I never worked with
tensorflow, but according to the documentation of
Conv2D it’s defined as
tf.keras.layers.Conv2D( filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(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 )
As you can see
strides is the third parameter.
Now you use
Conv2D(24,5,5, strides = (2,2), input_shape= (66,200,3), activation='relu') where the third parameter is
5 and then you try to set
strides again with the keyword parameter. It seems there is one parameter too much in your call.
Imagine the little Python gnome handling your code: “OK, the boss wants an instance of
Conv2D. He sets the first argument
4, the second argument
2 and the third argument
2. Done with the positional arguments. Now let’s continue with the keyword arguments. Here we have
strides and … oh, I already have
strides, so I don’t know what to do. The boss might be angry so I’ll tell him exactly what happenend:
TypeError: __init__() got multiple values for argument 'strides'“
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