I'm trying to convert a trained sign language classification solution in python to a C language headers so that I can deploy on a M4-cortex CPU board. In Python, I'm able to build model and train it and I can see it predicting with 90% accuracy. But I see an issue with number of weights used/generated in convolution layers
**Conv_1d configuration**
print(x_train.shape)
model = Sequential()
model.add(Conv1D(32,kernel_size=5, padding='same',
input_shape=x_train.shape[1:], name='conv1d_1'))
print(model.layers[0].kernel.numpy().shape)
**output:**
(1742, 45, 45)
**(5, 45, 32)**
According to above configuration
input dimension = 45x45x1 pixels of image(gray scale)
input channels = 1
output dimension = 45x45x32
output channesls = 32
kernel size = 5
As per the concept(w.r.t https://cs231n.github.io/convolutional-networks/)
number of weights = (input_channels) x (kernel_size) x (kernel_size) x (output_channels)=1x5x5x32=800
But keras model produces weights array of size = [5][45][32]=7200
I'm not sure if my interpretation of weight array in keras model is correct, I would be glad if someone can help me with this
Some bullets that should clarify your doubts.
You're formula for the number of weights can't be right because you're using a Conv1D, so the kernel size has only one dimension.
Defining the input shape x_train.shape[1:] = (45,45) corresponds to 45 filters applied on an array with 45 elements (again because it's a Conv1D).
Said so, the number of weights is:
# of weights = input_filters x kernel_size x output_filters = 45x5x32 = 7200 (without biases)
Considering that you have images, probably you're looking for Conv2D. In this case, the input shape should be (45,45,1), the kernel has two dimensions, and the number of parameters is exactly 800 (without biases)
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32,kernel_size=5, padding='same',
input_shape=(45, 45, 1), use_bias=False))
model.summary()
# Layer (type) Output Shape Param #
# conv (Conv2D) (None, 45, 45, 32) 800