I have a tf.keras.layers.Conv2D constructed like so:

>>> conv2d_layer = tf.keras.layers.Conv2D(filters=128, kernel_size=(3, 3), strides=2)

For reference that layer is part of a network where the prior layer is prior_layer = Conv2D(filters=64, kernel_size=(3, 3), strides=2).

When I call conv2d_layer.get_weights(), it returns a list with two entries:

>>> [w.shape for w in conv2d_layer.get_weights()]
[(3, 3, 64, 128), (128,)]

Why are there two np.ndarrays in conv2d_layer.get_weights()? What are their respective meanings?

CodePudding user response：

The first shape is for the weights of your conv2D, and the second one is the bias for the same layer, which is represented by a vector.

Looking at the documentation, you can see

For example, a Dense layer returns a list of two values: the kernel matrix and the bias vector. These can be used to set the weights of another Dense layer:

CodePudding user response：

You have 128 convolution filters, each filter has a bias and a kernel. The kernel has a size 3x3. Furthermore, the kernel depth is equal to the input depth (it means 64 in this example). So we have (3, 3, 64) for a kernel, and we have 128 filters, so all filter's shapes are equal to (3, 3, 64, 128). Also, we have a bias for every filter so the shape of the second weight is equal to (128,)