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Pytorch: can we use nn.Module layers directly in forward() function?

By : Filip
Date : September 16 2020, 12:00 PM
I wish this helpful for you You need to think of the scope of the trainable parameters.
If you define, say, a conv layer in the forward function of your model, then the scope of this "layer" and its trainable parameters is local to the function and will be discarded after every call to the forward method. You cannot update and train weights that are constantly being discarded after every forward pass.
code :

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How to convert pretrained FC layers to CONV layers in Pytorch

By : Rizwan Siddiquei
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I want to convert a pre-trained CNN (like VGG-16) to a fully convolutional network in Pytorch. How can I do so? , You can do that as follows (see comments for description):
code :
import torch
import torch.nn as nn
from torchvision import models

model = models.vgg16(pretrained=True)

features = model.features

fcLayers = nn.Sequential(
    # stop at last layer


### convert first fc layer to conv layer with 512x7x7 kernel
fc = fcLayers[0].state_dict()
in_ch = 512
out_ch = fc["weight"].size(0)

firstConv = nn.Conv2d(in_ch, out_ch, 7, 7)

### get the weights from the fc layer
firstConv.load_state_dict({"weight":fc["weight"].view(out_ch, in_ch, 7, 7),

convList = [firstConv]

# Similarly convert the remaining linear layers to conv layers 
for layer in enumerate(fcLayers[1:]):
    if isinstance(module, nn.Linear):
        # Convert the nn.Linear to nn.Conv
        fc = module.state_dict()
        in_ch = fc["weight"].size(1)
        out_ch = fc["weight"].size(0)
        conv = nn.Conv2d(in_ch, out_ch, 1, 1)

        conv.load_state_dict({"weight":fc["weight"].view(out_ch, in_ch, 1, 1),

        convList += [conv]
        # Append other layers such as ReLU and Dropout
        convList += [layer]

# Set the conv layers as a nn.Sequential module
convLayers = nn.Sequential(*convList)  

Pytorch What's the difference between define layer in __init__() and directly use in forward()?

By : Sally
Date : March 29 2020, 07:55 AM
wish helps you Everything which contains weights which you want to be trained during the training process should be defined in your __init__ method.
You don't need do define activation functions like softmax, ReLU or sigmoid in your __init__, you can just call them in forward.

too many arguments in a Pytorch custom nn module forward function

By : Tim Holmes
Date : March 29 2020, 07:55 AM
wish help you to fix your issue In the definition of model I forgot the parentheses on the torch.nn.Tanh class. It should be torch.nn.Tanh()
I keep thinking that these are functions and not classes. I still have some things to fix, but I'm glad I saw that. So frustrating. I found it by basically putting assert and print statements all over my code.

pytorch : unable to understand model.forward function

By : user2306253
Date : March 29 2020, 07:55 AM
Hope this helps I am learning deep learning and am trying to understand the pytorch code given below. I'm struggling to understand how the probability calculation works. Can somehow break it down in lay-man terms. a ton. , I'm a layman so I'll help you with the layman's terms :)
code :
input_size = 784
hidden_sizes = [128, 64]
output_size = 10
model = nn.Sequential(nn.Linear(input_size, hidden_sizes[0]),
nn.Linear(hidden_sizes[0], hidden_sizes[1]),
nn.Linear(hidden_sizes[1], output_size)
# Forward pass through the network and display output
images, labels = next(iter(trainloader))
images.resize_(images.shape[0], 1, 784)
ps = model.forward(images[0,:])

PyTorch why does the forward function run multiple times and can I change the input shape?

By : omar
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further You are calling forward twice in run:
Once for the training data Once for the validation data
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