model.py 文件源码

def CarND(input_shape, crop_shape):
    model = Sequential()

    # Crop
    # model.add(Cropping2D(((80,20),(1,1)), input_shape=input_shape, name="Crop"))
    model.add(Cropping2D(crop_shape, input_shape=input_shape, name="Crop"))

    # Resize
    model.add(AveragePooling2D(pool_size=(1,4), name="Resize", trainable=False))

    # Normalize input.
    model.add(BatchNormalization(axis=1, name="Normalize"))

    # Reduce dimensions through trainable convolution, activation, and
    # pooling layers.
    model.add(Convolution2D(24, 3, 3, subsample=(2,2), name="Convolution2D1", activation="relu"))
    model.add(MaxPooling2D(name="MaxPool1"))
    model.add(Convolution2D(36, 3, 3, subsample=(1,1), name="Convolution2D2", activation="relu"))
    model.add(MaxPooling2D(name="MaxPool2"))
    model.add(Convolution2D(48, 3, 3, subsample=(1,1), name="Convolution2D3", activation="relu"))
    model.add(MaxPooling2D(name="MaxPool3"))

    # Dropout for regularization
    model.add(Dropout(0.1, name="Dropout"))

    # Flatten input in a non-trainable layer before feeding into
    # fully-connected layers.
    model.add(Flatten(name="Flatten"))

    # Model steering through trainable layers comprising dense units
    # as ell as dropout units for regularization.
    model.add(Dense(100, activation="relu", name="FC2"))
    model.add(Dense(50, activation="relu", name="FC3"))
    model.add(Dense(10, activation="relu", name="FC4"))

    # Generate output (steering angles) with a single non-trainable
    # node.
    model.add(Dense(1, name="Readout", trainable=False))
    return model

# #+RESULTS:

#       Here is a summary of the actual model, as generated directly by
#       =model.summary= in Keras.