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from __future__ import print_functionimport numpy as npfrom keras.datasets import mnistfrom keras.models import Sequentialfrom keras.layers.core import Dense, Dropout, Activation,Flattenfrom keras.optimizers import RMSpropfrom keras.utils import np_utilsfrom keras.layers.convolutional import Conv2D, MaxPooling2Dimport matplotlib.pyplot as pltnp.random.seed(1671)  # for reproducibility# network and trainingNB_EPOCH = 20BATCH_SIZE = 128VERBOSE = 1NB_CLASSES = 10   # number of outputs = number of digitsOPTIMIZER = RMSprop() # optimizer, explainedin this chapterN_HIDDEN = 128VALIDATION_SPLIT=0.2 # how much TRAIN is reserved for VALIDATIONDROPOUT = 0.3# data: shuffled and split between train and test sets(X_train, y_train), (X_test, y_test) = mnist.load_data()#X_train is 60000 rows of 28x28 values --> reshaped in 60000 x 784RESHAPED = 784#X_train = X_train.reshape(60000, 28,28,1)X_test = X_test.reshape(10000, 28,28,1)X_train = X_train.astype('float32')X_test = X_test.astype('float32')# normalize X_train /= 255X_test /= 255print(X_train.shape[0], 'train samples')print(X_test.shape[0], 'test samples')# convert class vectors to binary class matricesY_train = np_utils.to_categorical(y_train, NB_CLASSES)Y_test = np_utils.to_categorical(y_test, NB_CLASSES)# M_HIDDEN hidden layers# 10 outputs# final stage is softmaxmodel = Sequential()model.add(Conv2D(32, kernel_size=5, padding='same',                        input_shape=(28, 28, 1)))#32个卷积核 ， model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2)))#池化层model.add(Dropout(0.25))#随机丢弃层model.add(Conv2D(64, kernel_size=5, padding='same'))#64个卷积核model.add(Activation('relu'))model.add(MaxPooling2D(pool_size=(2, 2)))#池化层model.add(Dropout(0.25))#随机丢弃层model.add(Flatten())model.add(Dense(1024))model.add(Activation('relu'))model.add(Dropout(0.5))model.add(Dense(NB_CLASSES))model.add(Activation('softmax'))model.summary()model.compile(loss='categorical_crossentropy',              optimizer=OPTIMIZER,              metrics=['accuracy'])history = model.fit(X_train, Y_train,                    batch_size=BATCH_SIZE, epochs=6,                    verbose=VERBOSE, validation_split=VALIDATION_SPLIT)score = model.evaluate(X_test, Y_test, verbose=VERBOSE)print("\nTest score:", score[0])print('Test accuracy:', score[1])# list all data in historyprint(history.history.keys())# summarize history for accuracyplt.plot(history.history['acc'])plt.plot(history.history['val_acc'])plt.title('model accuracy')plt.ylabel('accuracy')plt.xlabel('epoch')plt.legend(['train', 'test'], loc='upper left')plt.show()# summarize history for lossplt.plot(history.history['loss'])plt.plot(history.history['val_loss'])plt.title('model loss')plt.ylabel('loss')plt.xlabel('epoch')plt.legend(['train', 'test'], loc='upper left')plt.show()

model summary

_________________________________________________________________

Layer (type)                 Output Shape              Param #    ================================================================= conv2d_1 (Conv2D)            (None, 28, 28, 32)        832        _________________________________________________________________ activation_1 (Activation)    (None, 28, 28, 32)        0          _________________________________________________________________ max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0          _________________________________________________________________ dropout_1 (Dropout)          (None, 14, 14, 32)        0          _________________________________________________________________ conv2d_2 (Conv2D)            (None, 14, 14, 64)        51264      _________________________________________________________________ activation_2 (Activation)    (None, 14, 14, 64)        0          _________________________________________________________________ max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0          _________________________________________________________________ dropout_2 (Dropout)          (None, 7, 7, 64)          0          _________________________________________________________________ flatten_1 (Flatten)          (None, 3136)              0          _________________________________________________________________ dense_1 (Dense)              (None, 1024)              3212288    _________________________________________________________________ activation_3 (Activation)    (None, 1024)              0          _________________________________________________________________ dropout_3 (Dropout)          (None, 1024)              0          _________________________________________________________________ dense_2 (Dense)              (None, 10)                10250      _________________________________________________________________ activation_4 (Activation)    (None, 10)                0          ================================================================= Total params: 3,274,634

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