keras实现CNN手写字识别

分手后的思念是犯贱 2022-12-15 03:37 172阅读 0赞

# to try tensorflow, un-comment following two lines
    # import os
    # os.environ['KERAS_BACKEND']='tensorflow'
    
    import numpy as np
    np.random.seed(1337)  # for reproducibility
    from keras.datasets import mnist
    from keras.utils import np_utils
    from keras.models import Sequential
    from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten
    from keras.optimizers import Adam
    
    # download the mnist to the path '~/.keras/datasets/' if it is the first time to be called
    # training X shape (60000, 28x28), Y shape (60000, ). test X shape (10000, 28x28), Y shape (10000, )
    (X_train, y_train), (X_test, y_test) = mnist.load_data()
    
    # data pre-processing
    X_train = X_train.reshape(-1, 1,28, 28)/255.
    X_test = X_test.reshape(-1, 1,28, 28)/255.
    y_train = np_utils.to_categorical(y_train, num_classes=10)
    y_test = np_utils.to_categorical(y_test, num_classes=10)
    
    # Another way to build your CNN
    model = Sequential()
    
    # Conv layer 1 output shape (32, 28, 28)
    model.add(Convolution2D(
        batch_input_shape=(None, 1, 28, 28),
        filters=32,
        kernel_size=5,
        strides=1,
        padding='same',     # Padding method
        data_format='channels_first',
    ))
    model.add(Activation('relu'))
    
    # Pooling layer 1 (max pooling) output shape (32, 14, 14)
    model.add(MaxPooling2D(
        pool_size=2,
        strides=2,
        padding='same',    # Padding method
        data_format='channels_first',
    ))
    
    # Conv layer 2 output shape (64, 14, 14)
    model.add(Convolution2D(64, 5, strides=1, padding='same', data_format='channels_first'))
    model.add(Activation('relu'))
    
    # Pooling layer 2 (max pooling) output shape (64, 7, 7)
    model.add(MaxPooling2D(2, 2, 'same', data_format='channels_first'))
    
    # Fully connected layer 1 input shape (64 * 7 * 7) = (3136), output shape (1024)
    model.add(Flatten())
    model.add(Dense(1024))
    model.add(Activation('relu'))
    
    # Fully connected layer 2 to shape (10) for 10 classes
    model.add(Dense(10))
    model.add(Activation('softmax'))
    
    # Another way to define your optimizer
    adam = Adam(lr=1e-4)
    
    # We add metrics to get more results you want to see
    model.compile(optimizer=adam,
                  loss='categorical_crossentropy',
                  metrics=['accuracy'])
    
    print('Training ------------')
    # Another way to train the model
    model.fit(X_train, y_train, epochs=1, batch_size=64,)
    
    print('\nTesting ------------')
    # Evaluate the model with the metrics we defined earlier
    loss, accuracy = model.evaluate(X_test, y_test)
    
    print('\ntest loss: ', loss)
    print('\ntest accuracy: ', accuracy)