keras.py 文件源码

def default_categorical():
    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, Dense

    img_in = Input(shape=(120, 160, 3), name='img_in')                      # First layer, input layer, Shape comes from camera.py resolution, RGB
    x = img_in
    x = Convolution2D(24, (5,5), strides=(2,2), activation='relu')(x)       # 24 features, 5 pixel x 5 pixel kernel (convolution, feauture) window, 2wx2h stride, relu activation
    x = Convolution2D(32, (5,5), strides=(2,2), activation='relu')(x)       # 32 features, 5px5p kernel window, 2wx2h stride, relu activatiion
    x = Convolution2D(64, (5,5), strides=(2,2), activation='relu')(x)       # 64 features, 5px5p kernal window, 2wx2h stride, relu
    x = Convolution2D(64, (3,3), strides=(2,2), activation='relu')(x)       # 64 features, 3px3p kernal window, 2wx2h stride, relu
    x = Convolution2D(64, (3,3), strides=(1,1), activation='relu')(x)       # 64 features, 3px3p kernal window, 1wx1h stride, relu

    # Possibly add MaxPooling (will make it less sensitive to position in image).  Camera angle fixed, so may not to be needed

    x = Flatten(name='flattened')(x)                                        # Flatten to 1D (Fully connected)
    x = Dense(100, activation='relu')(x)                                    # Classify the data into 100 features, make all negatives 0
    x = Dropout(.1)(x)                                                      # Randomly drop out (turn off) 10% of the neurons (Prevent overfitting)
    x = Dense(50, activation='relu')(x)                                     # Classify the data into 50 features, make all negatives 0
    x = Dropout(.1)(x)                                                      # Randomly drop out 10% of the neurons (Prevent overfitting)
    #categorical output of the angle
    angle_out = Dense(15, activation='softmax', name='angle_out')(x)        # Connect every input with every output and output 15 hidden units. Use Softmax to give percentage. 15 categories and find best one based off percentage 0.0-1.0

    #continous output of throttle
    throttle_out = Dense(1, activation='relu', name='throttle_out')(x)      # Reduce to 1 number, Positive number only

    model = Model(inputs=[img_in], outputs=[angle_out, throttle_out])
    model.compile(optimizer='adam',
                  loss={'angle_out': 'categorical_crossentropy', 
                        'throttle_out': 'mean_absolute_error'},
                  loss_weights={'angle_out': 0.9, 'throttle_out': .001})

    return model