/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3815 - accuracy: 0.9500 - val_loss: 0.3895 - val_accuracy: 0.9667
Epoch 88/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3786 - accuracy: 0.9518 - val_loss: 0.3865 - val_accuracy: 0.9667
Epoch 89/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3758 - accuracy: 0.9500 - val_loss: 0.3835 - val_accuracy: 0.9667
Epoch 90/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3730 - accuracy: 0.9536 - val_loss: 0.3805 - val_accuracy: 0.9667
Epoch 91/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3702 - accuracy: 0.9589 - val_loss: 0.3777 - val_accuracy: 0.9667
Epoch 92/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3675 - accuracy: 0.9607 - val_loss: 0.3748 - val_accuracy: 0.9667
Epoch 93/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3648 - accuracy: 0.9625 - val_loss: 0.3721 - val_accuracy: 0.9708
Epoch 94/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.3622 - accuracy: 0.9625 - val_loss: 0.3693 - val_accuracy: 0.9708
Epoch 95/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3596 - accuracy: 0.9625 - val_loss: 0.3666 - val_accuracy: 0.9750
Epoch 96/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3571 - accuracy: 0.9625 - val_loss: 0.3639 - val_accuracy: 0.9750
Epoch 97/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3545 - accuracy: 0.9643 - val_loss: 0.3613 - val_accuracy: 0.9750
Epoch 98/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3521 - accuracy: 0.9661 - val_loss: 0.3587 - val_accuracy: 0.9750
Epoch 99/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3496 - accuracy: 0.9661 - val_loss: 0.3562 - val_accuracy: 0.9750
Epoch 100/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3473 - accuracy: 0.9679 - val_loss: 0.3537 - val_accuracy: 0.9750
Epoch 101/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3449 - accuracy: 0.9696 - val_loss: 0.3512 - val_accuracy: 0.9750
Epoch 102/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3425 - accuracy: 0.9714 - val_loss: 0.3488 - val_accuracy: 0.9750
Epoch 103/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3402 - accuracy: 0.9732 - val_loss: 0.3464 - val_accuracy: 0.9750
Epoch 104/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3380 - accuracy: 0.9732 - val_loss: 0.3440 - val_accuracy: 0.9750
Epoch 105/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3358 - accuracy: 0.9732 - val_loss: 0.3417 - val_accuracy: 0.9750
Epoch 106/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3336 - accuracy: 0.9732 - val_loss: 0.3394 - val_accuracy: 0.9750
Epoch 107/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3314 - accuracy: 0.9732 - val_loss: 0.3372 - val_accuracy: 0.9750
Epoch 108/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3293 - accuracy: 0.9732 - val_loss: 0.3349 - val_accuracy: 0.9750
Epoch 109/1000
560/560 [==============================] - ETA: 0s - loss: 0.3337 - accuracy: 1.00 - 0s 45us/sample - loss: 0.3272 - accuracy: 0.9732 - val_loss: 0.3327 - val_accuracy: 0.9750
Epoch 110/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3251 - accuracy: 0.9732 - val_loss: 0.3306 - val_accuracy: 0.9750
Epoch 111/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3231 - accuracy: 0.9732 - val_loss: 0.3284 - val_accuracy: 0.9792
Epoch 112/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3211 - accuracy: 0.9732 - val_loss: 0.3263 - val_accuracy: 0.9792
Epoch 113/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3191 - accuracy: 0.9732 - val_loss: 0.3242 - val_accuracy: 0.9792
Epoch 114/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3171 - accuracy: 0.9732 - val_loss: 0.3222 - val_accuracy: 0.9792
Epoch 115/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3152 - accuracy: 0.9732 - val_loss: 0.3202 - val_accuracy: 0.9792
Epoch 116/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3133 - accuracy: 0.9732 - val_loss: 0.3182 - val_accuracy: 0.9792
Epoch 117/1000
560/560 [==============================] - ETA: 0s - loss: 0.2653 - accuracy: 1.00 - 0s 43us/sample - loss: 0.3114 - accuracy: 0.9732 - val_loss: 0.3162 - val_accuracy: 0.9792
Epoch 118/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3095 - accuracy: 0.9732 - val_loss: 0.3143 - val_accuracy: 0.9792
Epoch 119/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.3077 - accuracy: 0.9732 - val_loss: 0.3123 - val_accuracy: 0.9792
Epoch 120/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3059 - accuracy: 0.9750 - val_loss: 0.3104 - val_accuracy: 0.9792
Epoch 121/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3041 - accuracy: 0.9768 - val_loss: 0.3085 - val_accuracy: 0.9792
Epoch 122/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.3023 - accuracy: 0.9786 - val_loss: 0.3067 - val_accuracy: 0.9792
Epoch 123/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.3006 - accuracy: 0.9786 - val_loss: 0.3049 - val_accuracy: 0.9792
Epoch 124/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2989 - accuracy: 0.9786 - val_loss: 0.3031 - val_accuracy: 0.9792
Epoch 125/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2972 - accuracy: 0.9786 - val_loss: 0.3013 - val_accuracy: 0.9833
Epoch 126/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2955 - accuracy: 0.9786 - val_loss: 0.2995 - val_accuracy: 0.9833
Epoch 127/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2938 - accuracy: 0.9786 - val_loss: 0.2978 - val_accuracy: 0.9833
Epoch 128/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2922 - accuracy: 0.9786 - val_loss: 0.2961 - val_accuracy: 0.9833
Epoch 129/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2906 - accuracy: 0.9786 - val_loss: 0.2944 - val_accuracy: 0.9833
Epoch 130/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2890 - accuracy: 0.9786 - val_loss: 0.2928 - val_accuracy: 0.9833
Epoch 131/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2874 - accuracy: 0.9786 - val_loss: 0.2911 - val_accuracy: 0.9833
Epoch 132/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2859 - accuracy: 0.9786 - val_loss: 0.2895 - val_accuracy: 0.9833
Epoch 133/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2844 - accuracy: 0.9786 - val_loss: 0.2878 - val_accuracy: 0.9833
Epoch 134/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2828 - accuracy: 0.9786 - val_loss: 0.2862 - val_accuracy: 0.9833
Epoch 135/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2813 - accuracy: 0.9786 - val_loss: 0.2847 - val_accuracy: 0.9833
Epoch 136/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2799 - accuracy: 0.9804 - val_loss: 0.2831 - val_accuracy: 0.9833
Epoch 137/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2784 - accuracy: 0.9804 - val_loss: 0.2816 - val_accuracy: 0.9833
Epoch 138/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2770 - accuracy: 0.9804 - val_loss: 0.2801 - val_accuracy: 0.9833
Epoch 139/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2755 - accuracy: 0.9804 - val_loss: 0.2785 - val_accuracy: 0.9833
Epoch 140/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2741 - accuracy: 0.9804 - val_loss: 0.2771 - val_accuracy: 0.9833
Epoch 141/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2727 - accuracy: 0.9821 - val_loss: 0.2756 - val_accuracy: 0.9833
Epoch 142/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2714 - accuracy: 0.9821 - val_loss: 0.2742 - val_accuracy: 0.9833
Epoch 143/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.2700 - accuracy: 0.9821 - val_loss: 0.2727 - val_accuracy: 0.9833
Epoch 144/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2686 - accuracy: 0.9821 - val_loss: 0.2713 - val_accuracy: 0.9833
Epoch 145/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2673 - accuracy: 0.9821 - val_loss: 0.2699 - val_accuracy: 0.9833
Epoch 146/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2660 - accuracy: 0.9821 - val_loss: 0.2685 - val_accuracy: 0.9833
Epoch 147/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2647 - accuracy: 0.9821 - val_loss: 0.2672 - val_accuracy: 0.9833
Epoch 148/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2634 - accuracy: 0.9821 - val_loss: 0.2658 - val_accuracy: 0.9833
Epoch 149/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2621 - accuracy: 0.9821 - val_loss: 0.2645 - val_accuracy: 0.9833
Epoch 150/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2609 - accuracy: 0.9821 - val_loss: 0.2632 - val_accuracy: 0.9833
Epoch 151/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2597 - accuracy: 0.9821 - val_loss: 0.2618 - val_accuracy: 0.9833
Epoch 152/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2584 - accuracy: 0.9821 - val_loss: 0.2605 - val_accuracy: 0.9833
Epoch 153/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2572 - accuracy: 0.9839 - val_loss: 0.2592 - val_accuracy: 0.9833
Epoch 154/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2560 - accuracy: 0.9821 - val_loss: 0.2580 - val_accuracy: 0.9833
Epoch 155/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2548 - accuracy: 0.9821 - val_loss: 0.2567 - val_accuracy: 0.9833
Epoch 156/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2537 - accuracy: 0.9821 - val_loss: 0.2555 - val_accuracy: 0.9833
Epoch 157/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2525 - accuracy: 0.9821 - val_loss: 0.2543 - val_accuracy: 0.9833
Epoch 158/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2513 - accuracy: 0.9821 - val_loss: 0.2531 - val_accuracy: 0.9833
Epoch 159/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2502 - accuracy: 0.9821 - val_loss: 0.2519 - val_accuracy: 0.9833
Epoch 160/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2491 - accuracy: 0.9821 - val_loss: 0.2507 - val_accuracy: 0.9833
Epoch 161/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2480 - accuracy: 0.9821 - val_loss: 0.2495 - val_accuracy: 0.9833
Epoch 162/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2469 - accuracy: 0.9821 - val_loss: 0.2483 - val_accuracy: 0.9833
Epoch 163/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2458 - accuracy: 0.9821 - val_loss: 0.2472 - val_accuracy: 0.9833
Epoch 164/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2447 - accuracy: 0.9821 - val_loss: 0.2460 - val_accuracy: 0.9833
Epoch 165/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2436 - accuracy: 0.9821 - val_loss: 0.2449 - val_accuracy: 0.9833
Epoch 166/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2426 - accuracy: 0.9821 - val_loss: 0.2438 - val_accuracy: 0.9833
Epoch 167/1000
560/560 [==============================] - ETA: 0s - loss: 0.2411 - accuracy: 0.96 - 0s 43us/sample - loss: 0.2415 - accuracy: 0.9821 - val_loss: 0.2427 - val_accuracy: 0.9833
Epoch 168/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2405 - accuracy: 0.9821 - val_loss: 0.2416 - val_accuracy: 0.9833
Epoch 169/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2395 - accuracy: 0.9821 - val_loss: 0.2405 - val_accuracy: 0.9833
Epoch 170/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2384 - accuracy: 0.9821 - val_loss: 0.2395 - val_accuracy: 0.9833
Epoch 171/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2374 - accuracy: 0.9821 - val_loss: 0.2384 - val_accuracy: 0.9833
Epoch 172/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2364 - accuracy: 0.9821 - val_loss: 0.2374 - val_accuracy: 0.9833
Epoch 173/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2355 - accuracy: 0.9821 - val_loss: 0.2363 - val_accuracy: 0.9833
Epoch 174/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2345 - accuracy: 0.9821 - val_loss: 0.2353 - val_accuracy: 0.9833
Epoch 175/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2335 - accuracy: 0.9821 - val_loss: 0.2343 - val_accuracy: 0.9833
Epoch 176/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2326 - accuracy: 0.9821 - val_loss: 0.2333 - val_accuracy: 0.9833
Epoch 177/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2316 - accuracy: 0.9821 - val_loss: 0.2323 - val_accuracy: 0.9833
Epoch 178/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2307 - accuracy: 0.9821 - val_loss: 0.2313 - val_accuracy: 0.9833
Epoch 179/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.2298 - accuracy: 0.9821 - val_loss: 0.2303 - val_accuracy: 0.9833
Epoch 180/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2288 - accuracy: 0.9821 - val_loss: 0.2293 - val_accuracy: 0.9833
Epoch 181/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2279 - accuracy: 0.9821 - val_loss: 0.2284 - val_accuracy: 0.9833
Epoch 182/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2270 - accuracy: 0.9821 - val_loss: 0.2274 - val_accuracy: 0.9833
Epoch 183/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2261 - accuracy: 0.9821 - val_loss: 0.2265 - val_accuracy: 0.9833
Epoch 184/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2252 - accuracy: 0.9821 - val_loss: 0.2255 - val_accuracy: 0.9833
Epoch 185/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2244 - accuracy: 0.9821 - val_loss: 0.2246 - val_accuracy: 0.9833
Epoch 186/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2235 - accuracy: 0.9821 - val_loss: 0.2237 - val_accuracy: 0.9833
Epoch 187/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2226 - accuracy: 0.9821 - val_loss: 0.2228 - val_accuracy: 0.9833
Epoch 188/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2218 - accuracy: 0.9821 - val_loss: 0.2219 - val_accuracy: 0.9833
Epoch 189/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2209 - accuracy: 0.9821 - val_loss: 0.2210 - val_accuracy: 0.9833
Epoch 190/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2201 - accuracy: 0.9821 - val_loss: 0.2201 - val_accuracy: 0.9833
Epoch 191/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2193 - accuracy: 0.9821 - val_loss: 0.2192 - val_accuracy: 0.9875
Epoch 192/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2185 - accuracy: 0.9821 - val_loss: 0.2184 - val_accuracy: 0.9875
Epoch 193/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2177 - accuracy: 0.9821 - val_loss: 0.2175 - val_accuracy: 0.9875
Epoch 194/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2168 - accuracy: 0.9821 - val_loss: 0.2167 - val_accuracy: 0.9875
Epoch 195/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.2160 - accuracy: 0.9821 - val_loss: 0.2158 - val_accuracy: 0.9875
Epoch 196/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2153 - accuracy: 0.9821 - val_loss: 0.2150 - val_accuracy: 0.9875
Epoch 197/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2145 - accuracy: 0.9821 - val_loss: 0.2141 - val_accuracy: 0.9875
Epoch 198/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2137 - accuracy: 0.9839 - val_loss: 0.2133 - val_accuracy: 0.9875
Epoch 199/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2129 - accuracy: 0.9839 - val_loss: 0.2125 - val_accuracy: 0.9875
Epoch 200/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2122 - accuracy: 0.9839 - val_loss: 0.2117 - val_accuracy: 0.9875
Epoch 201/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2114 - accuracy: 0.9839 - val_loss: 0.2109 - val_accuracy: 0.9875
Epoch 202/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2107 - accuracy: 0.9839 - val_loss: 0.2101 - val_accuracy: 0.9875
Epoch 203/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.2099 - accuracy: 0.9839 - val_loss: 0.2093 - val_accuracy: 0.9875
Epoch 204/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2092 - accuracy: 0.9839 - val_loss: 0.2086 - val_accuracy: 0.9875
Epoch 205/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2084 - accuracy: 0.9857 - val_loss: 0.2078 - val_accuracy: 0.9875
Epoch 206/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2077 - accuracy: 0.9857 - val_loss: 0.2070 - val_accuracy: 0.9875
Epoch 207/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2070 - accuracy: 0.9857 - val_loss: 0.2063 - val_accuracy: 0.9875
Epoch 208/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.2063 - accuracy: 0.9857 - val_loss: 0.2055 - val_accuracy: 0.9875
Epoch 209/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2056 - accuracy: 0.9857 - val_loss: 0.2047 - val_accuracy: 0.9875
Epoch 210/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2049 - accuracy: 0.9857 - val_loss: 0.2040 - val_accuracy: 0.9875
Epoch 211/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2042 - accuracy: 0.9857 - val_loss: 0.2033 - val_accuracy: 0.9875
Epoch 212/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.2035 - accuracy: 0.9857 - val_loss: 0.2025 - val_accuracy: 0.9875
Epoch 213/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2028 - accuracy: 0.9857 - val_loss: 0.2018 - val_accuracy: 0.9875
Epoch 214/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2021 - accuracy: 0.9857 - val_loss: 0.2011 - val_accuracy: 0.9875
Epoch 215/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.2014 - accuracy: 0.9857 - val_loss: 0.2004 - val_accuracy: 0.9875
Epoch 216/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.2008 - accuracy: 0.9857 - val_loss: 0.1997 - val_accuracy: 0.9875
Epoch 217/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.2001 - accuracy: 0.9857 - val_loss: 0.1990 - val_accuracy: 0.9875
Epoch 218/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1995 - accuracy: 0.9857 - val_loss: 0.1983 - val_accuracy: 0.9875
Epoch 219/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1988 - accuracy: 0.9857 - val_loss: 0.1976 - val_accuracy: 0.9875
Epoch 220/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1982 - accuracy: 0.9857 - val_loss: 0.1969 - val_accuracy: 0.9875
Epoch 221/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1975 - accuracy: 0.9857 - val_loss: 0.1962 - val_accuracy: 0.9875
Epoch 222/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1969 - accuracy: 0.9857 - val_loss: 0.1956 - val_accuracy: 0.9875
Epoch 223/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1963 - accuracy: 0.9857 - val_loss: 0.1949 - val_accuracy: 0.9875
Epoch 224/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1956 - accuracy: 0.9857 - val_loss: 0.1943 - val_accuracy: 0.9875
Epoch 225/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1950 - accuracy: 0.9857 - val_loss: 0.1936 - val_accuracy: 0.9875
Epoch 226/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1944 - accuracy: 0.9857 - val_loss: 0.1930 - val_accuracy: 0.9875
Epoch 227/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1938 - accuracy: 0.9857 - val_loss: 0.1923 - val_accuracy: 0.9875
Epoch 228/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1932 - accuracy: 0.9857 - val_loss: 0.1917 - val_accuracy: 0.9875
Epoch 229/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1926 - accuracy: 0.9857 - val_loss: 0.1910 - val_accuracy: 0.9875
Epoch 230/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1920 - accuracy: 0.9857 - val_loss: 0.1904 - val_accuracy: 0.9875
Epoch 231/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1914 - accuracy: 0.9857 - val_loss: 0.1898 - val_accuracy: 0.9875
Epoch 232/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1908 - accuracy: 0.9857 - val_loss: 0.1892 - val_accuracy: 0.9875
Epoch 233/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1902 - accuracy: 0.9857 - val_loss: 0.1885 - val_accuracy: 0.9875
Epoch 234/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1897 - accuracy: 0.9857 - val_loss: 0.1879 - val_accuracy: 0.9875
Epoch 235/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1891 - accuracy: 0.9857 - val_loss: 0.1873 - val_accuracy: 0.9875
Epoch 236/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1885 - accuracy: 0.9857 - val_loss: 0.1867 - val_accuracy: 0.9875
Epoch 237/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1879 - accuracy: 0.9857 - val_loss: 0.1861 - val_accuracy: 0.9875
Epoch 238/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1874 - accuracy: 0.9857 - val_loss: 0.1855 - val_accuracy: 0.9875
Epoch 239/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1868 - accuracy: 0.9857 - val_loss: 0.1849 - val_accuracy: 0.9875
Epoch 240/1000
560/560 [==============================] - ETA: 0s - loss: 0.2015 - accuracy: 0.93 - 0s 43us/sample - loss: 0.1863 - accuracy: 0.9857 - val_loss: 0.1843 - val_accuracy: 0.9875
Epoch 241/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1857 - accuracy: 0.9857 - val_loss: 0.1838 - val_accuracy: 0.9875
Epoch 242/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1852 - accuracy: 0.9857 - val_loss: 0.1832 - val_accuracy: 0.9917
Epoch 243/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1847 - accuracy: 0.9857 - val_loss: 0.1826 - val_accuracy: 0.9917
Epoch 244/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1841 - accuracy: 0.9857 - val_loss: 0.1820 - val_accuracy: 0.9917
Epoch 245/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1836 - accuracy: 0.9857 - val_loss: 0.1815 - val_accuracy: 0.9917
Epoch 246/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1831 - accuracy: 0.9857 - val_loss: 0.1809 - val_accuracy: 0.9917
Epoch 247/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1825 - accuracy: 0.9857 - val_loss: 0.1804 - val_accuracy: 0.9917
Epoch 248/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1820 - accuracy: 0.9857 - val_loss: 0.1798 - val_accuracy: 0.9917
Epoch 249/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1815 - accuracy: 0.9857 - val_loss: 0.1793 - val_accuracy: 0.9917
Epoch 250/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1810 - accuracy: 0.9857 - val_loss: 0.1787 - val_accuracy: 0.9917
Epoch 251/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1805 - accuracy: 0.9857 - val_loss: 0.1782 - val_accuracy: 0.9917
Epoch 252/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1800 - accuracy: 0.9857 - val_loss: 0.1776 - val_accuracy: 0.9917
Epoch 253/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1795 - accuracy: 0.9857 - val_loss: 0.1771 - val_accuracy: 0.9917
Epoch 254/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1790 - accuracy: 0.9857 - val_loss: 0.1766 - val_accuracy: 0.9917
Epoch 255/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1785 - accuracy: 0.9857 - val_loss: 0.1761 - val_accuracy: 0.9917
Epoch 256/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1780 - accuracy: 0.9857 - val_loss: 0.1755 - val_accuracy: 0.9917
Epoch 257/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1775 - accuracy: 0.9857 - val_loss: 0.1750 - val_accuracy: 0.9917
Epoch 258/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1770 - accuracy: 0.9857 - val_loss: 0.1745 - val_accuracy: 0.9917
Epoch 259/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1765 - accuracy: 0.9857 - val_loss: 0.1740 - val_accuracy: 0.9917
Epoch 260/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1761 - accuracy: 0.9857 - val_loss: 0.1735 - val_accuracy: 0.9917
Epoch 261/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1756 - accuracy: 0.9857 - val_loss: 0.1730 - val_accuracy: 0.9917
Epoch 262/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1751 - accuracy: 0.9857 - val_loss: 0.1725 - val_accuracy: 0.9917
Epoch 263/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1746 - accuracy: 0.9857 - val_loss: 0.1720 - val_accuracy: 0.9917
Epoch 264/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1742 - accuracy: 0.9857 - val_loss: 0.1715 - val_accuracy: 0.9917
Epoch 265/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1737 - accuracy: 0.9857 - val_loss: 0.1710 - val_accuracy: 0.9917
Epoch 266/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1733 - accuracy: 0.9857 - val_loss: 0.1705 - val_accuracy: 0.9917
Epoch 267/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1728 - accuracy: 0.9857 - val_loss: 0.1700 - val_accuracy: 0.9917
Epoch 268/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1723 - accuracy: 0.9857 - val_loss: 0.1696 - val_accuracy: 0.9917
Epoch 269/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1719 - accuracy: 0.9857 - val_loss: 0.1691 - val_accuracy: 0.9917
Epoch 270/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1714 - accuracy: 0.9857 - val_loss: 0.1686 - val_accuracy: 0.9917
Epoch 271/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1710 - accuracy: 0.9857 - val_loss: 0.1681 - val_accuracy: 0.9917
Epoch 272/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1706 - accuracy: 0.9857 - val_loss: 0.1677 - val_accuracy: 0.9917
Epoch 273/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1701 - accuracy: 0.9857 - val_loss: 0.1672 - val_accuracy: 0.9917
Epoch 274/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1697 - accuracy: 0.9857 - val_loss: 0.1668 - val_accuracy: 0.9917
Epoch 275/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1693 - accuracy: 0.9857 - val_loss: 0.1663 - val_accuracy: 0.9917
Epoch 276/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1688 - accuracy: 0.9857 - val_loss: 0.1658 - val_accuracy: 0.9917
Epoch 277/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1684 - accuracy: 0.9857 - val_loss: 0.1654 - val_accuracy: 0.9917
Epoch 278/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1680 - accuracy: 0.9857 - val_loss: 0.1649 - val_accuracy: 0.9917
Epoch 279/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1676 - accuracy: 0.9857 - val_loss: 0.1645 - val_accuracy: 0.9917
Epoch 280/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1671 - accuracy: 0.9857 - val_loss: 0.1640 - val_accuracy: 0.9917
Epoch 281/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1667 - accuracy: 0.9857 - val_loss: 0.1636 - val_accuracy: 0.9917
Epoch 282/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1663 - accuracy: 0.9857 - val_loss: 0.1632 - val_accuracy: 0.9917
Epoch 283/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1659 - accuracy: 0.9857 - val_loss: 0.1627 - val_accuracy: 0.9917
Epoch 284/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1655 - accuracy: 0.9857 - val_loss: 0.1623 - val_accuracy: 0.9917
Epoch 285/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1651 - accuracy: 0.9857 - val_loss: 0.1619 - val_accuracy: 0.9917
Epoch 286/1000
560/560 [==============================] - 0s 50us/sample - loss: 0.1647 - accuracy: 0.9857 - val_loss: 0.1614 - val_accuracy: 0.9917
Epoch 287/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1643 - accuracy: 0.9857 - val_loss: 0.1610 - val_accuracy: 0.9917
Epoch 288/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1639 - accuracy: 0.9857 - val_loss: 0.1606 - val_accuracy: 0.9917
Epoch 289/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1635 - accuracy: 0.9857 - val_loss: 0.1602 - val_accuracy: 0.9917
Epoch 290/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1631 - accuracy: 0.9857 - val_loss: 0.1598 - val_accuracy: 0.9917
Epoch 291/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1627 - accuracy: 0.9857 - val_loss: 0.1594 - val_accuracy: 0.9917
Epoch 292/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1623 - accuracy: 0.9857 - val_loss: 0.1589 - val_accuracy: 0.9917
Epoch 293/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1619 - accuracy: 0.9857 - val_loss: 0.1585 - val_accuracy: 0.9917
Epoch 294/1000
560/560 [==============================] - 0s 39us/sample - loss: 0.1616 - accuracy: 0.9857 - val_loss: 0.1581 - val_accuracy: 0.9917
Epoch 295/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1612 - accuracy: 0.9857 - val_loss: 0.1577 - val_accuracy: 0.9917
Epoch 296/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1608 - accuracy: 0.9857 - val_loss: 0.1573 - val_accuracy: 0.9917
Epoch 297/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1604 - accuracy: 0.9857 - val_loss: 0.1569 - val_accuracy: 0.9917
Epoch 298/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1601 - accuracy: 0.9857 - val_loss: 0.1566 - val_accuracy: 0.9917
Epoch 299/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1597 - accuracy: 0.9857 - val_loss: 0.1562 - val_accuracy: 0.9917
Epoch 300/1000
560/560 [==============================] - 0s 42us/sample - loss: 0.1593 - accuracy: 0.9857 - val_loss: 0.1558 - val_accuracy: 0.9917
Epoch 301/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1590 - accuracy: 0.9857 - val_loss: 0.1554 - val_accuracy: 0.9917
Epoch 302/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1586 - accuracy: 0.9857 - val_loss: 0.1550 - val_accuracy: 0.9917
Epoch 303/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1582 - accuracy: 0.9857 - val_loss: 0.1546 - val_accuracy: 0.9917
Epoch 304/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1579 - accuracy: 0.9857 - val_loss: 0.1542 - val_accuracy: 0.9917
Epoch 305/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1575 - accuracy: 0.9857 - val_loss: 0.1538 - val_accuracy: 0.9917
Epoch 306/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1572 - accuracy: 0.9857 - val_loss: 0.1535 - val_accuracy: 0.9917
Epoch 307/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1568 - accuracy: 0.9857 - val_loss: 0.1531 - val_accuracy: 0.9917
Epoch 308/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1565 - accuracy: 0.9857 - val_loss: 0.1527 - val_accuracy: 0.9917
Epoch 309/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1561 - accuracy: 0.9857 - val_loss: 0.1524 - val_accuracy: 0.9917
Epoch 310/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1558 - accuracy: 0.9857 - val_loss: 0.1520 - val_accuracy: 0.9917
Epoch 311/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1554 - accuracy: 0.9857 - val_loss: 0.1516 - val_accuracy: 0.9917
Epoch 312/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1551 - accuracy: 0.9857 - val_loss: 0.1513 - val_accuracy: 0.9917
Epoch 313/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1547 - accuracy: 0.9857 - val_loss: 0.1509 - val_accuracy: 0.9917
Epoch 314/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1544 - accuracy: 0.9857 - val_loss: 0.1505 - val_accuracy: 0.9917
Epoch 315/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1541 - accuracy: 0.9857 - val_loss: 0.1502 - val_accuracy: 0.9917
Epoch 316/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1537 - accuracy: 0.9857 - val_loss: 0.1498 - val_accuracy: 0.9917
Epoch 317/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1534 - accuracy: 0.9857 - val_loss: 0.1495 - val_accuracy: 0.9917
Epoch 318/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1530 - accuracy: 0.9857 - val_loss: 0.1491 - val_accuracy: 0.9917
Epoch 319/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1527 - accuracy: 0.9857 - val_loss: 0.1488 - val_accuracy: 0.9917
Epoch 320/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1524 - accuracy: 0.9857 - val_loss: 0.1484 - val_accuracy: 0.9917
Epoch 321/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1521 - accuracy: 0.9857 - val_loss: 0.1481 - val_accuracy: 0.9917
Epoch 322/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1517 - accuracy: 0.9857 - val_loss: 0.1477 - val_accuracy: 0.9917
Epoch 323/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1514 - accuracy: 0.9857 - val_loss: 0.1474 - val_accuracy: 0.9917
Epoch 324/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1511 - accuracy: 0.9857 - val_loss: 0.1470 - val_accuracy: 0.9917
Epoch 325/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1508 - accuracy: 0.9857 - val_loss: 0.1467 - val_accuracy: 0.9917
Epoch 326/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1505 - accuracy: 0.9857 - val_loss: 0.1464 - val_accuracy: 0.9917
Epoch 327/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1501 - accuracy: 0.9857 - val_loss: 0.1460 - val_accuracy: 0.9917
Epoch 328/1000
560/560 [==============================] - 0s 39us/sample - loss: 0.1498 - accuracy: 0.9857 - val_loss: 0.1457 - val_accuracy: 0.9917
Epoch 329/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1495 - accuracy: 0.9857 - val_loss: 0.1454 - val_accuracy: 0.9917
Epoch 330/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1492 - accuracy: 0.9857 - val_loss: 0.1450 - val_accuracy: 0.9917
Epoch 331/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1489 - accuracy: 0.9857 - val_loss: 0.1447 - val_accuracy: 0.9917
Epoch 332/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1486 - accuracy: 0.9857 - val_loss: 0.1444 - val_accuracy: 0.9917
Epoch 333/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1483 - accuracy: 0.9857 - val_loss: 0.1440 - val_accuracy: 0.9917
Epoch 334/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1480 - accuracy: 0.9857 - val_loss: 0.1437 - val_accuracy: 0.9917
Epoch 335/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1477 - accuracy: 0.9857 - val_loss: 0.1434 - val_accuracy: 0.9917
Epoch 336/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1474 - accuracy: 0.9857 - val_loss: 0.1431 - val_accuracy: 0.9917
Epoch 337/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1471 - accuracy: 0.9857 - val_loss: 0.1428 - val_accuracy: 0.9917
Epoch 338/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1468 - accuracy: 0.9857 - val_loss: 0.1424 - val_accuracy: 0.9917
Epoch 339/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1465 - accuracy: 0.9857 - val_loss: 0.1421 - val_accuracy: 0.9917
Epoch 340/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1462 - accuracy: 0.9857 - val_loss: 0.1418 - val_accuracy: 0.9917
Epoch 341/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1459 - accuracy: 0.9857 - val_loss: 0.1415 - val_accuracy: 0.9917
Epoch 342/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1456 - accuracy: 0.9857 - val_loss: 0.1412 - val_accuracy: 0.9917
Epoch 343/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1453 - accuracy: 0.9857 - val_loss: 0.1409 - val_accuracy: 0.9917
Epoch 344/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1450 - accuracy: 0.9857 - val_loss: 0.1406 - val_accuracy: 0.9917
Epoch 345/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1448 - accuracy: 0.9857 - val_loss: 0.1403 - val_accuracy: 0.9917
Epoch 346/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1445 - accuracy: 0.9857 - val_loss: 0.1400 - val_accuracy: 0.9917
Epoch 347/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1442 - accuracy: 0.9857 - val_loss: 0.1397 - val_accuracy: 0.9917
Epoch 348/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1439 - accuracy: 0.9857 - val_loss: 0.1394 - val_accuracy: 0.9917
Epoch 349/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1436 - accuracy: 0.9857 - val_loss: 0.1391 - val_accuracy: 0.9917
Epoch 350/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1433 - accuracy: 0.9857 - val_loss: 0.1388 - val_accuracy: 0.9917
Epoch 351/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1431 - accuracy: 0.9857 - val_loss: 0.1385 - val_accuracy: 0.9917
Epoch 352/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1428 - accuracy: 0.9857 - val_loss: 0.1382 - val_accuracy: 0.9917
Epoch 353/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1425 - accuracy: 0.9875 - val_loss: 0.1379 - val_accuracy: 0.9917
Epoch 354/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1422 - accuracy: 0.9893 - val_loss: 0.1376 - val_accuracy: 0.9917
Epoch 355/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1420 - accuracy: 0.9875 - val_loss: 0.1373 - val_accuracy: 0.9917
Epoch 356/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1417 - accuracy: 0.9893 - val_loss: 0.1370 - val_accuracy: 0.9917
Epoch 357/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1414 - accuracy: 0.9875 - val_loss: 0.1368 - val_accuracy: 0.9917
Epoch 358/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1412 - accuracy: 0.9893 - val_loss: 0.1365 - val_accuracy: 0.9917
Epoch 359/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1409 - accuracy: 0.9893 - val_loss: 0.1362 - val_accuracy: 0.9917
Epoch 360/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1406 - accuracy: 0.9893 - val_loss: 0.1359 - val_accuracy: 0.9917
Epoch 361/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1404 - accuracy: 0.9893 - val_loss: 0.1356 - val_accuracy: 0.9917
Epoch 362/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1401 - accuracy: 0.9893 - val_loss: 0.1354 - val_accuracy: 0.9917
Epoch 363/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1398 - accuracy: 0.9893 - val_loss: 0.1351 - val_accuracy: 0.9917
Epoch 364/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1396 - accuracy: 0.9893 - val_loss: 0.1348 - val_accuracy: 0.9917
Epoch 365/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1393 - accuracy: 0.9893 - val_loss: 0.1345 - val_accuracy: 0.9917
Epoch 366/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1391 - accuracy: 0.9893 - val_loss: 0.1343 - val_accuracy: 0.9917
Epoch 367/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1388 - accuracy: 0.9893 - val_loss: 0.1340 - val_accuracy: 0.9917
Epoch 368/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1386 - accuracy: 0.9893 - val_loss: 0.1337 - val_accuracy: 0.9917
Epoch 369/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1383 - accuracy: 0.9893 - val_loss: 0.1334 - val_accuracy: 0.9917
Epoch 370/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1381 - accuracy: 0.9893 - val_loss: 0.1332 - val_accuracy: 0.9917
Epoch 371/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1378 - accuracy: 0.9893 - val_loss: 0.1329 - val_accuracy: 0.9917
Epoch 372/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1376 - accuracy: 0.9893 - val_loss: 0.1327 - val_accuracy: 0.9917
Epoch 373/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1373 - accuracy: 0.9893 - val_loss: 0.1324 - val_accuracy: 0.9917
Epoch 374/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1371 - accuracy: 0.9893 - val_loss: 0.1321 - val_accuracy: 0.9917
Epoch 375/1000
560/560 [==============================] - 0s 42us/sample - loss: 0.1368 - accuracy: 0.9893 - val_loss: 0.1319 - val_accuracy: 0.9917
Epoch 376/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1366 - accuracy: 0.9893 - val_loss: 0.1316 - val_accuracy: 0.9917
Epoch 377/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1363 - accuracy: 0.9893 - val_loss: 0.1314 - val_accuracy: 0.9917
Epoch 378/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1361 - accuracy: 0.9893 - val_loss: 0.1311 - val_accuracy: 0.9917
Epoch 379/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1358 - accuracy: 0.9893 - val_loss: 0.1308 - val_accuracy: 0.9917
Epoch 380/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1356 - accuracy: 0.9893 - val_loss: 0.1306 - val_accuracy: 0.9917
Epoch 381/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1354 - accuracy: 0.9893 - val_loss: 0.1303 - val_accuracy: 0.9917
Epoch 382/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1351 - accuracy: 0.9893 - val_loss: 0.1301 - val_accuracy: 0.9917
Epoch 383/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1349 - accuracy: 0.9893 - val_loss: 0.1298 - val_accuracy: 0.9917
Epoch 384/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.1347 - accuracy: 0.9893 - val_loss: 0.1296 - val_accuracy: 0.9917
Epoch 385/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1344 - accuracy: 0.9893 - val_loss: 0.1293 - val_accuracy: 0.9917
Epoch 386/1000
560/560 [==============================] - ETA: 0s - loss: 0.1357 - accuracy: 0.96 - 0s 43us/sample - loss: 0.1342 - accuracy: 0.9893 - val_loss: 0.1291 - val_accuracy: 0.9917
Epoch 387/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1340 - accuracy: 0.9893 - val_loss: 0.1288 - val_accuracy: 0.9917
Epoch 388/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1337 - accuracy: 0.9893 - val_loss: 0.1286 - val_accuracy: 0.9917
Epoch 389/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1335 - accuracy: 0.9893 - val_loss: 0.1283 - val_accuracy: 0.9917
Epoch 390/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1333 - accuracy: 0.9893 - val_loss: 0.1281 - val_accuracy: 0.9917
Epoch 391/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1330 - accuracy: 0.9893 - val_loss: 0.1279 - val_accuracy: 0.9917
Epoch 392/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1328 - accuracy: 0.9893 - val_loss: 0.1276 - val_accuracy: 0.9917
Epoch 393/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1326 - accuracy: 0.9893 - val_loss: 0.1274 - val_accuracy: 0.9917
Epoch 394/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1324 - accuracy: 0.9893 - val_loss: 0.1271 - val_accuracy: 0.9917
Epoch 395/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1322 - accuracy: 0.9893 - val_loss: 0.1269 - val_accuracy: 0.9917
Epoch 396/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1319 - accuracy: 0.9893 - val_loss: 0.1267 - val_accuracy: 0.9917
Epoch 397/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1317 - accuracy: 0.9893 - val_loss: 0.1264 - val_accuracy: 0.9917
Epoch 398/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1315 - accuracy: 0.9893 - val_loss: 0.1262 - val_accuracy: 0.9917
Epoch 399/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1313 - accuracy: 0.9893 - val_loss: 0.1260 - val_accuracy: 0.9917
Epoch 400/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1310 - accuracy: 0.9893 - val_loss: 0.1257 - val_accuracy: 0.9917
Epoch 401/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1308 - accuracy: 0.9893 - val_loss: 0.1255 - val_accuracy: 0.9917
Epoch 402/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1306 - accuracy: 0.9893 - val_loss: 0.1253 - val_accuracy: 0.9917
Epoch 403/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1304 - accuracy: 0.9893 - val_loss: 0.1251 - val_accuracy: 0.9917
Epoch 404/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1302 - accuracy: 0.9893 - val_loss: 0.1248 - val_accuracy: 0.9917
Epoch 405/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1300 - accuracy: 0.9893 - val_loss: 0.1246 - val_accuracy: 0.9917
Epoch 406/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1298 - accuracy: 0.9893 - val_loss: 0.1244 - val_accuracy: 0.9917
Epoch 407/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1296 - accuracy: 0.9893 - val_loss: 0.1241 - val_accuracy: 0.9917
Epoch 408/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1293 - accuracy: 0.9893 - val_loss: 0.1239 - val_accuracy: 0.9917
Epoch 409/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1291 - accuracy: 0.9893 - val_loss: 0.1237 - val_accuracy: 0.9917
Epoch 410/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1289 - accuracy: 0.9893 - val_loss: 0.1235 - val_accuracy: 0.9917
Epoch 411/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1287 - accuracy: 0.9893 - val_loss: 0.1233 - val_accuracy: 0.9917
Epoch 412/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1285 - accuracy: 0.9893 - val_loss: 0.1230 - val_accuracy: 0.9917
Epoch 413/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1283 - accuracy: 0.9893 - val_loss: 0.1228 - val_accuracy: 0.9917
Epoch 414/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.1281 - accuracy: 0.9893 - val_loss: 0.1226 - val_accuracy: 0.9917
Epoch 415/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1279 - accuracy: 0.9893 - val_loss: 0.1224 - val_accuracy: 0.9917
Epoch 416/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1277 - accuracy: 0.9893 - val_loss: 0.1222 - val_accuracy: 0.9917
Epoch 417/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1275 - accuracy: 0.9893 - val_loss: 0.1220 - val_accuracy: 0.9917
Epoch 418/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1273 - accuracy: 0.9893 - val_loss: 0.1217 - val_accuracy: 0.9917
Epoch 419/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1271 - accuracy: 0.9893 - val_loss: 0.1215 - val_accuracy: 0.9917
Epoch 420/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1269 - accuracy: 0.9893 - val_loss: 0.1213 - val_accuracy: 0.9917
Epoch 421/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1267 - accuracy: 0.9893 - val_loss: 0.1211 - val_accuracy: 0.9917
Epoch 422/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1265 - accuracy: 0.9893 - val_loss: 0.1209 - val_accuracy: 0.9917
Epoch 423/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1263 - accuracy: 0.9893 - val_loss: 0.1207 - val_accuracy: 0.9917
Epoch 424/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1261 - accuracy: 0.9893 - val_loss: 0.1205 - val_accuracy: 0.9917
Epoch 425/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1259 - accuracy: 0.9893 - val_loss: 0.1203 - val_accuracy: 0.9917
Epoch 426/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1257 - accuracy: 0.9893 - val_loss: 0.1201 - val_accuracy: 0.9917
Epoch 427/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1255 - accuracy: 0.9893 - val_loss: 0.1199 - val_accuracy: 0.9917
Epoch 428/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1253 - accuracy: 0.9893 - val_loss: 0.1197 - val_accuracy: 0.9917
Epoch 429/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1251 - accuracy: 0.9893 - val_loss: 0.1195 - val_accuracy: 0.9917
Epoch 430/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1249 - accuracy: 0.9893 - val_loss: 0.1192 - val_accuracy: 0.9917
Epoch 431/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1247 - accuracy: 0.9893 - val_loss: 0.1190 - val_accuracy: 0.9917
Epoch 432/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1246 - accuracy: 0.9893 - val_loss: 0.1189 - val_accuracy: 0.9917
Epoch 433/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1244 - accuracy: 0.9893 - val_loss: 0.1187 - val_accuracy: 0.9917
Epoch 434/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1242 - accuracy: 0.9893 - val_loss: 0.1184 - val_accuracy: 0.9917
Epoch 435/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1240 - accuracy: 0.9893 - val_loss: 0.1182 - val_accuracy: 0.9917
Epoch 436/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1238 - accuracy: 0.9893 - val_loss: 0.1180 - val_accuracy: 0.9917
Epoch 437/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1236 - accuracy: 0.9893 - val_loss: 0.1179 - val_accuracy: 0.9917
Epoch 438/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1234 - accuracy: 0.9893 - val_loss: 0.1177 - val_accuracy: 0.9917
Epoch 439/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1233 - accuracy: 0.9893 - val_loss: 0.1175 - val_accuracy: 0.9917
Epoch 440/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1231 - accuracy: 0.9893 - val_loss: 0.1173 - val_accuracy: 0.9917
Epoch 441/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1229 - accuracy: 0.9893 - val_loss: 0.1171 - val_accuracy: 0.9917
Epoch 442/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1227 - accuracy: 0.9893 - val_loss: 0.1169 - val_accuracy: 0.9917
Epoch 443/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1225 - accuracy: 0.9893 - val_loss: 0.1167 - val_accuracy: 0.9917
Epoch 444/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1223 - accuracy: 0.9893 - val_loss: 0.1165 - val_accuracy: 0.9917
Epoch 445/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1222 - accuracy: 0.9893 - val_loss: 0.1163 - val_accuracy: 0.9917
Epoch 446/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1220 - accuracy: 0.9893 - val_loss: 0.1161 - val_accuracy: 0.9917
Epoch 447/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1218 - accuracy: 0.9893 - val_loss: 0.1159 - val_accuracy: 0.9917
Epoch 448/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1216 - accuracy: 0.9893 - val_loss: 0.1157 - val_accuracy: 0.9917
Epoch 449/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1214 - accuracy: 0.9893 - val_loss: 0.1155 - val_accuracy: 0.9917
Epoch 450/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1213 - accuracy: 0.9893 - val_loss: 0.1153 - val_accuracy: 0.9917
Epoch 451/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1211 - accuracy: 0.9893 - val_loss: 0.1152 - val_accuracy: 0.9917
Epoch 452/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1209 - accuracy: 0.9893 - val_loss: 0.1150 - val_accuracy: 0.9917
Epoch 453/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1207 - accuracy: 0.9893 - val_loss: 0.1148 - val_accuracy: 0.9917
Epoch 454/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1206 - accuracy: 0.9893 - val_loss: 0.1146 - val_accuracy: 0.9917
Epoch 455/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1204 - accuracy: 0.9893 - val_loss: 0.1144 - val_accuracy: 0.9917
Epoch 456/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1202 - accuracy: 0.9893 - val_loss: 0.1142 - val_accuracy: 0.9917
Epoch 457/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1201 - accuracy: 0.9893 - val_loss: 0.1141 - val_accuracy: 0.9917
Epoch 458/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1199 - accuracy: 0.9893 - val_loss: 0.1139 - val_accuracy: 0.9917
Epoch 459/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1197 - accuracy: 0.9893 - val_loss: 0.1137 - val_accuracy: 0.9917
Epoch 460/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1196 - accuracy: 0.9893 - val_loss: 0.1135 - val_accuracy: 0.9917
Epoch 461/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1194 - accuracy: 0.9893 - val_loss: 0.1133 - val_accuracy: 0.9917
Epoch 462/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1192 - accuracy: 0.9893 - val_loss: 0.1132 - val_accuracy: 0.9917
Epoch 463/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1190 - accuracy: 0.9893 - val_loss: 0.1130 - val_accuracy: 0.9917
Epoch 464/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1189 - accuracy: 0.9893 - val_loss: 0.1128 - val_accuracy: 0.9917
Epoch 465/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.1187 - accuracy: 0.9893 - val_loss: 0.1126 - val_accuracy: 0.9917
Epoch 466/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.1186 - accuracy: 0.9893 - val_loss: 0.1124 - val_accuracy: 0.9917
Epoch 467/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1184 - accuracy: 0.9893 - val_loss: 0.1123 - val_accuracy: 0.9917
Epoch 468/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1182 - accuracy: 0.9893 - val_loss: 0.1121 - val_accuracy: 0.9917
Epoch 469/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1181 - accuracy: 0.9893 - val_loss: 0.1119 - val_accuracy: 0.9917
Epoch 470/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1179 - accuracy: 0.9893 - val_loss: 0.1117 - val_accuracy: 0.9917
Epoch 471/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1177 - accuracy: 0.9893 - val_loss: 0.1116 - val_accuracy: 0.9917
Epoch 472/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1176 - accuracy: 0.9893 - val_loss: 0.1114 - val_accuracy: 0.9917
Epoch 473/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1174 - accuracy: 0.9893 - val_loss: 0.1112 - val_accuracy: 0.9917
Epoch 474/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1173 - accuracy: 0.9893 - val_loss: 0.1111 - val_accuracy: 0.9917
Epoch 475/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1171 - accuracy: 0.9893 - val_loss: 0.1109 - val_accuracy: 0.9917
Epoch 476/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1169 - accuracy: 0.9893 - val_loss: 0.1107 - val_accuracy: 0.9917
Epoch 477/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1168 - accuracy: 0.9893 - val_loss: 0.1106 - val_accuracy: 0.9917
Epoch 478/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1166 - accuracy: 0.9893 - val_loss: 0.1104 - val_accuracy: 0.9917
Epoch 479/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1165 - accuracy: 0.9893 - val_loss: 0.1102 - val_accuracy: 0.9917
Epoch 480/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1163 - accuracy: 0.9893 - val_loss: 0.1101 - val_accuracy: 0.9917
Epoch 481/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1161 - accuracy: 0.9893 - val_loss: 0.1099 - val_accuracy: 0.9917
Epoch 482/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1160 - accuracy: 0.9893 - val_loss: 0.1097 - val_accuracy: 0.9917
Epoch 483/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1158 - accuracy: 0.9893 - val_loss: 0.1096 - val_accuracy: 0.9917
Epoch 484/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1157 - accuracy: 0.9893 - val_loss: 0.1094 - val_accuracy: 0.9917
Epoch 485/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1155 - accuracy: 0.9893 - val_loss: 0.1092 - val_accuracy: 0.9917
Epoch 486/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1154 - accuracy: 0.9893 - val_loss: 0.1091 - val_accuracy: 0.9917
Epoch 487/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1152 - accuracy: 0.9893 - val_loss: 0.1089 - val_accuracy: 0.9917
Epoch 488/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1151 - accuracy: 0.9893 - val_loss: 0.1087 - val_accuracy: 0.9917
Epoch 489/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1149 - accuracy: 0.9893 - val_loss: 0.1086 - val_accuracy: 0.9917
Epoch 490/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1148 - accuracy: 0.9893 - val_loss: 0.1084 - val_accuracy: 0.9917
Epoch 491/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1146 - accuracy: 0.9893 - val_loss: 0.1083 - val_accuracy: 0.9917
Epoch 492/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1145 - accuracy: 0.9893 - val_loss: 0.1081 - val_accuracy: 0.9917
Epoch 493/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1143 - accuracy: 0.9893 - val_loss: 0.1080 - val_accuracy: 0.9917
Epoch 494/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1142 - accuracy: 0.9893 - val_loss: 0.1078 - val_accuracy: 0.9917
Epoch 495/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1140 - accuracy: 0.9893 - val_loss: 0.1076 - val_accuracy: 0.9917
Epoch 496/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1139 - accuracy: 0.9893 - val_loss: 0.1075 - val_accuracy: 0.9917
Epoch 497/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1137 - accuracy: 0.9893 - val_loss: 0.1073 - val_accuracy: 0.9917
Epoch 498/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1136 - accuracy: 0.9893 - val_loss: 0.1072 - val_accuracy: 0.9917
Epoch 499/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1134 - accuracy: 0.9893 - val_loss: 0.1070 - val_accuracy: 0.9917
Epoch 500/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1133 - accuracy: 0.9893 - val_loss: 0.1068 - val_accuracy: 0.9917
Epoch 501/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1131 - accuracy: 0.9893 - val_loss: 0.1067 - val_accuracy: 0.9917
Epoch 502/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1130 - accuracy: 0.9893 - val_loss: 0.1065 - val_accuracy: 0.9917
Epoch 503/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1129 - accuracy: 0.9893 - val_loss: 0.1064 - val_accuracy: 0.9917
Epoch 504/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1127 - accuracy: 0.9893 - val_loss: 0.1062 - val_accuracy: 0.9917
Epoch 505/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1126 - accuracy: 0.9893 - val_loss: 0.1061 - val_accuracy: 0.9917
Epoch 506/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1124 - accuracy: 0.9893 - val_loss: 0.1059 - val_accuracy: 0.9917
Epoch 507/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1123 - accuracy: 0.9893 - val_loss: 0.1058 - val_accuracy: 0.9917
Epoch 508/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1121 - accuracy: 0.9893 - val_loss: 0.1056 - val_accuracy: 0.9917
Epoch 509/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1120 - accuracy: 0.9893 - val_loss: 0.1055 - val_accuracy: 0.9917
Epoch 510/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1119 - accuracy: 0.9893 - val_loss: 0.1053 - val_accuracy: 0.9917
Epoch 511/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1117 - accuracy: 0.9893 - val_loss: 0.1052 - val_accuracy: 0.9917
Epoch 512/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1116 - accuracy: 0.9893 - val_loss: 0.1050 - val_accuracy: 0.9917
Epoch 513/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1114 - accuracy: 0.9893 - val_loss: 0.1049 - val_accuracy: 0.9917
Epoch 514/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1113 - accuracy: 0.9893 - val_loss: 0.1047 - val_accuracy: 0.9917
Epoch 515/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1112 - accuracy: 0.9893 - val_loss: 0.1046 - val_accuracy: 0.9917
Epoch 516/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1110 - accuracy: 0.9893 - val_loss: 0.1044 - val_accuracy: 0.9917
Epoch 517/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1109 - accuracy: 0.9893 - val_loss: 0.1043 - val_accuracy: 0.9917
Epoch 518/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1108 - accuracy: 0.9893 - val_loss: 0.1042 - val_accuracy: 0.9917
Epoch 519/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1106 - accuracy: 0.9893 - val_loss: 0.1040 - val_accuracy: 0.9917
Epoch 520/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1105 - accuracy: 0.9893 - val_loss: 0.1039 - val_accuracy: 0.9917
Epoch 521/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1104 - accuracy: 0.9893 - val_loss: 0.1037 - val_accuracy: 0.9917
Epoch 522/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1102 - accuracy: 0.9893 - val_loss: 0.1036 - val_accuracy: 0.9917
Epoch 523/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1101 - accuracy: 0.9893 - val_loss: 0.1034 - val_accuracy: 0.9917
Epoch 524/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1100 - accuracy: 0.9893 - val_loss: 0.1033 - val_accuracy: 0.9917
Epoch 525/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1098 - accuracy: 0.9893 - val_loss: 0.1032 - val_accuracy: 0.9917
Epoch 526/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1097 - accuracy: 0.9893 - val_loss: 0.1030 - val_accuracy: 0.9917
Epoch 527/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1096 - accuracy: 0.9893 - val_loss: 0.1029 - val_accuracy: 0.9917
Epoch 528/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1094 - accuracy: 0.9893 - val_loss: 0.1027 - val_accuracy: 0.9917
Epoch 529/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1093 - accuracy: 0.9893 - val_loss: 0.1026 - val_accuracy: 0.9917
Epoch 530/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1092 - accuracy: 0.9893 - val_loss: 0.1025 - val_accuracy: 0.9917
Epoch 531/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1090 - accuracy: 0.9893 - val_loss: 0.1023 - val_accuracy: 0.9917
Epoch 532/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1089 - accuracy: 0.9893 - val_loss: 0.1022 - val_accuracy: 0.9917
Epoch 533/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1088 - accuracy: 0.9893 - val_loss: 0.1020 - val_accuracy: 0.9917
Epoch 534/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1086 - accuracy: 0.9893 - val_loss: 0.1019 - val_accuracy: 0.9917
Epoch 535/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1085 - accuracy: 0.9893 - val_loss: 0.1018 - val_accuracy: 0.9917
Epoch 536/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1084 - accuracy: 0.9893 - val_loss: 0.1016 - val_accuracy: 0.9917
Epoch 537/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1083 - accuracy: 0.9893 - val_loss: 0.1015 - val_accuracy: 0.9917
Epoch 538/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1081 - accuracy: 0.9893 - val_loss: 0.1014 - val_accuracy: 0.9917
Epoch 539/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1080 - accuracy: 0.9893 - val_loss: 0.1012 - val_accuracy: 0.9917
Epoch 540/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1079 - accuracy: 0.9893 - val_loss: 0.1011 - val_accuracy: 0.9917
Epoch 541/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1078 - accuracy: 0.9893 - val_loss: 0.1010 - val_accuracy: 0.9917
Epoch 542/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1076 - accuracy: 0.9893 - val_loss: 0.1008 - val_accuracy: 0.9917
Epoch 543/1000
560/560 [==============================] - ETA: 0s - loss: 0.0988 - accuracy: 1.00 - 0s 41us/sample - loss: 0.1075 - accuracy: 0.9893 - val_loss: 0.1007 - val_accuracy: 0.9917
Epoch 544/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1074 - accuracy: 0.9893 - val_loss: 0.1006 - val_accuracy: 0.9917
Epoch 545/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1073 - accuracy: 0.9893 - val_loss: 0.1004 - val_accuracy: 0.9917
Epoch 546/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1071 - accuracy: 0.9893 - val_loss: 0.1003 - val_accuracy: 0.9917
Epoch 547/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1070 - accuracy: 0.9893 - val_loss: 0.1002 - val_accuracy: 0.9917
Epoch 548/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1069 - accuracy: 0.9893 - val_loss: 0.1000 - val_accuracy: 0.9917
Epoch 549/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1068 - accuracy: 0.9893 - val_loss: 0.0999 - val_accuracy: 0.9917
Epoch 550/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1066 - accuracy: 0.9893 - val_loss: 0.0998 - val_accuracy: 0.9917
Epoch 551/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1065 - accuracy: 0.9893 - val_loss: 0.0996 - val_accuracy: 0.9917
Epoch 552/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1064 - accuracy: 0.9893 - val_loss: 0.0995 - val_accuracy: 0.9917
Epoch 553/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1063 - accuracy: 0.9893 - val_loss: 0.0994 - val_accuracy: 0.9917
Epoch 554/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1062 - accuracy: 0.9893 - val_loss: 0.0993 - val_accuracy: 0.9917
Epoch 555/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1060 - accuracy: 0.9893 - val_loss: 0.0991 - val_accuracy: 0.9917
Epoch 556/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1059 - accuracy: 0.9893 - val_loss: 0.0990 - val_accuracy: 0.9917
Epoch 557/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1058 - accuracy: 0.9893 - val_loss: 0.0989 - val_accuracy: 0.9917
Epoch 558/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1057 - accuracy: 0.9893 - val_loss: 0.0987 - val_accuracy: 0.9917
Epoch 559/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1056 - accuracy: 0.9893 - val_loss: 0.0986 - val_accuracy: 0.9917
Epoch 560/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1054 - accuracy: 0.9893 - val_loss: 0.0985 - val_accuracy: 0.9917
Epoch 561/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1053 - accuracy: 0.9893 - val_loss: 0.0984 - val_accuracy: 0.9917
Epoch 562/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1052 - accuracy: 0.9893 - val_loss: 0.0982 - val_accuracy: 0.9917
Epoch 563/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1051 - accuracy: 0.9893 - val_loss: 0.0981 - val_accuracy: 0.9917
Epoch 564/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1050 - accuracy: 0.9893 - val_loss: 0.0980 - val_accuracy: 0.9917
Epoch 565/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1049 - accuracy: 0.9893 - val_loss: 0.0979 - val_accuracy: 0.9917
Epoch 566/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1047 - accuracy: 0.9893 - val_loss: 0.0977 - val_accuracy: 0.9917
Epoch 567/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1046 - accuracy: 0.9893 - val_loss: 0.0976 - val_accuracy: 0.9917
Epoch 568/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1045 - accuracy: 0.9893 - val_loss: 0.0975 - val_accuracy: 0.9917
Epoch 569/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1044 - accuracy: 0.9893 - val_loss: 0.0974 - val_accuracy: 0.9917
Epoch 570/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1043 - accuracy: 0.9893 - val_loss: 0.0973 - val_accuracy: 0.9917
Epoch 571/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1042 - accuracy: 0.9893 - val_loss: 0.0971 - val_accuracy: 0.9917
Epoch 572/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1041 - accuracy: 0.9893 - val_loss: 0.0970 - val_accuracy: 0.9917
Epoch 573/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1039 - accuracy: 0.9893 - val_loss: 0.0969 - val_accuracy: 0.9917
Epoch 574/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1038 - accuracy: 0.9893 - val_loss: 0.0968 - val_accuracy: 0.9917
Epoch 575/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1037 - accuracy: 0.9893 - val_loss: 0.0967 - val_accuracy: 0.9917
Epoch 576/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1036 - accuracy: 0.9893 - val_loss: 0.0965 - val_accuracy: 0.9917
Epoch 577/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1035 - accuracy: 0.9893 - val_loss: 0.0964 - val_accuracy: 0.9917
Epoch 578/1000
560/560 [==============================] - 0s 49us/sample - loss: 0.1034 - accuracy: 0.9893 - val_loss: 0.0963 - val_accuracy: 0.9917
Epoch 579/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1033 - accuracy: 0.9893 - val_loss: 0.0962 - val_accuracy: 0.9917
Epoch 580/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1032 - accuracy: 0.9893 - val_loss: 0.0961 - val_accuracy: 0.9917
Epoch 581/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1030 - accuracy: 0.9893 - val_loss: 0.0959 - val_accuracy: 0.9917
Epoch 582/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1029 - accuracy: 0.9893 - val_loss: 0.0958 - val_accuracy: 0.9917
Epoch 583/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1028 - accuracy: 0.9893 - val_loss: 0.0957 - val_accuracy: 0.9917
Epoch 584/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1027 - accuracy: 0.9893 - val_loss: 0.0956 - val_accuracy: 0.9917
Epoch 585/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1026 - accuracy: 0.9893 - val_loss: 0.0955 - val_accuracy: 0.9917
Epoch 586/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1025 - accuracy: 0.9893 - val_loss: 0.0954 - val_accuracy: 0.9917
Epoch 587/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1024 - accuracy: 0.9893 - val_loss: 0.0952 - val_accuracy: 0.9917
Epoch 588/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1023 - accuracy: 0.9893 - val_loss: 0.0951 - val_accuracy: 0.9917
Epoch 589/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.1022 - accuracy: 0.9893 - val_loss: 0.0950 - val_accuracy: 0.9917
Epoch 590/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1021 - accuracy: 0.9893 - val_loss: 0.0949 - val_accuracy: 0.9917
Epoch 591/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1020 - accuracy: 0.9893 - val_loss: 0.0948 - val_accuracy: 0.9917
Epoch 592/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1019 - accuracy: 0.9893 - val_loss: 0.0947 - val_accuracy: 0.9917
Epoch 593/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1017 - accuracy: 0.9893 - val_loss: 0.0946 - val_accuracy: 0.9917
Epoch 594/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1016 - accuracy: 0.9893 - val_loss: 0.0944 - val_accuracy: 0.9917
Epoch 595/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1015 - accuracy: 0.9893 - val_loss: 0.0943 - val_accuracy: 0.9917
Epoch 596/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1014 - accuracy: 0.9893 - val_loss: 0.0942 - val_accuracy: 0.9917
Epoch 597/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1013 - accuracy: 0.9893 - val_loss: 0.0941 - val_accuracy: 0.9917
Epoch 598/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1012 - accuracy: 0.9893 - val_loss: 0.0940 - val_accuracy: 0.9917
Epoch 599/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1011 - accuracy: 0.9893 - val_loss: 0.0939 - val_accuracy: 0.9917
Epoch 600/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1010 - accuracy: 0.9893 - val_loss: 0.0938 - val_accuracy: 0.9917
Epoch 601/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1009 - accuracy: 0.9893 - val_loss: 0.0937 - val_accuracy: 0.9917
Epoch 602/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1008 - accuracy: 0.9893 - val_loss: 0.0936 - val_accuracy: 0.9917
Epoch 603/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1007 - accuracy: 0.9893 - val_loss: 0.0934 - val_accuracy: 0.9917
Epoch 604/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1006 - accuracy: 0.9893 - val_loss: 0.0933 - val_accuracy: 0.9917
Epoch 605/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.1005 - accuracy: 0.9893 - val_loss: 0.0932 - val_accuracy: 0.9917
Epoch 606/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1004 - accuracy: 0.9893 - val_loss: 0.0931 - val_accuracy: 0.9917
Epoch 607/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.1003 - accuracy: 0.9893 - val_loss: 0.0930 - val_accuracy: 0.9917
Epoch 608/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1002 - accuracy: 0.9893 - val_loss: 0.0929 - val_accuracy: 0.9917
Epoch 609/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.1001 - accuracy: 0.9893 - val_loss: 0.0928 - val_accuracy: 0.9917
Epoch 610/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.1000 - accuracy: 0.9893 - val_loss: 0.0927 - val_accuracy: 0.9917
Epoch 611/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0999 - accuracy: 0.9893 - val_loss: 0.0926 - val_accuracy: 0.9917
Epoch 612/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0998 - accuracy: 0.9893 - val_loss: 0.0925 - val_accuracy: 0.9917
Epoch 613/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0997 - accuracy: 0.9893 - val_loss: 0.0924 - val_accuracy: 0.9917
Epoch 614/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0996 - accuracy: 0.9893 - val_loss: 0.0923 - val_accuracy: 0.9917
Epoch 615/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0995 - accuracy: 0.9893 - val_loss: 0.0921 - val_accuracy: 0.9917
Epoch 616/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0994 - accuracy: 0.9893 - val_loss: 0.0920 - val_accuracy: 0.9917
Epoch 617/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0993 - accuracy: 0.9893 - val_loss: 0.0919 - val_accuracy: 0.9917
Epoch 618/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0992 - accuracy: 0.9893 - val_loss: 0.0918 - val_accuracy: 0.9917
Epoch 619/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0991 - accuracy: 0.9893 - val_loss: 0.0917 - val_accuracy: 0.9917
Epoch 620/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0990 - accuracy: 0.9893 - val_loss: 0.0916 - val_accuracy: 0.9917
Epoch 621/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0989 - accuracy: 0.9893 - val_loss: 0.0915 - val_accuracy: 0.9917
Epoch 622/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0988 - accuracy: 0.9893 - val_loss: 0.0914 - val_accuracy: 0.9917
Epoch 623/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0987 - accuracy: 0.9893 - val_loss: 0.0913 - val_accuracy: 0.9917
Epoch 624/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0986 - accuracy: 0.9893 - val_loss: 0.0912 - val_accuracy: 0.9917
Epoch 625/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0985 - accuracy: 0.9893 - val_loss: 0.0911 - val_accuracy: 0.9917
Epoch 626/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0984 - accuracy: 0.9893 - val_loss: 0.0910 - val_accuracy: 0.9917
Epoch 627/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0983 - accuracy: 0.9893 - val_loss: 0.0909 - val_accuracy: 0.9917
Epoch 628/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0982 - accuracy: 0.9893 - val_loss: 0.0908 - val_accuracy: 0.9917
Epoch 629/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0981 - accuracy: 0.9893 - val_loss: 0.0907 - val_accuracy: 0.9917
Epoch 630/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0980 - accuracy: 0.9893 - val_loss: 0.0906 - val_accuracy: 0.9917
Epoch 631/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0979 - accuracy: 0.9893 - val_loss: 0.0905 - val_accuracy: 0.9917
Epoch 632/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0978 - accuracy: 0.9893 - val_loss: 0.0904 - val_accuracy: 0.9917
Epoch 633/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0977 - accuracy: 0.9893 - val_loss: 0.0903 - val_accuracy: 0.9917
Epoch 634/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0976 - accuracy: 0.9893 - val_loss: 0.0902 - val_accuracy: 0.9917
Epoch 635/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0976 - accuracy: 0.9893 - val_loss: 0.0901 - val_accuracy: 0.9917
Epoch 636/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0975 - accuracy: 0.9893 - val_loss: 0.0900 - val_accuracy: 0.9917
Epoch 637/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0974 - accuracy: 0.9893 - val_loss: 0.0899 - val_accuracy: 0.9917
Epoch 638/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0973 - accuracy: 0.9893 - val_loss: 0.0898 - val_accuracy: 0.9917
Epoch 639/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0972 - accuracy: 0.9893 - val_loss: 0.0897 - val_accuracy: 0.9917
Epoch 640/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0971 - accuracy: 0.9893 - val_loss: 0.0896 - val_accuracy: 0.9917
Epoch 641/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0970 - accuracy: 0.9893 - val_loss: 0.0895 - val_accuracy: 0.9917
Epoch 642/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0969 - accuracy: 0.9893 - val_loss: 0.0894 - val_accuracy: 0.9917
Epoch 643/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0968 - accuracy: 0.9893 - val_loss: 0.0893 - val_accuracy: 0.9917
Epoch 644/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0967 - accuracy: 0.9893 - val_loss: 0.0892 - val_accuracy: 0.9917
Epoch 645/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0966 - accuracy: 0.9893 - val_loss: 0.0891 - val_accuracy: 0.9917
Epoch 646/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0965 - accuracy: 0.9893 - val_loss: 0.0890 - val_accuracy: 0.9917
Epoch 647/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0964 - accuracy: 0.9893 - val_loss: 0.0889 - val_accuracy: 0.9917
Epoch 648/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0964 - accuracy: 0.9893 - val_loss: 0.0888 - val_accuracy: 0.9917
Epoch 649/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0963 - accuracy: 0.9893 - val_loss: 0.0887 - val_accuracy: 0.9917
Epoch 650/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0962 - accuracy: 0.9893 - val_loss: 0.0886 - val_accuracy: 0.9917
Epoch 651/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0961 - accuracy: 0.9893 - val_loss: 0.0885 - val_accuracy: 0.9917
Epoch 652/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0960 - accuracy: 0.9893 - val_loss: 0.0884 - val_accuracy: 0.9917
Epoch 653/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0959 - accuracy: 0.9893 - val_loss: 0.0883 - val_accuracy: 0.9917
Epoch 654/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0958 - accuracy: 0.9893 - val_loss: 0.0883 - val_accuracy: 0.9917
Epoch 655/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0957 - accuracy: 0.9893 - val_loss: 0.0882 - val_accuracy: 0.9917
Epoch 656/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0956 - accuracy: 0.9893 - val_loss: 0.0881 - val_accuracy: 0.9917
Epoch 657/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0956 - accuracy: 0.9893 - val_loss: 0.0880 - val_accuracy: 0.9917
Epoch 658/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0955 - accuracy: 0.9893 - val_loss: 0.0879 - val_accuracy: 0.9917
Epoch 659/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0954 - accuracy: 0.9893 - val_loss: 0.0878 - val_accuracy: 0.9917
Epoch 660/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0953 - accuracy: 0.9893 - val_loss: 0.0877 - val_accuracy: 0.9917
Epoch 661/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0952 - accuracy: 0.9893 - val_loss: 0.0876 - val_accuracy: 0.9917
Epoch 662/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0951 - accuracy: 0.9893 - val_loss: 0.0875 - val_accuracy: 0.9917
Epoch 663/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0950 - accuracy: 0.9893 - val_loss: 0.0874 - val_accuracy: 0.9917
Epoch 664/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0949 - accuracy: 0.9893 - val_loss: 0.0873 - val_accuracy: 0.9917
Epoch 665/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0949 - accuracy: 0.9893 - val_loss: 0.0872 - val_accuracy: 0.9917
Epoch 666/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0948 - accuracy: 0.9893 - val_loss: 0.0871 - val_accuracy: 0.9917
Epoch 667/1000
560/560 [==============================] - 0s 42us/sample - loss: 0.0947 - accuracy: 0.9893 - val_loss: 0.0870 - val_accuracy: 0.9917
Epoch 668/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0946 - accuracy: 0.9893 - val_loss: 0.0869 - val_accuracy: 0.9917
Epoch 669/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0945 - accuracy: 0.9893 - val_loss: 0.0869 - val_accuracy: 0.9917
Epoch 670/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0944 - accuracy: 0.9893 - val_loss: 0.0868 - val_accuracy: 0.9917
Epoch 671/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0943 - accuracy: 0.9893 - val_loss: 0.0867 - val_accuracy: 0.9917
Epoch 672/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0943 - accuracy: 0.9893 - val_loss: 0.0866 - val_accuracy: 0.9917
Epoch 673/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0942 - accuracy: 0.9893 - val_loss: 0.0865 - val_accuracy: 0.9917
Epoch 674/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0941 - accuracy: 0.9893 - val_loss: 0.0864 - val_accuracy: 0.9917
Epoch 675/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0940 - accuracy: 0.9893 - val_loss: 0.0863 - val_accuracy: 0.9917
Epoch 676/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0939 - accuracy: 0.9893 - val_loss: 0.0862 - val_accuracy: 0.9917
Epoch 677/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0938 - accuracy: 0.9893 - val_loss: 0.0861 - val_accuracy: 0.9917
Epoch 678/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0938 - accuracy: 0.9893 - val_loss: 0.0861 - val_accuracy: 0.9917
Epoch 679/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0937 - accuracy: 0.9893 - val_loss: 0.0860 - val_accuracy: 0.9917
Epoch 680/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0936 - accuracy: 0.9893 - val_loss: 0.0859 - val_accuracy: 0.9917
Epoch 681/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0935 - accuracy: 0.9893 - val_loss: 0.0858 - val_accuracy: 0.9917
Epoch 682/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0934 - accuracy: 0.9893 - val_loss: 0.0857 - val_accuracy: 0.9917
Epoch 683/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0933 - accuracy: 0.9893 - val_loss: 0.0856 - val_accuracy: 0.9917
Epoch 684/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0933 - accuracy: 0.9893 - val_loss: 0.0855 - val_accuracy: 0.9917
Epoch 685/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0932 - accuracy: 0.9893 - val_loss: 0.0854 - val_accuracy: 0.9917
Epoch 686/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0931 - accuracy: 0.9893 - val_loss: 0.0854 - val_accuracy: 0.9917
Epoch 687/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0930 - accuracy: 0.9893 - val_loss: 0.0853 - val_accuracy: 0.9917
Epoch 688/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0929 - accuracy: 0.9893 - val_loss: 0.0852 - val_accuracy: 0.9917
Epoch 689/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0928 - accuracy: 0.9893 - val_loss: 0.0851 - val_accuracy: 0.9917
Epoch 690/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0928 - accuracy: 0.9893 - val_loss: 0.0850 - val_accuracy: 0.9917
Epoch 691/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0927 - accuracy: 0.9893 - val_loss: 0.0849 - val_accuracy: 0.9917
Epoch 692/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0926 - accuracy: 0.9893 - val_loss: 0.0848 - val_accuracy: 0.9917
Epoch 693/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0925 - accuracy: 0.9893 - val_loss: 0.0847 - val_accuracy: 0.9917
Epoch 694/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0924 - accuracy: 0.9911 - val_loss: 0.0847 - val_accuracy: 0.9917
Epoch 695/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0924 - accuracy: 0.9893 - val_loss: 0.0846 - val_accuracy: 0.9917
Epoch 696/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0923 - accuracy: 0.9911 - val_loss: 0.0845 - val_accuracy: 0.9917
Epoch 697/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0922 - accuracy: 0.9911 - val_loss: 0.0844 - val_accuracy: 0.9917
Epoch 698/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0921 - accuracy: 0.9911 - val_loss: 0.0843 - val_accuracy: 0.9917
Epoch 699/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0920 - accuracy: 0.9911 - val_loss: 0.0842 - val_accuracy: 0.9917
Epoch 700/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0920 - accuracy: 0.9911 - val_loss: 0.0841 - val_accuracy: 0.9917
Epoch 701/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0919 - accuracy: 0.9911 - val_loss: 0.0841 - val_accuracy: 0.9917
Epoch 702/1000
560/560 [==============================] - ETA: 0s - loss: 0.1018 - accuracy: 1.00 - 0s 43us/sample - loss: 0.0918 - accuracy: 0.9911 - val_loss: 0.0840 - val_accuracy: 0.9917
Epoch 703/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0917 - accuracy: 0.9911 - val_loss: 0.0839 - val_accuracy: 0.9917
Epoch 704/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0917 - accuracy: 0.9911 - val_loss: 0.0838 - val_accuracy: 0.9917
Epoch 705/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0916 - accuracy: 0.9911 - val_loss: 0.0837 - val_accuracy: 0.9917
Epoch 706/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0915 - accuracy: 0.9911 - val_loss: 0.0836 - val_accuracy: 0.9917
Epoch 707/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0914 - accuracy: 0.9911 - val_loss: 0.0836 - val_accuracy: 0.9917
Epoch 708/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0913 - accuracy: 0.9911 - val_loss: 0.0835 - val_accuracy: 0.9917
Epoch 709/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0913 - accuracy: 0.9911 - val_loss: 0.0834 - val_accuracy: 0.9917
Epoch 710/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0912 - accuracy: 0.9911 - val_loss: 0.0833 - val_accuracy: 0.9917
Epoch 711/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0911 - accuracy: 0.9911 - val_loss: 0.0832 - val_accuracy: 0.9917
Epoch 712/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0910 - accuracy: 0.9911 - val_loss: 0.0832 - val_accuracy: 0.9917
Epoch 713/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0910 - accuracy: 0.9911 - val_loss: 0.0831 - val_accuracy: 0.9917
Epoch 714/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0909 - accuracy: 0.9911 - val_loss: 0.0830 - val_accuracy: 0.9917
Epoch 715/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0908 - accuracy: 0.9911 - val_loss: 0.0829 - val_accuracy: 0.9917
Epoch 716/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0907 - accuracy: 0.9911 - val_loss: 0.0828 - val_accuracy: 0.9917
Epoch 717/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0907 - accuracy: 0.9911 - val_loss: 0.0828 - val_accuracy: 0.9917
Epoch 718/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0906 - accuracy: 0.9911 - val_loss: 0.0827 - val_accuracy: 0.9917
Epoch 719/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0905 - accuracy: 0.9911 - val_loss: 0.0826 - val_accuracy: 0.9917
Epoch 720/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0904 - accuracy: 0.9911 - val_loss: 0.0825 - val_accuracy: 0.9917
Epoch 721/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0904 - accuracy: 0.9911 - val_loss: 0.0824 - val_accuracy: 0.9917
Epoch 722/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0903 - accuracy: 0.9911 - val_loss: 0.0824 - val_accuracy: 0.9917
Epoch 723/1000
560/560 [==============================] - 0s 39us/sample - loss: 0.0902 - accuracy: 0.9911 - val_loss: 0.0823 - val_accuracy: 0.9917
Epoch 724/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0901 - accuracy: 0.9911 - val_loss: 0.0822 - val_accuracy: 0.9917
Epoch 725/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0901 - accuracy: 0.9911 - val_loss: 0.0821 - val_accuracy: 0.9917
Epoch 726/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0900 - accuracy: 0.9911 - val_loss: 0.0820 - val_accuracy: 0.9917
Epoch 727/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0899 - accuracy: 0.9911 - val_loss: 0.0820 - val_accuracy: 0.9917
Epoch 728/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0898 - accuracy: 0.9911 - val_loss: 0.0819 - val_accuracy: 0.9917
Epoch 729/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0898 - accuracy: 0.9911 - val_loss: 0.0818 - val_accuracy: 0.9917
Epoch 730/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0897 - accuracy: 0.9911 - val_loss: 0.0817 - val_accuracy: 0.9917
Epoch 731/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0896 - accuracy: 0.9911 - val_loss: 0.0816 - val_accuracy: 0.9917
Epoch 732/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0895 - accuracy: 0.9911 - val_loss: 0.0816 - val_accuracy: 0.9917
Epoch 733/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0895 - accuracy: 0.9911 - val_loss: 0.0815 - val_accuracy: 0.9917
Epoch 734/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0894 - accuracy: 0.9911 - val_loss: 0.0814 - val_accuracy: 0.9917
Epoch 735/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0893 - accuracy: 0.9911 - val_loss: 0.0813 - val_accuracy: 0.9917
Epoch 736/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0893 - accuracy: 0.9911 - val_loss: 0.0813 - val_accuracy: 0.9917
Epoch 737/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0892 - accuracy: 0.9911 - val_loss: 0.0812 - val_accuracy: 0.9917
Epoch 738/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0891 - accuracy: 0.9911 - val_loss: 0.0811 - val_accuracy: 0.9917
Epoch 739/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0890 - accuracy: 0.9911 - val_loss: 0.0810 - val_accuracy: 0.9917
Epoch 740/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0890 - accuracy: 0.9911 - val_loss: 0.0810 - val_accuracy: 0.9917
Epoch 741/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0889 - accuracy: 0.9911 - val_loss: 0.0809 - val_accuracy: 0.9917
Epoch 742/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0888 - accuracy: 0.9911 - val_loss: 0.0808 - val_accuracy: 0.9917
Epoch 743/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0888 - accuracy: 0.9911 - val_loss: 0.0807 - val_accuracy: 0.9917
Epoch 744/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0887 - accuracy: 0.9911 - val_loss: 0.0807 - val_accuracy: 0.9917
Epoch 745/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0886 - accuracy: 0.9911 - val_loss: 0.0806 - val_accuracy: 0.9917
Epoch 746/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0886 - accuracy: 0.9911 - val_loss: 0.0805 - val_accuracy: 0.9917
Epoch 747/1000
560/560 [==============================] - 0s 52us/sample - loss: 0.0885 - accuracy: 0.9911 - val_loss: 0.0804 - val_accuracy: 0.9917
Epoch 748/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0884 - accuracy: 0.9911 - val_loss: 0.0804 - val_accuracy: 0.9917
Epoch 749/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0883 - accuracy: 0.9911 - val_loss: 0.0803 - val_accuracy: 0.9917
Epoch 750/1000
560/560 [==============================] - ETA: 0s - loss: 0.0653 - accuracy: 1.00 - 0s 46us/sample - loss: 0.0883 - accuracy: 0.9911 - val_loss: 0.0802 - val_accuracy: 0.9917
Epoch 751/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0882 - accuracy: 0.9911 - val_loss: 0.0801 - val_accuracy: 0.9917
Epoch 752/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0881 - accuracy: 0.9911 - val_loss: 0.0801 - val_accuracy: 0.9917
Epoch 753/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0881 - accuracy: 0.9911 - val_loss: 0.0800 - val_accuracy: 0.9917
Epoch 754/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0880 - accuracy: 0.9911 - val_loss: 0.0799 - val_accuracy: 0.9917
Epoch 755/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0879 - accuracy: 0.9911 - val_loss: 0.0799 - val_accuracy: 0.9917
Epoch 756/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0879 - accuracy: 0.9911 - val_loss: 0.0798 - val_accuracy: 0.9917
Epoch 757/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0878 - accuracy: 0.9911 - val_loss: 0.0797 - val_accuracy: 0.9917
Epoch 758/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0877 - accuracy: 0.9911 - val_loss: 0.0796 - val_accuracy: 0.9917
Epoch 759/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0877 - accuracy: 0.9911 - val_loss: 0.0796 - val_accuracy: 0.9917
Epoch 760/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0876 - accuracy: 0.9911 - val_loss: 0.0795 - val_accuracy: 0.9917
Epoch 761/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0875 - accuracy: 0.9911 - val_loss: 0.0794 - val_accuracy: 0.9917
Epoch 762/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0874 - accuracy: 0.9911 - val_loss: 0.0793 - val_accuracy: 0.9917
Epoch 763/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0874 - accuracy: 0.9911 - val_loss: 0.0793 - val_accuracy: 0.9917
Epoch 764/1000
560/560 [==============================] - 0s 50us/sample - loss: 0.0873 - accuracy: 0.9911 - val_loss: 0.0792 - val_accuracy: 0.9917
Epoch 765/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0872 - accuracy: 0.9911 - val_loss: 0.0791 - val_accuracy: 0.9917
Epoch 766/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0872 - accuracy: 0.9911 - val_loss: 0.0791 - val_accuracy: 0.9917
Epoch 767/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0871 - accuracy: 0.9911 - val_loss: 0.0790 - val_accuracy: 0.9917
Epoch 768/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0870 - accuracy: 0.9911 - val_loss: 0.0789 - val_accuracy: 0.9917
Epoch 769/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0870 - accuracy: 0.9911 - val_loss: 0.0788 - val_accuracy: 0.9917
Epoch 770/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0869 - accuracy: 0.9911 - val_loss: 0.0788 - val_accuracy: 0.9917
Epoch 771/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0868 - accuracy: 0.9911 - val_loss: 0.0787 - val_accuracy: 0.9917
Epoch 772/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0868 - accuracy: 0.9911 - val_loss: 0.0786 - val_accuracy: 0.9917
Epoch 773/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0867 - accuracy: 0.9911 - val_loss: 0.0786 - val_accuracy: 0.9917
Epoch 774/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0866 - accuracy: 0.9911 - val_loss: 0.0785 - val_accuracy: 0.9917
Epoch 775/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0866 - accuracy: 0.9911 - val_loss: 0.0784 - val_accuracy: 0.9917
Epoch 776/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0865 - accuracy: 0.9911 - val_loss: 0.0784 - val_accuracy: 0.9917
Epoch 777/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0865 - accuracy: 0.9911 - val_loss: 0.0783 - val_accuracy: 0.9917
Epoch 778/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0864 - accuracy: 0.9911 - val_loss: 0.0782 - val_accuracy: 0.9958
Epoch 779/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0863 - accuracy: 0.9911 - val_loss: 0.0781 - val_accuracy: 0.9958
Epoch 780/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0863 - accuracy: 0.9911 - val_loss: 0.0781 - val_accuracy: 0.9958
Epoch 781/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0862 - accuracy: 0.9911 - val_loss: 0.0780 - val_accuracy: 0.9958
Epoch 782/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0861 - accuracy: 0.9911 - val_loss: 0.0779 - val_accuracy: 0.9958
Epoch 783/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0861 - accuracy: 0.9911 - val_loss: 0.0779 - val_accuracy: 0.9958
Epoch 784/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0860 - accuracy: 0.9911 - val_loss: 0.0778 - val_accuracy: 0.9958
Epoch 785/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0859 - accuracy: 0.9911 - val_loss: 0.0777 - val_accuracy: 0.9958
Epoch 786/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0859 - accuracy: 0.9911 - val_loss: 0.0777 - val_accuracy: 0.9958
Epoch 787/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0858 - accuracy: 0.9911 - val_loss: 0.0776 - val_accuracy: 0.9958
Epoch 788/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0857 - accuracy: 0.9911 - val_loss: 0.0775 - val_accuracy: 0.9958
Epoch 789/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0857 - accuracy: 0.9911 - val_loss: 0.0775 - val_accuracy: 0.9958
Epoch 790/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0856 - accuracy: 0.9911 - val_loss: 0.0774 - val_accuracy: 0.9958
Epoch 791/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0856 - accuracy: 0.9911 - val_loss: 0.0773 - val_accuracy: 0.9958
Epoch 792/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0855 - accuracy: 0.9911 - val_loss: 0.0773 - val_accuracy: 0.9958
Epoch 793/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0854 - accuracy: 0.9911 - val_loss: 0.0772 - val_accuracy: 0.9958
Epoch 794/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0854 - accuracy: 0.9911 - val_loss: 0.0771 - val_accuracy: 0.9958
Epoch 795/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0853 - accuracy: 0.9911 - val_loss: 0.0771 - val_accuracy: 0.9958
Epoch 796/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0852 - accuracy: 0.9911 - val_loss: 0.0770 - val_accuracy: 0.9958
Epoch 797/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0852 - accuracy: 0.9911 - val_loss: 0.0769 - val_accuracy: 0.9958
Epoch 798/1000
560/560 [==============================] - 0s 39us/sample - loss: 0.0851 - accuracy: 0.9911 - val_loss: 0.0769 - val_accuracy: 0.9958
Epoch 799/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0851 - accuracy: 0.9911 - val_loss: 0.0768 - val_accuracy: 0.9958
Epoch 800/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0850 - accuracy: 0.9911 - val_loss: 0.0767 - val_accuracy: 0.9958
Epoch 801/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0849 - accuracy: 0.9911 - val_loss: 0.0767 - val_accuracy: 0.9958
Epoch 802/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0849 - accuracy: 0.9911 - val_loss: 0.0766 - val_accuracy: 0.9958
Epoch 803/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0848 - accuracy: 0.9911 - val_loss: 0.0765 - val_accuracy: 0.9958
Epoch 804/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0847 - accuracy: 0.9911 - val_loss: 0.0765 - val_accuracy: 0.9958
Epoch 805/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0847 - accuracy: 0.9911 - val_loss: 0.0764 - val_accuracy: 0.9958
Epoch 806/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0846 - accuracy: 0.9911 - val_loss: 0.0763 - val_accuracy: 0.9958
Epoch 807/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0846 - accuracy: 0.9911 - val_loss: 0.0763 - val_accuracy: 0.9958
Epoch 808/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0845 - accuracy: 0.9911 - val_loss: 0.0762 - val_accuracy: 0.9958
Epoch 809/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0844 - accuracy: 0.9911 - val_loss: 0.0762 - val_accuracy: 0.9958
Epoch 810/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0844 - accuracy: 0.9911 - val_loss: 0.0761 - val_accuracy: 0.9958
Epoch 811/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0843 - accuracy: 0.9911 - val_loss: 0.0760 - val_accuracy: 0.9958
Epoch 812/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0843 - accuracy: 0.9911 - val_loss: 0.0760 - val_accuracy: 0.9958
Epoch 813/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0842 - accuracy: 0.9911 - val_loss: 0.0759 - val_accuracy: 0.9958
Epoch 814/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0841 - accuracy: 0.9911 - val_loss: 0.0758 - val_accuracy: 0.9958
Epoch 815/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0841 - accuracy: 0.9911 - val_loss: 0.0758 - val_accuracy: 0.9958
Epoch 816/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0840 - accuracy: 0.9911 - val_loss: 0.0757 - val_accuracy: 0.9958
Epoch 817/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0840 - accuracy: 0.9911 - val_loss: 0.0756 - val_accuracy: 0.9958
Epoch 818/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0839 - accuracy: 0.9911 - val_loss: 0.0756 - val_accuracy: 0.9958
Epoch 819/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0838 - accuracy: 0.9911 - val_loss: 0.0755 - val_accuracy: 0.9958
Epoch 820/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0838 - accuracy: 0.9911 - val_loss: 0.0754 - val_accuracy: 0.9958
Epoch 821/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0837 - accuracy: 0.9911 - val_loss: 0.0754 - val_accuracy: 0.9958
Epoch 822/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0837 - accuracy: 0.9911 - val_loss: 0.0753 - val_accuracy: 0.9958
Epoch 823/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0836 - accuracy: 0.9911 - val_loss: 0.0753 - val_accuracy: 0.9958
Epoch 824/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0835 - accuracy: 0.9911 - val_loss: 0.0752 - val_accuracy: 0.9958
Epoch 825/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0835 - accuracy: 0.9911 - val_loss: 0.0751 - val_accuracy: 0.9958
Epoch 826/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0834 - accuracy: 0.9911 - val_loss: 0.0751 - val_accuracy: 0.9958
Epoch 827/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0834 - accuracy: 0.9911 - val_loss: 0.0750 - val_accuracy: 0.9958
Epoch 828/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0833 - accuracy: 0.9911 - val_loss: 0.0749 - val_accuracy: 0.9958
Epoch 829/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0833 - accuracy: 0.9911 - val_loss: 0.0749 - val_accuracy: 0.9958
Epoch 830/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0832 - accuracy: 0.9911 - val_loss: 0.0748 - val_accuracy: 0.9958
Epoch 831/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0831 - accuracy: 0.9911 - val_loss: 0.0748 - val_accuracy: 0.9958
Epoch 832/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0831 - accuracy: 0.9911 - val_loss: 0.0747 - val_accuracy: 0.9958
Epoch 833/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0830 - accuracy: 0.9911 - val_loss: 0.0746 - val_accuracy: 0.9958
Epoch 834/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0830 - accuracy: 0.9911 - val_loss: 0.0746 - val_accuracy: 0.9958
Epoch 835/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0829 - accuracy: 0.9911 - val_loss: 0.0745 - val_accuracy: 0.9958
Epoch 836/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0829 - accuracy: 0.9911 - val_loss: 0.0745 - val_accuracy: 0.9958
Epoch 837/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0828 - accuracy: 0.9911 - val_loss: 0.0744 - val_accuracy: 0.9958
Epoch 838/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0827 - accuracy: 0.9911 - val_loss: 0.0743 - val_accuracy: 0.9958
Epoch 839/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0827 - accuracy: 0.9911 - val_loss: 0.0743 - val_accuracy: 0.9958
Epoch 840/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0826 - accuracy: 0.9911 - val_loss: 0.0742 - val_accuracy: 0.9958
Epoch 841/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0826 - accuracy: 0.9911 - val_loss: 0.0742 - val_accuracy: 0.9958
Epoch 842/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0825 - accuracy: 0.9911 - val_loss: 0.0741 - val_accuracy: 0.9958
Epoch 843/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0825 - accuracy: 0.9911 - val_loss: 0.0740 - val_accuracy: 0.9958
Epoch 844/1000
560/560 [==============================] - ETA: 0s - loss: 0.1042 - accuracy: 0.96 - 0s 45us/sample - loss: 0.0824 - accuracy: 0.9911 - val_loss: 0.0740 - val_accuracy: 0.9958
Epoch 845/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0823 - accuracy: 0.9911 - val_loss: 0.0739 - val_accuracy: 0.9958
Epoch 846/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0823 - accuracy: 0.9911 - val_loss: 0.0739 - val_accuracy: 0.9958
Epoch 847/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0822 - accuracy: 0.9911 - val_loss: 0.0738 - val_accuracy: 0.9958
Epoch 848/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0822 - accuracy: 0.9911 - val_loss: 0.0737 - val_accuracy: 0.9958
Epoch 849/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0821 - accuracy: 0.9911 - val_loss: 0.0737 - val_accuracy: 0.9958
Epoch 850/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0821 - accuracy: 0.9911 - val_loss: 0.0736 - val_accuracy: 0.9958
Epoch 851/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0820 - accuracy: 0.9911 - val_loss: 0.0736 - val_accuracy: 0.9958
Epoch 852/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0820 - accuracy: 0.9911 - val_loss: 0.0735 - val_accuracy: 0.9958
Epoch 853/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0819 - accuracy: 0.9911 - val_loss: 0.0734 - val_accuracy: 0.9958
Epoch 854/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0818 - accuracy: 0.9911 - val_loss: 0.0734 - val_accuracy: 0.9958
Epoch 855/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0818 - accuracy: 0.9911 - val_loss: 0.0733 - val_accuracy: 0.9958
Epoch 856/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0817 - accuracy: 0.9911 - val_loss: 0.0733 - val_accuracy: 0.9958
Epoch 857/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0817 - accuracy: 0.9911 - val_loss: 0.0732 - val_accuracy: 0.9958
Epoch 858/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0816 - accuracy: 0.9911 - val_loss: 0.0731 - val_accuracy: 0.9958
Epoch 859/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0816 - accuracy: 0.9911 - val_loss: 0.0731 - val_accuracy: 0.9958
Epoch 860/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0815 - accuracy: 0.9911 - val_loss: 0.0730 - val_accuracy: 0.9958
Epoch 861/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0815 - accuracy: 0.9911 - val_loss: 0.0730 - val_accuracy: 0.9958
Epoch 862/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0814 - accuracy: 0.9911 - val_loss: 0.0729 - val_accuracy: 0.9958
Epoch 863/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0814 - accuracy: 0.9911 - val_loss: 0.0729 - val_accuracy: 0.9958
Epoch 864/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0813 - accuracy: 0.9911 - val_loss: 0.0728 - val_accuracy: 0.9958
Epoch 865/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0812 - accuracy: 0.9911 - val_loss: 0.0727 - val_accuracy: 0.9958
Epoch 866/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0812 - accuracy: 0.9911 - val_loss: 0.0727 - val_accuracy: 0.9958
Epoch 867/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0811 - accuracy: 0.9911 - val_loss: 0.0726 - val_accuracy: 0.9958
Epoch 868/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0811 - accuracy: 0.9911 - val_loss: 0.0726 - val_accuracy: 0.9958
Epoch 869/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0810 - accuracy: 0.9911 - val_loss: 0.0725 - val_accuracy: 0.9958
Epoch 870/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0810 - accuracy: 0.9911 - val_loss: 0.0725 - val_accuracy: 0.9958
Epoch 871/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0809 - accuracy: 0.9911 - val_loss: 0.0724 - val_accuracy: 0.9958
Epoch 872/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0809 - accuracy: 0.9911 - val_loss: 0.0724 - val_accuracy: 0.9958
Epoch 873/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0808 - accuracy: 0.9911 - val_loss: 0.0723 - val_accuracy: 0.9958
Epoch 874/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0808 - accuracy: 0.9911 - val_loss: 0.0722 - val_accuracy: 0.9958
Epoch 875/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0807 - accuracy: 0.9911 - val_loss: 0.0722 - val_accuracy: 0.9958
Epoch 876/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0807 - accuracy: 0.9911 - val_loss: 0.0721 - val_accuracy: 0.9958
Epoch 877/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0806 - accuracy: 0.9911 - val_loss: 0.0721 - val_accuracy: 0.9958
Epoch 878/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0806 - accuracy: 0.9911 - val_loss: 0.0720 - val_accuracy: 0.9958
Epoch 879/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0805 - accuracy: 0.9911 - val_loss: 0.0720 - val_accuracy: 0.9958
Epoch 880/1000
560/560 [==============================] - 0s 42us/sample - loss: 0.0805 - accuracy: 0.9911 - val_loss: 0.0719 - val_accuracy: 0.9958
Epoch 881/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0804 - accuracy: 0.9911 - val_loss: 0.0718 - val_accuracy: 0.9958
Epoch 882/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0803 - accuracy: 0.9911 - val_loss: 0.0718 - val_accuracy: 0.9958
Epoch 883/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0803 - accuracy: 0.9911 - val_loss: 0.0717 - val_accuracy: 0.9958
Epoch 884/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0802 - accuracy: 0.9911 - val_loss: 0.0717 - val_accuracy: 0.9958
Epoch 885/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0802 - accuracy: 0.9911 - val_loss: 0.0716 - val_accuracy: 0.9958
Epoch 886/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0801 - accuracy: 0.9911 - val_loss: 0.0716 - val_accuracy: 0.9958
Epoch 887/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0801 - accuracy: 0.9911 - val_loss: 0.0715 - val_accuracy: 0.9958
Epoch 888/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0800 - accuracy: 0.9911 - val_loss: 0.0715 - val_accuracy: 0.9958
Epoch 889/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0800 - accuracy: 0.9911 - val_loss: 0.0714 - val_accuracy: 0.9958
Epoch 890/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0799 - accuracy: 0.9911 - val_loss: 0.0714 - val_accuracy: 0.9958
Epoch 891/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0799 - accuracy: 0.9911 - val_loss: 0.0713 - val_accuracy: 0.9958
Epoch 892/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0798 - accuracy: 0.9911 - val_loss: 0.0713 - val_accuracy: 0.9958
Epoch 893/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0798 - accuracy: 0.9911 - val_loss: 0.0712 - val_accuracy: 0.9958
Epoch 894/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0797 - accuracy: 0.9911 - val_loss: 0.0711 - val_accuracy: 0.9958
Epoch 895/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0797 - accuracy: 0.9911 - val_loss: 0.0711 - val_accuracy: 0.9958
Epoch 896/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0796 - accuracy: 0.9911 - val_loss: 0.0710 - val_accuracy: 0.9958
Epoch 897/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0796 - accuracy: 0.9911 - val_loss: 0.0710 - val_accuracy: 0.9958
Epoch 898/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0795 - accuracy: 0.9911 - val_loss: 0.0709 - val_accuracy: 0.9958
Epoch 899/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0795 - accuracy: 0.9911 - val_loss: 0.0709 - val_accuracy: 0.9958
Epoch 900/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0794 - accuracy: 0.9911 - val_loss: 0.0708 - val_accuracy: 0.9958
Epoch 901/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0794 - accuracy: 0.9911 - val_loss: 0.0708 - val_accuracy: 0.9958
Epoch 902/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0793 - accuracy: 0.9911 - val_loss: 0.0707 - val_accuracy: 0.9958
Epoch 903/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0793 - accuracy: 0.9911 - val_loss: 0.0707 - val_accuracy: 0.9958
Epoch 904/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0792 - accuracy: 0.9911 - val_loss: 0.0706 - val_accuracy: 0.9958
Epoch 905/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0792 - accuracy: 0.9911 - val_loss: 0.0706 - val_accuracy: 0.9958
Epoch 906/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0791 - accuracy: 0.9911 - val_loss: 0.0705 - val_accuracy: 0.9958
Epoch 907/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0791 - accuracy: 0.9911 - val_loss: 0.0705 - val_accuracy: 0.9958
Epoch 908/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0790 - accuracy: 0.9911 - val_loss: 0.0704 - val_accuracy: 0.9958
Epoch 909/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0790 - accuracy: 0.9911 - val_loss: 0.0703 - val_accuracy: 0.9958
Epoch 910/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0789 - accuracy: 0.9911 - val_loss: 0.0703 - val_accuracy: 0.9958
Epoch 911/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0789 - accuracy: 0.9911 - val_loss: 0.0702 - val_accuracy: 0.9958
Epoch 912/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0788 - accuracy: 0.9911 - val_loss: 0.0702 - val_accuracy: 0.9958
Epoch 913/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0788 - accuracy: 0.9911 - val_loss: 0.0701 - val_accuracy: 0.9958
Epoch 914/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0787 - accuracy: 0.9911 - val_loss: 0.0701 - val_accuracy: 0.9958
Epoch 915/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0787 - accuracy: 0.9911 - val_loss: 0.0700 - val_accuracy: 0.9958
Epoch 916/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0786 - accuracy: 0.9911 - val_loss: 0.0700 - val_accuracy: 0.9958
Epoch 917/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0786 - accuracy: 0.9911 - val_loss: 0.0699 - val_accuracy: 0.9958
Epoch 918/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0785 - accuracy: 0.9911 - val_loss: 0.0699 - val_accuracy: 0.9958
Epoch 919/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0785 - accuracy: 0.9911 - val_loss: 0.0698 - val_accuracy: 0.9958
Epoch 920/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0784 - accuracy: 0.9911 - val_loss: 0.0698 - val_accuracy: 0.9958
Epoch 921/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0784 - accuracy: 0.9911 - val_loss: 0.0697 - val_accuracy: 0.9958
Epoch 922/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0784 - accuracy: 0.9911 - val_loss: 0.0697 - val_accuracy: 0.9958
Epoch 923/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0783 - accuracy: 0.9911 - val_loss: 0.0696 - val_accuracy: 0.9958
Epoch 924/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0783 - accuracy: 0.9911 - val_loss: 0.0696 - val_accuracy: 0.9958
Epoch 925/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0782 - accuracy: 0.9911 - val_loss: 0.0695 - val_accuracy: 0.9958
Epoch 926/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0782 - accuracy: 0.9911 - val_loss: 0.0695 - val_accuracy: 0.9958
Epoch 927/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0781 - accuracy: 0.9911 - val_loss: 0.0694 - val_accuracy: 0.9958
Epoch 928/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0781 - accuracy: 0.9911 - val_loss: 0.0694 - val_accuracy: 0.9958
Epoch 929/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0780 - accuracy: 0.9911 - val_loss: 0.0693 - val_accuracy: 0.9958
Epoch 930/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0780 - accuracy: 0.9911 - val_loss: 0.0693 - val_accuracy: 0.9958
Epoch 931/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0779 - accuracy: 0.9911 - val_loss: 0.0692 - val_accuracy: 0.9958
Epoch 932/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0779 - accuracy: 0.9911 - val_loss: 0.0692 - val_accuracy: 0.9958
Epoch 933/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0778 - accuracy: 0.9911 - val_loss: 0.0691 - val_accuracy: 0.9958
Epoch 934/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0778 - accuracy: 0.9911 - val_loss: 0.0691 - val_accuracy: 0.9958
Epoch 935/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0777 - accuracy: 0.9911 - val_loss: 0.0690 - val_accuracy: 0.9958
Epoch 936/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0777 - accuracy: 0.9911 - val_loss: 0.0690 - val_accuracy: 0.9958
Epoch 937/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0776 - accuracy: 0.9911 - val_loss: 0.0689 - val_accuracy: 0.9958
Epoch 938/1000
560/560 [==============================] - 0s 48us/sample - loss: 0.0776 - accuracy: 0.9911 - val_loss: 0.0689 - val_accuracy: 0.9958
Epoch 939/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0775 - accuracy: 0.9911 - val_loss: 0.0688 - val_accuracy: 0.9958
Epoch 940/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0775 - accuracy: 0.9911 - val_loss: 0.0688 - val_accuracy: 0.9958
Epoch 941/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0775 - accuracy: 0.9911 - val_loss: 0.0687 - val_accuracy: 0.9958
Epoch 942/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0774 - accuracy: 0.9911 - val_loss: 0.0687 - val_accuracy: 0.9958
Epoch 943/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0774 - accuracy: 0.9911 - val_loss: 0.0686 - val_accuracy: 0.9958
Epoch 944/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0773 - accuracy: 0.9911 - val_loss: 0.0686 - val_accuracy: 0.9958
Epoch 945/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0773 - accuracy: 0.9911 - val_loss: 0.0685 - val_accuracy: 0.9958
Epoch 946/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0772 - accuracy: 0.9911 - val_loss: 0.0685 - val_accuracy: 0.9958
Epoch 947/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0772 - accuracy: 0.9911 - val_loss: 0.0684 - val_accuracy: 0.9958
Epoch 948/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0771 - accuracy: 0.9911 - val_loss: 0.0684 - val_accuracy: 0.9958
Epoch 949/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0771 - accuracy: 0.9911 - val_loss: 0.0683 - val_accuracy: 0.9958
Epoch 950/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0770 - accuracy: 0.9911 - val_loss: 0.0683 - val_accuracy: 0.9958
Epoch 951/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0770 - accuracy: 0.9911 - val_loss: 0.0682 - val_accuracy: 0.9958
Epoch 952/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0770 - accuracy: 0.9911 - val_loss: 0.0682 - val_accuracy: 0.9958
Epoch 953/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0769 - accuracy: 0.9911 - val_loss: 0.0681 - val_accuracy: 0.9958
Epoch 954/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0769 - accuracy: 0.9911 - val_loss: 0.0681 - val_accuracy: 0.9958
Epoch 955/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0768 - accuracy: 0.9911 - val_loss: 0.0680 - val_accuracy: 0.9958
Epoch 956/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0768 - accuracy: 0.9911 - val_loss: 0.0680 - val_accuracy: 0.9958
Epoch 957/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0767 - accuracy: 0.9911 - val_loss: 0.0679 - val_accuracy: 0.9958
Epoch 958/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0767 - accuracy: 0.9911 - val_loss: 0.0679 - val_accuracy: 0.9958
Epoch 959/1000
560/560 [==============================] - 0s 44us/sample - loss: 0.0766 - accuracy: 0.9911 - val_loss: 0.0678 - val_accuracy: 0.9958
Epoch 960/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0766 - accuracy: 0.9911 - val_loss: 0.0678 - val_accuracy: 0.9958
Epoch 961/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0765 - accuracy: 0.9911 - val_loss: 0.0678 - val_accuracy: 0.9958
Epoch 962/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0765 - accuracy: 0.9911 - val_loss: 0.0677 - val_accuracy: 0.9958
Epoch 963/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0765 - accuracy: 0.9911 - val_loss: 0.0677 - val_accuracy: 0.9958
Epoch 964/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0764 - accuracy: 0.9911 - val_loss: 0.0676 - val_accuracy: 0.9958
Epoch 965/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0764 - accuracy: 0.9911 - val_loss: 0.0676 - val_accuracy: 0.9958
Epoch 966/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0763 - accuracy: 0.9911 - val_loss: 0.0675 - val_accuracy: 0.9958
Epoch 967/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0763 - accuracy: 0.9911 - val_loss: 0.0675 - val_accuracy: 0.9958
Epoch 968/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0762 - accuracy: 0.9911 - val_loss: 0.0674 - val_accuracy: 0.9958
Epoch 969/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0762 - accuracy: 0.9911 - val_loss: 0.0674 - val_accuracy: 0.9958
Epoch 970/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0762 - accuracy: 0.9911 - val_loss: 0.0673 - val_accuracy: 0.9958
Epoch 971/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0761 - accuracy: 0.9911 - val_loss: 0.0673 - val_accuracy: 0.9958
Epoch 972/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0761 - accuracy: 0.9911 - val_loss: 0.0672 - val_accuracy: 0.9958
Epoch 973/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0760 - accuracy: 0.9911 - val_loss: 0.0672 - val_accuracy: 0.9958
Epoch 974/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0760 - accuracy: 0.9911 - val_loss: 0.0671 - val_accuracy: 0.9958
Epoch 975/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0759 - accuracy: 0.9911 - val_loss: 0.0671 - val_accuracy: 0.9958
Epoch 976/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0759 - accuracy: 0.9911 - val_loss: 0.0671 - val_accuracy: 0.9958
Epoch 977/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0758 - accuracy: 0.9911 - val_loss: 0.0670 - val_accuracy: 0.9958
Epoch 978/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0758 - accuracy: 0.9911 - val_loss: 0.0670 - val_accuracy: 0.9958
Epoch 979/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0758 - accuracy: 0.9911 - val_loss: 0.0669 - val_accuracy: 0.9958
Epoch 980/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0757 - accuracy: 0.9911 - val_loss: 0.0669 - val_accuracy: 0.9958
Epoch 981/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0757 - accuracy: 0.9911 - val_loss: 0.0668 - val_accuracy: 0.9958
Epoch 982/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0756 - accuracy: 0.9911 - val_loss: 0.0668 - val_accuracy: 0.9958
Epoch 983/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0756 - accuracy: 0.9911 - val_loss: 0.0667 - val_accuracy: 0.9958
Epoch 984/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0755 - accuracy: 0.9911 - val_loss: 0.0667 - val_accuracy: 0.9958
Epoch 985/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0755 - accuracy: 0.9911 - val_loss: 0.0666 - val_accuracy: 0.9958
Epoch 986/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0755 - accuracy: 0.9911 - val_loss: 0.0666 - val_accuracy: 0.9958
Epoch 987/1000
560/560 [==============================] - 0s 41us/sample - loss: 0.0754 - accuracy: 0.9911 - val_loss: 0.0665 - val_accuracy: 0.9958
Epoch 988/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0754 - accuracy: 0.9911 - val_loss: 0.0665 - val_accuracy: 0.9958
Epoch 989/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0753 - accuracy: 0.9911 - val_loss: 0.0665 - val_accuracy: 0.9958
Epoch 990/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0753 - accuracy: 0.9911 - val_loss: 0.0664 - val_accuracy: 0.9958
Epoch 991/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0753 - accuracy: 0.9911 - val_loss: 0.0664 - val_accuracy: 0.9958
Epoch 992/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0752 - accuracy: 0.9911 - val_loss: 0.0663 - val_accuracy: 0.9958
Epoch 993/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0752 - accuracy: 0.9911 - val_loss: 0.0663 - val_accuracy: 0.9958
Epoch 994/1000
560/560 [==============================] - 0s 46us/sample - loss: 0.0751 - accuracy: 0.9911 - val_loss: 0.0662 - val_accuracy: 0.9958
Epoch 995/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0751 - accuracy: 0.9911 - val_loss: 0.0662 - val_accuracy: 0.9958
Epoch 996/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0750 - accuracy: 0.9911 - val_loss: 0.0661 - val_accuracy: 0.9958
Epoch 997/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0750 - accuracy: 0.9911 - val_loss: 0.0661 - val_accuracy: 0.9958
Epoch 998/1000
560/560 [==============================] - 0s 45us/sample - loss: 0.0750 - accuracy: 0.9911 - val_loss: 0.0661 - val_accuracy: 0.9958
Epoch 999/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0749 - accuracy: 0.9911 - val_loss: 0.0660 - val_accuracy: 0.9958
Epoch 1000/1000
560/560 [==============================] - 0s 43us/sample - loss: 0.0749 - accuracy: 0.9911 - val_loss: 0.0660 - val_accuracy: 0.9958
查看评价指标的变化趋势
pd.DataFrame(history.history).plot(figsize=(8, 5))
plt.grid(True)
plt.xlabel('epoch')
plt.show()
模型验证
loss_test, accuracy_test = model.evaluate(x_test, y_test)
print(loss_test, accuracy_test)
200/200 [==============================] - 0s 35us/sample - loss: 0.0773 - accuracy: 1.0000
0.07725808694958687 1.0
模型预测
查看测试集的预测结果
y_test_pred = model.predict(x_test)
200/200 [==============================] - 0s 35us/sample - loss: 0.0773 - accuracy: 1.0000
0.07725808694958687 1.0
画出测试集的散点图和预测曲线
plt.plot(np.sort(x_test), y_test_pred[np.argsort(x_test)], color='r', label='predict')
plt.scatter(x_test, y_test, color='c', label='test dataset')
plt.legend()
plt.show()
查看logistic回归模型的系数w和截距b
w, b = model.layers[0].get_weights()
print('Weight={0} bias={1}'.format(w.item(), b.item()))
Weight=6.792507171630859 bias=-3.3748743534088135