Keras中利用两层卷积神经网络进行mnist手写识别

代码

import numpy as np

np.random.seed(1337)  # for reproducibility
from keras.datasets import mnist
from keras.datasets import cifar10
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from keras import backend as K
import h5py

# 全局变量
batch_size = 256
nb_classes = 10
epochs = 12
# input image dimensions
img_rows, img_cols = 28, 28
# number of convolutional filters to use
nb_filters = 32
# size of pooling area for max pooling
pool_size = (2, 2)
# convolution kernel size
kernel_size = (3, 3)

# the data, shuffled and split between train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()



print(np.shape(X_train),np.shape(y_train))
print(np.shape(X_test),np.shape(y_test))
print(type(X_train))
print(X_train.shape[0])
print(np.shape( X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)))
print('标签：',y_train[0:20])




#根据不同的backend定下不同的格式
if K.image_dim_ordering() == 'th':
    X_train = X_train.reshape(X_train.shape[0], 1, img_rows, img_cols)
    X_test = X_test.reshape(X_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)#把第三个通道1补上
    X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)#元组类型，元素不能更改

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255
print('X_train shape:', X_train.shape)
print(X_train.shape[0], 'train samples')
print(X_test.shape[0], 'test samples')

# 转换为one_hot类型
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

print(Y_train.shape,Y_train[0,0:11])
print(Y_test.shape,Y_test[0,0:11])
# 构建模型
model = Sequential()
"""
model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1],
                        border_mode='same',
                        input_shape=input_shape))
"""
model.add(Convolution2D(nb_filters, (kernel_size[0], kernel_size[1]),
                        padding='same',
                        input_shape=input_shape))  # 卷积层1
model.add(Activation('relu'))  # 激活层
model.add(MaxPooling2D(pool_size=pool_size))#池化层
model.add(Convolution2D(nb_filters, (kernel_size[0], kernel_size[1])))  # 卷积层2
model.add(Activation('relu'))  # 激活层
model.add(MaxPooling2D(pool_size=pool_size))  # 池化层
model.add(Dropout(0.25))  # 神经元随机失活
model.add(Flatten())  # 拉成一维数据
model.add(Dense(128))  # 全连接层1
model.add(Activation('relu'))  # 激活层
model.add(Dropout(0.5))  # 随机失活
model.add(Dense(nb_classes))  # 全连接层2
model.add(Activation('softmax'))  # Softmax评分

# 编译模型
model.compile(loss='categorical_crossentropy',
              optimizer='adadelta',
              metrics=['accuracy'])
# 训练模型
model.fit(X_train, Y_train, batch_size=batch_size, epochs=epochs,
          verbose=1, validation_data=(X_test, Y_test))
# 评估模型
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])import numpy as np

np.random.seed(1337)