keras.py 文件源码

def default_imu(num_outputs, num_imu_inputs):
    '''
    Notes: this model depends on concatenate which failed on keras < 2.0.8
    '''

    from keras.layers import Input, Dense
    from keras.models import Model
    from keras.layers import Convolution2D, MaxPooling2D, Reshape, BatchNormalization
    from keras.layers import Activation, Dropout, Flatten, Cropping2D, Lambda
    from keras.layers.merge import concatenate

    img_in = Input(shape=(120,160,3), name='img_in')
    imu_in = Input(shape=(num_imu_inputs,), name="imu_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, (3,3), strides=(2,2), 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)

    y = imu_in
    y = Dense(14, activation='relu')(y)
    y = Dense(14, activation='relu')(y)
    y = Dense(14, activation='relu')(y)

    z = concatenate([x, y])
    z = Dense(50, activation='relu')(z)
    z = Dropout(.1)(z)
    z = Dense(50, activation='relu')(z)
    z = Dropout(.1)(z)

    outputs = [] 

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

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

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

    return model