https://github.com/jelly-lemon/keras_mnist_0112

用Keras實現MNIST手寫數字識別

MNIST手寫數字資料集介紹

MNIST手寫數字資料集來自美國國家標準與技術研究所，National Institute of Standards and Technology (NIST)。 訓練集 (training set) 由來自 250 個不同人手寫的數字構成，其中50%是高中學生，50% 來自人口普查局 (the Census Bureau) 的工作人員。測試集(test set) 也是同樣比例的手寫數字資料。

圖片特點：
這些圖片已進行尺寸規格化，並在固定尺寸的影象中居中
訓練集：60,000個示例
測試集：10,000個示例
大下：28x28

使用神經網路來識別手寫數字

環境

Win 10
PyCharm
Anaconda
Python 3.6.7
TensorFlow 1.10.0
Keras 2.2.0

MLP

CNN

MLP和CNN對比

使用Anaconda安裝tensorflow和keras的環境，並在pycharm設定環境

TensorFlow 1.10.0
Keras 2.2.0
tendorflow和keras版本要相互對應，否則程式會報錯。

該網站提供了python、tensorflow和keras對應的版本：

https://docs.floydhub.com/guides/environments/

最新版本的tensorflow自帶keras，不需要單獨下載。

CNN:卷積神經網路

1、匯入資料
2、歸一化
2、資料格式轉換
3、進行one-hot編碼
4、建立CNN模型
5、編譯模型
6、訓練模型
7、使用模型進行預測

原始碼

import keras
from keras import Sequential
from keras.datasets import mnist
from keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense
import matplotlib.pyplot as plt

# 一些引數
batch_size = 128
epochs = 10
num_classes = 10
img_rows, img_cols = 28, 28
input_shape = (img_rows, img_cols, 1)   # 輸入資料形狀

# 獲取資料
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 歸一化
x_train = x_train.astype('float32') / 255.0
x_test = x_test.astype('float32') / 255.0

# 改變資料形狀，格式為(n_samples, rows, cols, channels)
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)

# 控制檯列印輸出樣本數量資訊
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# 樣本標籤轉化為one-hot編碼格式
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# 建立CNN模型
model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(3, 3), activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(filters=64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(rate=0.2))
model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
model.add(Dropout(rate=0.5))
model.add(Dense(num_classes, activation='softmax'))
model.summary()     # 在控制檯輸出模型引數資訊
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])

# 訓練模型
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

# 預測
n = 10   # 給出需要預測的圖片數量，為了方便，只取前5張圖片
predicted_number = model.predict(x_test[:n], n)

# 畫圖
plt.figure(figsize=(10, 3))
for i in range(n):
    plt.subplot(1, n, i + 1)
    t = x_test[i].reshape(28, 28)   # 向量需要reshape為矩陣
    plt.imshow(t, cmap='gray')      # 以灰度圖顯示
    plt.subplots_adjust(wspace=2)   # 調整子圖間的間距，挨太緊了不好看
    # 第一個數字是真實標籤，第二個數字是預測數值
    # 如果預測正確，綠色顯示，否則紅色顯示
    # 預測結果是one-hot編碼，需要轉化為數字
    if y_test[i].argmax() == predicted_number[i].argmax():
        plt.title('%d,%d' % (y_test[i].argmax(), predicted_number[i].argmax()), color='green')
    else:
        plt.title('%d,%d' % (y_test[i].argmax(), predicted_number[i].argmax()), color='red')
    plt.xticks([])  # 取消x軸刻度
    plt.yticks([])
plt.show()
 

模型預測結果

模型訓練過程

D:\Users\Stark\Anaconda3\python.exe D:/Python/keras_mnist001/venv/Include/MNIST_CNN.py
Using TensorFlow backend.
D:\Users\Stark\Anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint8 = np.dtype([("qint8", np.int8, 1)])
D:\Users\Stark\Anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
D:\Users\Stark\Anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint16 = np.dtype([("qint16", np.int16, 1)])
D:\Users\Stark\Anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
D:\Users\Stark\Anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  _np_qint32 = np.dtype([("qint32", np.int32, 1)])
D:\Users\Stark\Anaconda3\lib\site-packages\tensorflow\python\framework\dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
  np_resource = np.dtype([("resource", np.ubyte, 1)])
60000 train samples
10000 test samples
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 24, 24, 64)        18496     
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 12, 12, 64)        0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 12, 12, 64)        0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 9216)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               1179776   
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 10)                1290      
=================================================================
Total params: 1,199,882
Trainable params: 1,199,882
Non-trainable params: 0
_________________________________________________________________
Train on 60000 samples, validate on 10000 samples
Epoch 1/10
2020-11-19 11:05:41.533253: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2

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51584/60000 [========================>.....] - ETA: 11s - loss: 0.2817 - acc: 0.9142
51712/60000 [========================>.....] - ETA: 10s - loss: 0.2814 - acc: 0.9143
51840/60000 [========================>.....] - ETA: 10s - loss: 0.2810 - acc: 0.9144
51968/60000 [========================>.....] - ETA: 10s - loss: 0.2804 - acc: 0.9146
52096/60000 [=========================>....] - ETA: 10s - loss: 0.2802 - acc: 0.9146
52224/60000 [=========================>....] - ETA: 10s - loss: 0.2801 - acc: 0.9147
52352/60000 [=========================>....] - ETA: 10s - loss: 0.2797 - acc: 0.9148
52480/60000 [=========================>....] - ETA: 9s - loss: 0.2794 - acc: 0.9149 
52608/60000 [=========================>....] - ETA: 9s - loss: 0.2788 - acc: 0.9151
52736/60000 [=========================>....] - ETA: 9s - loss: 0.2784 - acc: 0.9152
52864/60000 [=========================>....] - ETA: 9s - loss: 0.2779 - acc: 0.9153
52992/60000 [=========================>....] - ETA: 9s - loss: 0.2777 - acc: 0.9153
53120/60000 [=========================>....] - ETA: 9s - loss: 0.2774 - acc: 0.9155
53248/60000 [=========================>....] - ETA: 8s - loss: 0.2769 - acc: 0.9156
53376/60000 [=========================>....] - ETA: 8s - loss: 0.2764 - acc: 0.9158
53504/60000 [=========================>....] - ETA: 8s - loss: 0.2761 - acc: 0.9158
53632/60000 [=========================>....] - ETA: 8s - loss: 0.2756 - acc: 0.9160
53760/60000 [=========================>....] - ETA: 8s - loss: 0.2752 - acc: 0.9161
53888/60000 [=========================>....] - ETA: 8s - loss: 0.2747 - acc: 0.9163
54016/60000 [==========================>...] - ETA: 7s - loss: 0.2743 - acc: 0.9164
54144/60000 [==========================>...] - ETA: 7s - loss: 0.2738 - acc: 0.9165
54272/60000 [==========================>...] - ETA: 7s - loss: 0.2733 - acc: 0.9167
54400/60000 [==========================>...] - ETA: 7s - loss: 0.2728 - acc: 0.9168
54528/60000 [==========================>...] - ETA: 7s - loss: 0.2726 - acc: 0.9168
54656/60000 [==========================>...] - ETA: 7s - loss: 0.2722 - acc: 0.9169
54784/60000 [==========================>...] - ETA: 6s - loss: 0.2718 - acc: 0.9170
54912/60000 [==========================>...] - ETA: 6s - loss: 0.2714 - acc: 0.9172
55040/60000 [==========================>...] - ETA: 6s - loss: 0.2711 - acc: 0.9172
55168/60000 [==========================>...] - ETA: 6s - loss: 0.2708 - acc: 0.9173
55296/60000 [==========================>...] - ETA: 6s - loss: 0.2705 - acc: 0.9174
55424/60000 [==========================>...] - ETA: 5s - loss: 0.2701 - acc: 0.9175
55552/60000 [==========================>...] - ETA: 5s - loss: 0.2697 - acc: 0.9176
55680/60000 [==========================>...] - ETA: 5s - loss: 0.2694 - acc: 0.9178
55808/60000 [==========================>...] - ETA: 5s - loss: 0.2691 - acc: 0.9179
55936/60000 [==========================>...] - ETA: 5s - loss: 0.2688 - acc: 0.9180
56064/60000 [===========================>..] - ETA: 5s - loss: 0.2685 - acc: 0.9181
56192/60000 [===========================>..] - ETA: 4s - loss: 0.2681 - acc: 0.9182
56320/60000 [===========================>..] - ETA: 4s - loss: 0.2677 - acc: 0.9184
56448/60000 [===========================>..] - ETA: 4s - loss: 0.2673 - acc: 0.9185
56576/60000 [===========================>..] - ETA: 4s - loss: 0.2670 - acc: 0.9187
56704/60000 [===========================>..] - ETA: 4s - loss: 0.2668 - acc: 0.9187
56832/60000 [===========================>..] - ETA: 4s - loss: 0.2664 - acc: 0.9188
56960/60000 [===========================>..] - ETA: 3s - loss: 0.2664 - acc: 0.9189
57088/60000 [===========================>..] - ETA: 3s - loss: 0.2662 - acc: 0.9189
57216/60000 [===========================>..] - ETA: 3s - loss: 0.2658 - acc: 0.9191
57344/60000 [===========================>..] - ETA: 3s - loss: 0.2656 - acc: 0.9192
57472/60000 [===========================>..] - ETA: 3s - loss: 0.2652 - acc: 0.9193
57600/60000 [===========================>..] - ETA: 3s - loss: 0.2648 - acc: 0.9194
57728/60000 [===========================>..] - ETA: 2s - loss: 0.2643 - acc: 0.9196
57856/60000 [===========================>..] - ETA: 2s - loss: 0.2639 - acc: 0.9197
57984/60000 [===========================>..] - ETA: 2s - loss: 0.2635 - acc: 0.9198
58112/60000 [============================>.] - ETA: 2s - loss: 0.2630 - acc: 0.9200
58240/60000 [============================>.] - ETA: 2s - loss: 0.2627 - acc: 0.9201
58368/60000 [============================>.] - ETA: 2s - loss: 0.2623 - acc: 0.9202
58496/60000 [============================>.] - ETA: 1s - loss: 0.2620 - acc: 0.9203
58624/60000 [============================>.] - ETA: 1s - loss: 0.2616 - acc: 0.9204
58752/60000 [============================>.] - ETA: 1s - loss: 0.2613 - acc: 0.9205
58880/60000 [============================>.] - ETA: 1s - loss: 0.2609 - acc: 0.9206
59008/60000 [============================>.] - ETA: 1s - loss: 0.2605 - acc: 0.9207
59136/60000 [============================>.] - ETA: 1s - loss: 0.2603 - acc: 0.9208
59264/60000 [============================>.] - ETA: 0s - loss: 0.2599 - acc: 0.9209
59392/60000 [============================>.] - ETA: 0s - loss: 0.2595 - acc: 0.9210
59520/60000 [============================>.] - ETA: 0s - loss: 0.2591 - acc: 0.9211
59648/60000 [============================>.] - ETA: 0s - loss: 0.2588 - acc: 0.9212
59776/60000 [============================>.] - ETA: 0s - loss: 0.2585 - acc: 0.9213
59904/60000 [============================>.] - ETA: 0s - loss: 0.2582 - acc: 0.9214
60000/60000 [==============================] - 82s 1ms/step - loss: 0.2579 - acc: 0.9215 - val_loss: 0.0537 - val_acc: 0.9822
Epoch 2/10

  128/60000 [..............................] - ETA: 1:13 - loss: 0.0293 - acc: 0.9922
  256/60000 [..............................] - ETA: 1:14 - loss: 0.0388 - acc: 0.9922
  384/60000 [..............................] - ETA: 1:15 - loss: 0.0702 - acc: 0.9844
  512/60000 [..............................] - ETA: 1:15 - loss: 0.0736 - acc: 0.9844

。。。。。。

59136/60000 [============================>.] - ETA: 1s - loss: 0.0294 - acc: 0.9912
59264/60000 [============================>.] - ETA: 0s - loss: 0.0295 - acc: 0.9911
59392/60000 [============================>.] - ETA: 0s - loss: 0.0295 - acc: 0.9911
59520/60000 [============================>.] - ETA: 0s - loss: 0.0296 - acc: 0.9911
59648/60000 [============================>.] - ETA: 0s - loss: 0.0296 - acc: 0.9911
59776/60000 [============================>.] - ETA: 0s - loss: 0.0296 - acc: 0.9911
59904/60000 [============================>.] - ETA: 0s - loss: 0.0296 - acc: 0.9911
60000/60000 [==============================] - 78s 1ms/step - loss: 0.0297 - acc: 0.9911 - val_loss: 0.0255 - val_acc: 0.9917
Epoch 10/10

  128/60000 [..............................] - ETA: 1:18 - loss: 0.0095 - acc: 0.9922
  256/60000 [..............................] - ETA: 1:17 - loss: 0.0169 - acc: 0.9922
  384/60000 [..............................] - ETA: 1:16 - loss: 0.0194 - acc: 0.9870
  512/60000 [..............................] - ETA: 1:15 - loss: 0.0202 - acc: 0.9883
  640/60000 [..............................] - ETA: 1:15 - loss: 0.0170 - acc: 0.9906
  768/60000 [..............................] - ETA: 1:15 - loss: 0.0182 - acc: 0.9896
  896/60000 [..............................] - ETA: 1:15 - loss: 0.0203 - acc: 0.9888
 1024/60000 [..............................] - ETA: 1:14 - loss: 0.0189 - acc: 0.9902
 1152/60000 [..............................] - ETA: 1:14 - loss: 0.0198 - acc: 0.9905
 1280/60000 [..............................] - ETA: 1:14 - loss: 0.0221 - acc: 0.9906
 1408/60000 [..............................] - ETA: 1:13 - loss: 0.0219 - acc: 0.9901
 1536/60000 [..............................] - ETA: 1:13 - loss: 0.0203 - acc: 0.9909
 1664/60000 [..............................] - ETA: 1:13 - loss: 0.0198 - acc: 0.9910
 1792/60000 [..............................] - ETA: 1:13 - loss: 0.0196 - acc: 0.9911
 1920/60000 [..............................] - ETA: 1:12 - loss: 0.0229 - acc: 0.9901
 2048/60000 [>.............................] - ETA: 1:12 - loss: 0.0221 - acc: 0.9907
 2176/60000 [>.............................] - ETA: 1:12 - loss: 0.0231 - acc: 0.9908
 2304/60000 [>.............................] - ETA: 1:12 - loss: 0.0257 - acc: 0.9909
 2432/60000 [>.............................] - ETA: 1:12 - loss: 0.0254 - acc: 0.9910
 2560/60000 [>.............................] - ETA: 1:12 - loss: 0.0273 - acc: 0.9898
 2688/60000 [>.............................] - ETA: 1:11 - loss: 0.0265 - acc: 0.9903
 2816/60000 [>.............................] - ETA: 1:11 - loss: 0.0255 - acc: 0.9908
 2944/60000 [>.............................] - ETA: 1:11 - loss: 0.0255 - acc: 0.9908
 3072/60000 [>.............................] - ETA: 1:11 - loss: 0.0250 - acc: 0.9912
 3200/60000 [>.............................] - ETA: 1:11 - loss: 0.0242 - acc: 0.9916
 3328/60000 [>.............................] - ETA: 1:11 - loss: 0.0237 - acc: 0.9919
 3456/60000 [>.............................] - ETA: 1:10 - loss: 0.0254 - acc: 0.9916
 3584/60000 [>.............................] - ETA: 1:10 - loss: 0.0249 - acc: 0.9919
 3712/60000 [>.............................] - ETA: 1:10 - loss: 0.0244 - acc: 0.9922
 3840/60000 [>.............................] - ETA: 1:10 - loss: 0.0241 - acc: 0.9922
 3968/60000 [>.............................] - ETA: 1:10 - loss: 0.0239 - acc: 0.9922
 4096/60000 [=>............................] - ETA: 1:10 - loss: 0.0259 - acc: 0.9922
 4224/60000 [=>............................] - ETA: 1:09 - loss: 0.0262 - acc: 0.9922
 4352/60000 [=>............................] - ETA: 1:09 - loss: 0.0262 - acc: 0.9922
 4480/60000 [=>............................] - ETA: 1:09 - loss: 0.0259 - acc: 0.9924
 4608/60000 [=>............................] - ETA: 1:09 - loss: 0.0255 - acc: 0.9926
 4736/60000 [=>............................] - ETA: 1:09 - loss: 0.0252 - acc: 0.9924
 4864/60000 [=>............................] - ETA: 1:09 - loss: 0.0246 - acc: 0.9926
 4992/60000 [=>............................] - ETA: 1:08 - loss: 0.0247 - acc: 0.9926
 5120/60000 [=>............................] - ETA: 1:08 - loss: 0.0251 - acc: 0.9924
 5248/60000 [=>............................] - ETA: 1:08 - loss: 0.0250 - acc: 0.9924
 5376/60000 [=>............................] - ETA: 1:08 - loss: 0.0251 - acc: 0.9924
 5504/60000 [=>............................] - ETA: 1:08 - loss: 0.0248 - acc: 0.9924
 5632/60000 [=>............................] - ETA: 1:08 - loss: 0.0243 - acc: 0.9925
 5760/60000 [=>............................] - ETA: 1:08 - loss: 0.0250 - acc: 0.9924
 5888/60000 [=>............................] - ETA: 1:07 - loss: 0.0246 - acc: 0.9925
 6016/60000 [==>...........................] - ETA: 1:07 - loss: 0.0242 - acc: 0.9927
 6144/60000 [==>...........................] - ETA: 1:07 - loss: 0.0252 - acc: 0.9925
 6272/60000 [==>...........................] - ETA: 1:07 - loss: 0.0249 - acc: 0.9925
 6400/60000 [==>...........................] - ETA: 1:07 - loss: 0.0259 - acc: 0.9923
 6528/60000 [==>...........................] - ETA: 1:07 - loss: 0.0256 - acc: 0.9925
 6656/60000 [==>...........................] - ETA: 1:07 - loss: 0.0258 - acc: 0.9925
 6784/60000 [==>...........................] - ETA: 1:06 - loss: 0.0254 - acc: 0.9926
 6912/60000 [==>...........................] - ETA: 1:06 - loss: 0.0255 - acc: 0.9926
 7040/60000 [==>...........................] - ETA: 1:06 - loss: 0.0252 - acc: 0.9926
 7168/60000 [==>...........................] - ETA: 1:06 - loss: 0.0253 - acc: 0.9925
 7296/60000 [==>...........................] - ETA: 1:06 - loss: 0.0250 - acc: 0.9926
 7424/60000 [==>...........................] - ETA: 1:06 - loss: 0.0247 - acc: 0.9927
 7552/60000 [==>...........................] - ETA: 1:05 - loss: 0.0249 - acc: 0.9927
 7680/60000 [==>...........................] - ETA: 1:05 - loss: 0.0251 - acc: 0.9926
 7808/60000 [==>...........................] - ETA: 1:05 - loss: 0.0255 - acc: 0.9923
 7936/60000 [==>...........................] - ETA: 1:05 - loss: 0.0251 - acc: 0.9924
 8064/60000 [===>..........................] - ETA: 1:05 - loss: 0.0248 - acc: 0.9926
 8192/60000 [===>..........................] - ETA: 1:05 - loss: 0.0260 - acc: 0.9923
 8320/60000 [===>..........................] - ETA: 1:05 - loss: 0.0260 - acc: 0.9922
 8448/60000 [===>..........................] - ETA: 1:04 - loss: 0.0259 - acc: 0.9922
 8576/60000 [===>..........................] - ETA: 1:04 - loss: 0.0257 - acc: 0.9923
 8704/60000 [===>..........................] - ETA: 1:04 - loss: 0.0255 - acc: 0.9924
 8832/60000 [===>..........................] - ETA: 1:04 - loss: 0.0259 - acc: 0.9922
 8960/60000 [===>..........................] - ETA: 1:04 - loss: 0.0258 - acc: 0.9922
 9088/60000 [===>..........................] - ETA: 1:04 - loss: 0.0258 - acc: 0.9922
 9216/60000 [===>..........................] - ETA: 1:03 - loss: 0.0257 - acc: 0.9922
 9344/60000 [===>..........................] - ETA: 1:03 - loss: 0.0257 - acc: 0.9921
 9472/60000 [===>..........................] - ETA: 1:03 - loss: 0.0254 - acc: 0.9922
 9600/60000 [===>..........................] - ETA: 1:03 - loss: 0.0254 - acc: 0.9922
 9728/60000 [===>..........................] - ETA: 1:03 - loss: 0.0255 - acc: 0.9921
 9856/60000 [===>..........................] - ETA: 1:03 - loss: 0.0253 - acc: 0.9921
 9984/60000 [===>..........................] - ETA: 1:02 - loss: 0.0252 - acc: 0.9921
10112/60000 [====>.........................] - ETA: 1:02 - loss: 0.0251 - acc: 0.9921
10240/60000 [====>.........................] - ETA: 1:02 - loss: 0.0249 - acc: 0.9922
10368/60000 [====>.........................] - ETA: 1:02 - loss: 0.0248 - acc: 0.9923
10496/60000 [====>.........................] - ETA: 1:02 - loss: 0.0248 - acc: 0.9922
10624/60000 [====>.........................] - ETA: 1:02 - loss: 0.0246 - acc: 0.9923
10752/60000 [====>.........................] - ETA: 1:02 - loss: 0.0246 - acc: 0.9923
10880/60000 [====>.........................] - ETA: 1:01 - loss: 0.0244 - acc: 0.9924
11008/60000 [====>.........................] - ETA: 1:01 - loss: 0.0253 - acc: 0.9922
11136/60000 [====>.........................] - ETA: 1:01 - loss: 0.0252 - acc: 0.9922
11264/60000 [====>.........................] - ETA: 1:01 - loss: 0.0249 - acc: 0.9923
11392/60000 [====>.........................] - ETA: 1:01 - loss: 0.0250 - acc: 0.9922
11520/60000 [====>.........................] - ETA: 1:01 - loss: 0.0248 - acc: 0.9922
11648/60000 [====>.........................] - ETA: 1:00 - loss: 0.0246 - acc: 0.9923
11776/60000 [====>.........................] - ETA: 1:00 - loss: 0.0246 - acc: 0.9923
11904/60000 [====>.........................] - ETA: 1:00 - loss: 0.0244 - acc: 0.9924
12032/60000 [=====>........................] - ETA: 1:00 - loss: 0.0245 - acc: 0.9922
12160/60000 [=====>........................] - ETA: 1:00 - loss: 0.0243 - acc: 0.9923
12288/60000 [=====>........................] - ETA: 1:00 - loss: 0.0241 - acc: 0.9924
12416/60000 [=====>........................] - ETA: 59s - loss: 0.0239 - acc: 0.9923 
12544/60000 [=====>........................] - ETA: 59s - loss: 0.0238 - acc: 0.9923
12672/60000 [=====>........................] - ETA: 59s - loss: 0.0237 - acc: 0.9923
12800/60000 [=====>........................] - ETA: 59s - loss: 0.0236 - acc: 0.9924
12928/60000 [=====>........................] - ETA: 59s - loss: 0.0238 - acc: 0.9923
13056/60000 [=====>........................] - ETA: 59s - loss: 0.0236 - acc: 0.9924
13184/60000 [=====>........................] - ETA: 58s - loss: 0.0238 - acc: 0.9924
13312/60000 [=====>........................] - ETA: 58s - loss: 0.0236 - acc: 0.9924
13440/60000 [=====>........................] - ETA: 58s - loss: 0.0235 - acc: 0.9925
13568/60000 [=====>........................] - ETA: 58s - loss: 0.0234 - acc: 0.9925
13696/60000 [=====>........................] - ETA: 58s - loss: 0.0236 - acc: 0.9923
13824/60000 [=====>........................] - ETA: 58s - loss: 0.0235 - acc: 0.9923
13952/60000 [=====>........................] - ETA: 57s - loss: 0.0235 - acc: 0.9923
14080/60000 [======>.......................] - ETA: 57s - loss: 0.0233 - acc: 0.9924
14208/60000 [======>.......................] - ETA: 57s - loss: 0.0239 - acc: 0.9923
14336/60000 [======>.......................] - ETA: 57s - loss: 0.0237 - acc: 0.9924
14464/60000 [======>.......................] - ETA: 57s - loss: 0.0239 - acc: 0.9923
14592/60000 [======>.......................] - ETA: 57s - loss: 0.0245 - acc: 0.9921
14720/60000 [======>.......................] - ETA: 56s - loss: 0.0245 - acc: 0.9920
14848/60000 [======>.......................] - ETA: 56s - loss: 0.0244 - acc: 0.9921
14976/60000 [======>.......................] - ETA: 56s - loss: 0.0243 - acc: 0.9921
15104/60000 [======>.......................] - ETA: 56s - loss: 0.0243 - acc: 0.9921
15232/60000 [======>.......................] - ETA: 56s - loss: 0.0241 - acc: 0.9921
15360/60000 [======>.......................] - ETA: 56s - loss: 0.0241 - acc: 0.9921
15488/60000 [======>.......................] - ETA: 55s - loss: 0.0241 - acc: 0.9921
15616/60000 [======>.......................] - ETA: 55s - loss: 0.0242 - acc: 0.9920
15744/60000 [======>.......................] - ETA: 55s - loss: 0.0240 - acc: 0.9920
15872/60000 [======>.......................] - ETA: 55s - loss: 0.0242 - acc: 0.9920
16000/60000 [=======>......................] - ETA: 55s - loss: 0.0246 - acc: 0.9919
16128/60000 [=======>......................] - ETA: 55s - loss: 0.0248 - acc: 0.9918
16256/60000 [=======>......................] - ETA: 55s - loss: 0.0249 - acc: 0.9918
16384/60000 [=======>......................] - ETA: 54s - loss: 0.0251 - acc: 0.9916
16512/60000 [=======>......................] - ETA: 54s - loss: 0.0253 - acc: 0.9916
16640/60000 [=======>......................] - ETA: 54s - loss: 0.0265 - acc: 0.9913
16768/60000 [=======>......................] - ETA: 54s - loss: 0.0265 - acc: 0.9914
16896/60000 [=======>......................] - ETA: 54s - loss: 0.0264 - acc: 0.9915
17024/60000 [=======>......................] - ETA: 54s - loss: 0.0263 - acc: 0.9915
17152/60000 [=======>......................] - ETA: 53s - loss: 0.0265 - acc: 0.9914
17280/60000 [=======>......................] - ETA: 53s - loss: 0.0263 - acc: 0.9914
17408/60000 [=======>......................] - ETA: 53s - loss: 0.0264 - acc: 0.9914
17536/60000 [=======>......................] - ETA: 53s - loss: 0.0262 - acc: 0.9914
17664/60000 [=======>......................] - ETA: 53s - loss: 0.0261 - acc: 0.9915
17792/60000 [=======>......................] - ETA: 53s - loss: 0.0261 - acc: 0.9915
17920/60000 [=======>......................] - ETA: 52s - loss: 0.0263 - acc: 0.9914
18048/60000 [========>.....................] - ETA: 52s - loss: 0.0267 - acc: 0.9914
18176/60000 [========>.....................] - ETA: 52s - loss: 0.0266 - acc: 0.9914
18304/60000 [========>.....................] - ETA: 52s - loss: 0.0266 - acc: 0.9914
18432/60000 [========>.....................] - ETA: 52s - loss: 0.0265 - acc: 0.9914
18560/60000 [========>.....................] - ETA: 52s - loss: 0.0264 - acc: 0.9915
18688/60000 [========>.....................] - ETA: 52s - loss: 0.0264 - acc: 0.9914
18816/60000 [========>.....................] - ETA: 51s - loss: 0.0268 - acc: 0.9914
18944/60000 [========>.....................] - ETA: 51s - loss: 0.0270 - acc: 0.9914
19072/60000 [========>.....................] - ETA: 51s - loss: 0.0271 - acc: 0.9914
19200/60000 [========>.....................] - ETA: 51s - loss: 0.0270 - acc: 0.9914
19328/60000 [========>.....................] - ETA: 51s - loss: 0.0269 - acc: 0.9915
19456/60000 [========>.....................] - ETA: 51s - loss: 0.0269 - acc: 0.9914
19584/60000 [========>.....................] - ETA: 50s - loss: 0.0270 - acc: 0.9914
19712/60000 [========>.....................] - ETA: 50s - loss: 0.0268 - acc: 0.9915
19840/60000 [========>.....................] - ETA: 50s - loss: 0.0269 - acc: 0.9914
19968/60000 [========>.....................] - ETA: 50s - loss: 0.0268 - acc: 0.9914
20096/60000 [=========>....................] - ETA: 50s - loss: 0.0267 - acc: 0.9915
20224/60000 [=========>....................] - ETA: 50s - loss: 0.0267 - acc: 0.9915
20352/60000 [=========>....................] - ETA: 49s - loss: 0.0266 - acc: 0.9915
20480/60000 [=========>....................] - ETA: 49s - loss: 0.0267 - acc: 0.9914
20608/60000 [=========>....................] - ETA: 49s - loss: 0.0267 - acc: 0.9914
20736/60000 [=========>....................] - ETA: 49s - loss: 0.0268 - acc: 0.9914
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59904/60000 [============================>.] - ETA: 0s - loss: 0.0281 - acc: 0.9913
60000/60000 [==============================] - 79s 1ms/step - loss: 0.0282 - acc: 0.9912 - val_loss: 0.0255 - val_acc: 0.9913

Process finished with exit code 0