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  • Keras CNN 数字识别

    难点是版本变化后 方法不再匹配 ,同时每个方法具体意义需要揣摩
    
    # Larger CNN for the MNIST Dataset
    # 2.Negative dimension size caused by subtracting 5 from 1 for 'conv2d_4/convolution' (op: 'Conv2D') with input shapes
    # 3.UserWarning: Update your `Conv2D` call to the Keras 2 API: http://blog.csdn.net/johinieli/article/details/69222956
    # 4.Error when checking input: expected conv2d_1_input to have shape (None, 28, 28, 1) but got array with shape (60000, 1, 28, 28)
    
    # talk to wumi,you good .
    
    # python 3.5.4
    # keras.__version__  : 2.0.6
    # thensorflow 1.2.1
    # theano 0.10.0beta1
    
    # 不错的blog  http://blog.csdn.net/shizhengxin123/article/details/72383728
    
    import numpy
    from keras.datasets import mnist
    from keras.models import Sequential
    from keras.layers import Dense
    from keras.layers import Dropout
    from keras.layers import Flatten
    from keras.layers.convolutional import Conv2D
    from keras.layers.convolutional import MaxPooling2D
    from keras.utils import np_utils
    import matplotlib.pyplot as plt
    from keras.constraints import maxnorm
    from keras.optimizers import SGD
    # fix random seed for reproducibility
    seed = 7
    numpy.random.seed(seed)
    # load data
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    # reshape to be [samples][pixels][width][height]
    X_train = X_train.reshape(X_train.shape[0], 28, 28,1).astype('float32')
    X_test = X_test.reshape(X_test.shape[0],28, 28,1).astype('float32')
    #X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32')
    #X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32')    <---4 
    # normalize inputs from 0-255 to 0-1
    X_train = X_train / 255
    X_test = X_test / 255
    # one hot encode outputs
    y_train = np_utils.to_categorical(y_train)
    y_test = np_utils.to_categorical(y_test)
    num_classes = y_test.shape[1]
    ###raw
    # define the larger model
    def larger_model():
        # create model
        model = Sequential()
        model.add(Conv2D(30, (5, 5), padding='valid', input_shape=(28, 28,1), activation='relu'))
    	#model.add(Conv2D(30, (5, 5), padding='valid', input_shape=(28, 28,1), activation='relu'))   <----3,2
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.4))
        model.add(Conv2D(15, (3, 3), activation='relu'))
        model.add(MaxPooling2D(pool_size=(2, 2)))
        model.add(Dropout(0.4))
        model.add(Flatten())
        model.add(Dense(128, activation='relu'))
        model.add(Dropout(0.4))
        model.add(Dense(50, activation='relu'))
        model.add(Dropout(0.4))
        model.add(Dense(num_classes, activation='softmax'))
        # Compile model
        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
        return model
    
    # build the model
    model = larger_model()
    # Fit the model
    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200, verbose=2)   # epochs 200 too bigger
    #model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=200, batch_size=200, verbose=2)
    # Final evaluation of the model
    scores = model.evaluate(X_test, y_test, verbose=0)
    print("Large CNN Error: %.2f%%" % (100-scores[1]*100))
    scores1 = model.evaluate(X_test[0], y_test[0], verbose=0)
    print(scores1)

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  • 原文地址:https://www.cnblogs.com/TendToBigData/p/10501211.html
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