cnn.py 文件源码

def train(img_shape):
    classes = ['ALB', 'BET', 'DOL', 'LAG', 'NoF', 'OTHER', 'SHARK', 'YFT']

    # Model
    model = Sequential()
    model.add(Convolution2D(
        32, 3, 3, input_shape=img_shape, activation='relu', W_constraint=maxnorm(3)))
    model.add(Dropout(0.2))
    model.add(Convolution2D(32, 3, 3, activation='relu', W_constraint=maxnorm(3)))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    model.add(Flatten())
    model.add(Dense(512, activation='relu', W_constraint=maxnorm(3)))
    model.add(Dropout(0.5))
    model.add(Dense(len(classes), activation='softmax'))

    features, labels = get_featurs_labels(img_shape)

    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
    model.fit(features, labels, nb_epoch=10, batch_size=32, validation_split=0.2, verbose=1)
    return model