#基于mnist数据集的手写数字识别
#构造了cnn网络拟合识别函数,前两层为卷积层,第三层为池化层,第四层为Flatten层,最后两层为全连接层
#基于Keras 2.1.1 Tensorflow 1.4.0
代码:
1 from __future__ import print_function 2 import numpy as np 3 np.random.seed(1337) 4 from keras.datasets import mnist 5 from keras.models import Sequential 6 from keras.layers import Dense, Dropout, Activation, Flatten 7 from keras.layers import Convolution2D, MaxPooling2D 8 from keras.utils import np_utils 9 from keras import backend as K 10 11 batch_size = 128 12 nb_classes = 10 13 nb_epoch = 12 14 15 img_rows, img_cols = 28, 28 16 nb_filters = 32 17 pool_size = (2,2) 18 kernel_size = (3,3) 19 (X_train, y_train), (X_test, y_test) = mnist.load_data() 20 21 X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1) 22 X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1) 23 input_shape = (img_rows, img_cols, 1) 24 X_train = X_train.astype('float32') 25 X_test = X_test.astype('float32') 26 X_train /= 255 27 X_test /= 255 28 29 Y_train = np_utils.to_categorical(y_train, nb_classes) 30 Y_test = np_utils.to_categorical(y_test, nb_classes) 31 # 建立序贯模型 32 model = Sequential() 33 34 model.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1],border_mode='valid',input_shape=input_shape)) 35 model.add(Activation('relu')) 36 37 38 model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1])) 39 model.add(Activation('relu')) 40 model.add(MaxPooling2D(pool_size=pool_size)) 41 model.add(Dropout(0.25)) 42 model.add(Flatten()) 43 model.add(Dense(128)) 44 model.add(Activation('relu')) 45 model.add(Dropout(0.5)) 46 model.add(Dense(nb_classes)) 47 model.add(Activation('softmax')) 48 49 50 model.summary() 51 model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy']) 52 model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test)) 53 54 score = model.evaluate(X_test, Y_test, verbose=0) 55 print('Test score:', score[0]) 56 print('Test accuracy:', score[1])
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