FInding number of trainable parameters in CNN

Question

I have a convolutional layer $g$ with 10 feature maps given by: $$g(x^i) = \sigma([z_1,z_2,\dots,z_{10}])$$ where $z_j = x^i \cdot w_j$ for some convolutional kernel $w_j$ of size 3. Each $x^i$ is padded with a zero at each end. And $x_i$ is a set of 1-D signals in $R^{100}$.

I'm trying to find the number of trainable parameters in this layer using Keras.

from keras.models import Sequential
from keras.layers import Conv2D, Dense

model = Sequential()
model.add(Conv2D(filters = 10,
                 kernel_size = (3,3),
                 input_shape = (1000,100,1),
                 use_bias = False))
model.summary()


Output :
----------
Model: "sequential_26"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_22 (Conv2D)           (None, 998, 98, 10)       90        
=================================================================
Total params: 90
Trainable params: 90
Non-trainable params: 0

Am I creating the correct arguments in Keras for this convolutional layer? I think the actual amount of trainable parameters is 30? But i dont know what to place for the arguments. Could someone also give me intuition to the actual number of learnable parameters? Thanks.

Answer 1

You've specified 10 filters in a 2d convolution, each of size $3\times 3$ so you have $3 \times 3 \times 10=90$ trainable parameters.

You have 1d data, but you're using a 2d convolution. Perhaps this is a typo and you meant Conv1D?