Calculating size of output of a Conv layer in CNN model

Question

In convolutional Neural Networks, How to know the output of a specific conv layer? (I am using keras to build a CNN model)

For example if I am using one dimensional conv layer, where number_of_filters=20, kernel_size=10, and input_shape(500,1)

 cnn.add(Conv1D(20,kernel_size=10,strides=1, padding="same",activation="sigmoid",input_shape=(Dimension_of_input,1)))

and if I am using two dimensional conv layer, where number_of_filters=64, kernal_size=(5,100), input_shape= (5,720,1) (height,width,channel)

 Conv2D(64, (5, 100),
       padding="same",
       activation="sigmoid",
       data_format="channels_last",
       input_shape=(5,720,1)

what is the number of output in the above two conv layers? Is there any equation that can be used to know the number of outputs of a conv layer in convolution neural network?

Answer 1

Yes, there are equations for it, you can find them in the CS231N course website. But as this is a programming site, Keras provides an easy way to get this information programmaticaly, by using the summary function of a Model.

model = Sequential()
fill model with layers
model.summary()

This will print in terminal/console all the layer information, such as input shapes, output shapes, and number of parameters for each layer.