keras.py 文件源码

def default_n_linear(num_outputs):
    from keras.layers import Input, Dense, merge
    from keras.models import Model
    from keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization
    from keras.layers import Activation, Dropout, Flatten, Cropping2D, Lambda

    img_in = Input(shape=(120,160,3), name='img_in')
    x = img_in
    x = Cropping2D(cropping=((60,0), (0,0)))(x) #trim 60 pixels off top
    x = Lambda(lambda x: x/127.5 - 1.)(x) # normalize and re-center
    x = Convolution2D(24, (5,5), strides=(2,2), activation='relu')(x)
    x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x)
    x = Convolution2D(64, (5,5), strides=(1,1), activation='relu')(x)
    x = Convolution2D(64, (3,3), strides=(1,1), activation='relu')(x)
    x = Convolution2D(64, (3,3), strides=(1,1), activation='relu')(x)

    x = Flatten(name='flattened')(x)
    x = Dense(100, activation='relu')(x)
    x = Dropout(.1)(x)
    x = Dense(50, activation='relu')(x)
    x = Dropout(.1)(x)

    outputs = [] 

    for i in range(num_outputs):
        outputs.append(Dense(1, activation='linear', name='n_outputs' + str(i))(x))

    model = Model(inputs=[img_in], outputs=outputs)


    model.compile(optimizer='adam',
                  loss='mse')

    return model