Layers Kernel sizeStridePadding Input size Activation Conv1 20035011 11000012 Relu Pool1 22221 519712 Conv2 202411 5997 Relu Pool2 22220 10218 Flatten 10104 Linear Classifier16 400

In this model, there are two convolutional layers (Conv1 and Conv2) followed by two pooling layers (Pool1 and Pool2). The kernel size, stride, and padding for each layer are as follows:

Conv1:

• Kernel size: 200*3
• Stride: 50*1
• Padding: 1
• Input size: 110,00012

Pool1:

• Kernel size: 2*2
• Stride: 2*2
• Padding: 1
• Input size: 519712

Conv2:

• Kernel size: 20*2
• Stride: 4*1
• Padding: 1
• Input size: 5997

Pool2:

• Kernel size: 2*2
• Stride: 2*2
• Padding: 0
• Input size: 10218

After the pooling layers, the data is flattened into a 1-dimensional vector with dimensions 10104.

Finally, there is a linear classifier layer with input size 400 and output size 1*6