__init__() got multiple values for argument 'padding'

Question

    def get_model(summary=False, backend='tf'):
        """ Return the Keras model of the network
        """
        model = Sequential()
        if backend == 'tf':
            input_shape=(256, 80, 60, 1) # l, h, w, c
        else:
            input_shape=(1, 256, 80, 60) # c, l, h, w
        model.add(Convolution3D(64, 3, 3, 3, activation='relu',
                                padding='same', name='conv1',
                                input_shape=input_shape))
        model.add(MaxPooling3D(pool_size=(1, 2, 2), strides=(1, 2, 2),
                               padding='valid', name='pool1'))
        # 2nd layer group
        model.add(Convolution3D(128, 3, 3, 3, activation='relu',
                                padding='same', name='conv2'))
        model.add(MaxPooling3D(pool_size=(2, 2, 2), strides=(2, 2, 2),
                               padding='valid', name='pool2'))
+ other layers as well

if __name__ == '__main__':
    model = get_model(summary=True,backend='tf')

I am trying to implement the c3d model for video classification.

Input size = 256 X 80 X 60 X 1

The error is showing in the main function.

I am trying to use the C3D model for video classification. 256 frames, 80 H, 60 W, 1 channel(grayscale) But encountering this prob of padding (earlier was using tf = 1.14.0, it worked fine now tf = 2.2.0)

Answer 1

This is the function signature:

tf.keras.layers.Conv3D(
    filters, kernel_size, strides=(1, 1, 1), padding='valid',
    data_format=None, dilation_rate=(1, 1, 1), groups=1, activation=None,
    use_bias=True, kernel_initializer='glorot_uniform',
    bias_initializer='zeros', kernel_regularizer=None,
    bias_regularizer=None, activity_regularizer=None, kernel_constraint=None,
    bias_constraint=None, **kwargs
)

It requires 2 positional arguments .

the first is a filter which is integer (64)

the second is kernel_size which is integer or tuple of 3 integers .

Because your input is not a (3, 3, 3) tuple it takes kernel_size=3 and then assigns the rest of 3, 3 to the keyed arguments which are strides and padding , but then it sees another padding= assignment so it errors.

bottom line, call:

model.add(Convolution3D(64, (3, 3, 3), activation='relu',