Consider the following code for Conv1D layer
# The inputs are 128-length vectors with 10 timesteps, and the batch size # is 4. input_shape = (4, 10, 128) x = tf.random.normal(input_shape) y = tf.keras.layers.Conv1D(32, 3, activation='relu',input_shape=input_shape[1:])(x) print(y.shape) (4, 8, 32)
It has been given that there are 10 vectors, with each of length 128. Then how does the output will be of shape
If we apply a filter of size 3, we will then get a vector of length 126, if stride is 1. But, I cannot see 126 anywhere in the output.
How to understand the shapes of input and output?