Conv1- Kernel size 2003- Stride 501- Padding 1- Input size 11000012Pool1- Kernel size 22- Stride 22- Padding 1- Input size 519712Conv2- Kernel size 202- Stride 41- Padding 1- Input size 5997Pool2- Ker

Conv1:

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

Processing steps:

1. Apply the convolution operation to the input with a kernel size of 2003 and a stride of 501.
2. Apply padding of 1 to the input.
3. Calculate the output size using the formula: output_size = (input_size - kernel_size + 2 * padding) / stride + 1 = (10,000 - 200 + 2 * 1) / 50 + 1 = 197
4. The output size after convolution is 119712.

Pool1:

• Kernel size: 2*2
• Stride: 2*2
• Padding: 1
• Input size: 119712

Processing steps:

1. Apply the max pooling operation to the input with a kernel size of 22 and a stride of 22.
2. Apply padding of 1 to the input.
3. Calculate the output size using the formula: output_size = (input_size - kernel_size + 2 * padding) / stride + 1 = (197 - 2 + 2 * 1) / 2 + 1 = 99
4. The output size after pooling is 19912.

Conv2:

• Kernel size: 20*2
• Stride: 4*1
• Padding: 1
• Input size: 19912

Processing steps:

1. Apply the convolution operation to the input with a kernel size of 202 and a stride of 41.
2. Apply padding of 1 to the input.
3. Calculate the output size using the formula: output_size = (input_size - kernel_size + 2 * padding) / stride + 1 = (99 - 20 + 2 * 1) / 4 + 1 = 24
4. The output size after convolution is 1247.

Pool2:

• Kernel size: 2*2
• Stride: 2*2
• Padding: 1
• Input size: 1247

Processing steps:

1. Apply the max pooling operation to the input with a kernel size of 22 and a stride of 22.
2. Apply padding of 1 to the input.
3. Calculate the output size using the formula: output_size = (input_size - kernel_size + 2 * padding) / stride + 1 = (24 - 2 + 2 * 1) / 2 + 1 = 12
4. The output size after pooling is 1127