keras实现的mnist手写数字识别(带测试的手写图)

import numpy as np np.random.seed(1337) # for reproducibility from keras.datasets import mnist 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 # 全局变量 batch_size = 128 nb_classes = 10 epochs = 2 # 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() # 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) X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) 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) #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(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]) json_string = model.to_json()#等价于 json_string = model.get_config() open('my_model_architecture.json','w').write(json_string) model.save_weights('my_model_weights.h5')