# 如何将张量流模型转换为pytorch模型？

I'm new to pytorch. Here's an architecture of a tensorflow model and I'd like to convert it into a pytorch model.

1）在tensorflow中，Conv2D函数将过滤器作为输入。但是，在pytorch中，该功能将输入通道和输出通道的大小作为输入。因此，如何找到与过滤器大小一起提供的等效数量的输入通道和输出通道。

2）在张量流中，致密层具有一个称为“节点”的参数。但是，在pytorch中，同一层具有2个不同的输入（输入参数的大小和目标参数的大小），如何根据节点数确定它们。

``````from keras.utils import to_categorical
from keras.layers import Conv2D, MaxPool2D, Dense, Flatten, Dropout

model = Sequential()
``````

``````import torch.nn.functional as F
import torch

# The network should inherit from the nn.Module
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# Define 2D convolution layers
# 3: input channels, 32: output channels, 5: kernel size, 1: stride
self.conv1 = nn.Conv2d(3, 32, 5, 1)   # The size of input channel is 3 because all images are coloured
self.conv2 = nn.Conv2d(32, 64, 5, 1)
self.conv3 = nn.Conv2d(64, 128, 3, 1)
self.conv3 = nn.Conv2d(128, 256, 3, 1)
# It will 'filter' out some of the input by the probability(assign zero)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
# Fully connected layer: input size, output size
self.fc1 = nn.Linear(36864, 128)
self.fc2 = nn.Linear(128, 10)

# forward() link all layers together,
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = self.conv3(x)
x = F.relu(x)
x = self.conv4(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
``````