MNIST 数据集的 Keras 序列模型示例

以下是构建简单多层感知机（在第 5 章中详细介绍）的一个小例子，用于对 MNIST 集中的手写数字进行分类：

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import SGD
from keras import utils
import numpy as np

# define some hyper parameters
batch_size = 100 
n_inputs = 784
n_classes = 10 
n_epochs = 10 

# get the data
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# reshape the two dimensional 28 x 28 pixels
# sized images into a single vector of 784 pixels
x_train = x_train.reshape(60000, n_inputs)
x_test = x_test.reshape(10000, n_inputs)

# convert the input values to float32
x_train = x_train.astype(np.float32)
x_test = x_test.astype(np.float32)

# normalize the values of image vectors to fit under 1
x_train /= 255
x_test /= 255

# convert output data into one hot encoded format
y_train = utils.to_categorical(y_train, n_classes)
y_test = utils.to_categorical(y_test, n_classes)

# build a sequential model
model = Sequential()
# the first layer has to specify the dimensions of the input vector
model.add(Dense(units=128, activation='sigmoid', input_shape=(n_inputs,)))
# add dropout layer for preventing overfitting
model.add(Dropout(0.1))
model.add(Dense(units=128, activation='sigmoid'))
model.add(Dropout(0.1))
# output layer can only have the neurons equal to the number of outputs
model.add(Dense(units=n_classes, activation='softmax'))

# print the summary of our model
model.summary()

# compile the model
model.compile(loss='categorical_crossentropy',
              optimizer=SGD(),
              metrics=['accuracy'])

# train the model
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=n_epochs)

# evaluate the model and print the accuracy score
scores = model.evaluate(x_test, y_test)

print('\n loss:', scores[0])
print('\n accuracy:', scores[1])

我们从描述和训练 Keras 模型得到以下输出：

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_7 (Dense)              (None, 128)               100480    
_________________________________________________________________
dropout_5 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_8 (Dense)              (None, 128)               16512     
_________________________________________________________________
dropout_6 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_9 (Dense)              (None, 10)                1290      
=================================================================
Total params: 118,282
Trainable params: 118,282
Non-trainable params: 0
_________________________________________________________________
Epoch 1/10
60000/60000 [========================] - 3s - loss: 2.3018 - acc: 0.1312     
Epoch 2/10
60000/60000 [========================] - 2s - loss: 2.2395 - acc: 0.1920     
Epoch 3/10
60000/60000 [========================] - 2s - loss: 2.1539 - acc: 0.2843     
Epoch 4/10
60000/60000 [========================] - 2s - loss: 2.0214 - acc: 0.3856     
Epoch 5/10
60000/60000 [========================] - 3s - loss: 1.8269 - acc: 0.4739     
Epoch 6/10
60000/60000 [========================] - 2s - loss: 1.5973 - acc: 0.5426     
Epoch 7/10
60000/60000 [========================] - 2s - loss: 1.3846 - acc: 0.6028     
Epoch 8/10
60000/60000 [========================] - 3s - loss: 1.2133 - acc: 0.6502     
Epoch 9/10
60000/60000 [========================] - 3s - loss: 1.0821 - acc: 0.6842     
Epoch 10/10
60000/60000 [========================] - 3s - loss: 0.9799 - acc: 0.7157     

 loss: 0.859834249687
 accuracy: 0.788

您可以看到，在 Keras 中构建和训练模型是多么容易。

您可以从他们记录完备的网站 https://keras.io 获取有关 Keras 的更多信息。