[FIXED] How to perform convolution with constant filter in tensorflow/keras

Issue

At a certain stage in a resnet, I have 6 features per image i.e. each example is of shape 1X8X8X6, I want to involve each feature with 4 constant filters (DWT) of size 1X2X2X1 with a stride of 2 to get 24 features in next layer and the image to become 1X4X4X24. However, I am unable to use tf.nn.conv2d or tf.nn.convolution for this purpose, conv2d says fourth dimension of input be equal to 3rd dimension of the filter, but how can I do this, I tried doing for the first filter but even this doesn't work:

x_in = np.random.randn(1,8,8,6)
kernel_in = np.array([[[[1],[1]],[[1],[1]]]])
kernel_in.shape
x = tf.constant(x_in, dtype=tf.float32)
kernel = tf.constant(kernel_in, dtype=tf.float32)
tf.nn.convolution(x, kernel, strides=[1, 1, 1, 1], padding='VALID')

Solution

try in this way

x_in = np.random.randn(1,8,8,6) # [batch, in_height, in_width, in_channels]
kernel_in = np.ones((2,2,6,24)) # [filter_height, filter_width, in_channels, out_channels]

x = tf.constant(x_in, dtype=tf.float32)
kernel = tf.constant(kernel_in, dtype=tf.float32)

tf.nn.conv2d(x, kernel, strides=[1, 2, 2, 1], padding='VALID')
# <tf.Tensor: shape=(1, 4, 4, 24), dtype=float32, numpy=....>

Answered By - Marco Cerliani