import numpy as np import tensorflow as tf from tensorflow.keras.layers import Conv2D N, n_H, n_W, n_C = 1, 5, 5, 3 n_filter = 5 k_size = 4 images = tf.random.uniform(minval=0, maxval=1, shape=(N,n_H,n_W,n_C)) # Forward Propogation(Tensorflow) conv = Conv2D(filters=n_filter, kernel_size=k_size) Y = conv(images) print(f"Y.shape : {Y.shape}\n") print(f"Y : \n{Y.numpy().squeeze()}\n") # Output Y.sh..
