keras-MNIST代码

一、代码1

import numpy as np
from keras.datasets import mnist
from keras.utils import np_utils
from keras.models import Sequential
from keras.layers import Dense
#from keras.optimizers import SGD
import tensorflow as tf
(x_train,y_train),(x_test,y_test)=mnist.load_data()
print('x_shape:',x_train.shape)
print('y_shape:',y_train.shape)
x_train=x_train.reshape(x_train.shape[0],-1)/255.0
x_test=x_test.reshape(x_test.shape[0],-1)/255.0

y_train=np_utils.to_categorical(y_train,num_classes=10)
y_test=np_utils.to_categorical(y_test,num_classes=10)

model=Sequential([
    Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')
     ])
#sgd=SGD(lr=0.2)
model.compile(
    optimizer=tf.keras.optimizers.SGD(learning_rate=0.2),
    loss='mse',
    metrics=['accuracy'],
)

model.fit(x_train,y_train,batch_size=32,epochs=10)

loss,accuracy=model.evaluate(x_test,y_test)
print('\ntest loss',loss)
print('accuracy',accuracy)

结果为：

x_shape: (60000, 28, 28)
y_shape: (60000,)

Epoch 1/10
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1875/1875 [==============================] - 6s 2ms/step - loss: 0.0379 - accuracy: 0.7729
Epoch 2/10
   1/1875 [..............................] - ETA: 0s - loss: 0.0248 - accuracy: 0.7812
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1875/1875 [==============================] - 4s 2ms/step - loss: 0.0204 - accuracy: 0.8804
Epoch 3/10
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1875/1875 [==============================] - 4s 2ms/step - loss: 0.0178 - accuracy: 0.8921
Epoch 4/10
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1875/1875 [==============================] - 4s 2ms/step - loss: 0.0165 - accuracy: 0.8991
Epoch 5/10
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1875/1875 [==============================] - 4s 2ms/step - loss: 0.0157 - accuracy: 0.9035
Epoch 6/10
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1875/1875 [==============================] - 4s 2ms/step - loss: 0.0151 - accuracy: 0.9063
Epoch 7/10
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Epoch 8/10
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Epoch 9/10
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Epoch 10/10
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1662/1875 [=========================>....] - ETA: 0s - loss: 0.0137 - accuracy: 0.9139
1689/1875 [==========================>...] - ETA: 0s - loss: 0.0137 - accuracy: 0.9139
1717/1875 [==========================>...] - ETA: 0s - loss: 0.0138 - accuracy: 0.9137
1744/1875 [==========================>...] - ETA: 0s - loss: 0.0137 - accuracy: 0.9139
1772/1875 [===========================>..] - ETA: 0s - loss: 0.0138 - accuracy: 0.9138
1795/1875 [===========================>..] - ETA: 0s - loss: 0.0137 - accuracy: 0.9140
1823/1875 [============================>.] - ETA: 0s - loss: 0.0138 - accuracy: 0.9138
1850/1875 [============================>.] - ETA: 0s - loss: 0.0138 - accuracy: 0.9136
1875/1875 [==============================] - 4s 2ms/step - loss: 0.0138 - accuracy: 0.9136
  1/313 [..............................] - ETA: 59s - loss: 0.0069 - accuracy: 0.9688
 28/313 [=>............................] - ETA: 0s - loss: 0.0143 - accuracy: 0.9107 
 58/313 [====>.........................] - ETA: 0s - loss: 0.0163 - accuracy: 0.8966
 88/313 [=======>......................] - ETA: 0s - loss: 0.0167 - accuracy: 0.8949
116/313 [==========>...................] - ETA: 0s - loss: 0.0159 - accuracy: 0.9011
142/313 [============>.................] - ETA: 0s - loss: 0.0167 - accuracy: 0.8941
172/313 [===============>..............] - ETA: 0s - loss: 0.0157 - accuracy: 0.9010
202/313 [==================>...........] - ETA: 0s - loss: 0.0149 - accuracy: 0.9066
233/313 [=====================>........] - ETA: 0s - loss: 0.0141 - accuracy: 0.9124
262/313 [========================>.....] - ETA: 0s - loss: 0.0138 - accuracy: 0.9145
292/313 [==========================>...] - ETA: 0s - loss: 0.0129 - accuracy: 0.9202
313/313 [==============================] - 1s 2ms/step - loss: 0.0131 - accuracy: 0.9182

test loss 0.01306093018501997
accuracy 0.9182000160217285