Forward pass computation in PyTorch - ML Experiment: Train & Evaluate

Experiment - Forward pass computation

Problem:You have a simple neural network model in PyTorch that classifies images into 10 classes. The model is defined, but you want to understand how the forward pass works and verify that the output shape and values are correct.

Current Metrics:No training done yet. The model outputs raw scores (logits) for each class. Output shape is (batch_size, 10).

Issue:You are unsure if the forward pass is implemented correctly and if the output tensor shape and values make sense.

Your Task

Run a forward pass on a batch of dummy input data and verify the output shape is (batch_size, 10). Also, check the output values to confirm they are raw scores (not probabilities).

Use PyTorch only.

Do not train the model; just run a forward pass.

Use a batch size of 4 and input size matching the model input.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

PyTorch

import torch
import torch.nn as nn

# Define a simple neural network
class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.flatten = nn.Flatten()
        self.linear1 = nn.Linear(28*28, 128)
        self.relu = nn.ReLU()
        self.linear2 = nn.Linear(128, 10)  # 10 classes

    def forward(self, x):
        x = self.flatten(x)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)  # Output logits
        return x

# Instantiate the model
model = SimpleNet()

# Create dummy input: batch_size=4, channels=1, height=28, width=28
dummy_input = torch.randn(4, 1, 28, 28)

# Forward pass
output = model(dummy_input)

# Print output shape and values
print(f"Output shape: {output.shape}")
print(f"Output values (first sample): {output[0]}")

Defined a simple neural network with flatten, linear, and ReLU layers.

Created dummy input tensor with batch size 4 and correct input shape.

Ran forward pass using model(dummy_input).

Printed output shape and first sample output values to verify.

Results Interpretation

Before: No forward pass run, unsure about output shape and values.

After: Output shape confirmed as (4, 10), matching batch size and number of classes. Output values are raw scores (logits), not probabilities.

The forward pass transforms input data through the network layers to produce raw scores for each class. Checking output shape and values helps confirm the model is implemented correctly before training.

Bonus Experiment

Apply a softmax function to the output logits to convert them into probabilities and verify the probabilities sum to 1 for each sample.

💡 Hint

Use torch.nn.functional.softmax on the output tensor along the class dimension (dim=1). Then sum the probabilities for each sample to check they equal 1.

Practice

1. What does the forward pass in a PyTorch model do?

easy

A. It moves input data through the model to get predictions.

B. It updates the model weights during training.

C. It saves the model to disk.

D. It initializes the model parameters.

Solution

1. Step 1: Understand the role of forward pass

   The forward pass takes input data and processes it through the model layers to produce output predictions.

2. Step 2: Differentiate from other processes

   Updating weights happens in backward pass, saving is separate, and initialization is before training.

3. Final Answer:

   It moves input data through the model to get predictions. -> Option A

4. Quick Check:

   Forward pass = data to predictions [OK]

Hint: Forward pass = input to output predictions [OK]

Common Mistakes:

• Confusing forward pass with weight update
• Thinking forward pass saves the model
• Mixing initialization with forward pass

2. Which of the following is the correct way to run a forward pass on a PyTorch model named model with input tensor x?

easy

A. output = model.forward(x)

B. output = model.predict(x)

C. output = model.run(x)

D. output = model(x)

Solution

1. Step 1: Recall PyTorch model call syntax

   In PyTorch, calling model(x) runs the forward pass internally.

2. Step 2: Identify incorrect options

   Directly calling model.forward(x) is discouraged; model.run and model.predict are not standard methods.

3. Final Answer:

   output = model(x) -> Option D

4. Quick Check:

   Use model(x) to run forward pass [OK]

Hint: Use model(input) to run forward pass [OK]

Common Mistakes:

• Calling model.forward(x) directly
• Using non-existent methods like model.run or model.predict
• Confusing syntax with other frameworks

3. Given the following PyTorch code, what is the output tensor shape?

import torch
import torch.nn as nn

class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(4, 2)
    def forward(self, x):
        return self.linear(x)

model = SimpleNet()
x = torch.randn(3, 4)
output = model(x)
print(output.shape)

medium

A. torch.Size([3, 2])

B. torch.Size([2, 3])

C. torch.Size([4, 2])

D. torch.Size([3, 4])

Solution

1. Step 1: Analyze input and layer dimensions

   The input tensor has shape (3, 4), meaning batch size 3 and feature size 4. The linear layer maps 4 features to 2 outputs.

2. Step 2: Compute output shape

   Applying linear layer to each batch item changes feature size from 4 to 2, so output shape is (3, 2).

3. Final Answer:

   torch.Size([3, 2]) -> Option A

4. Quick Check:

   Input batch 3, features 4 -> output features 2 [OK]

Hint: Output shape = (batch, output features) [OK]

Common Mistakes:

• Mixing batch and feature dimensions
• Assuming output shape equals input shape
• Confusing input and output feature sizes

4. Identify the error in this PyTorch forward pass code snippet:

import torch
import torch.nn as nn

class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc = nn.Linear(5, 3)
    def forward(self, x):
        return self.fc(x)

model = Net()
x = torch.randn(4, 4)
output = model(x)
print(output)

medium

A. Missing activation function after Linear layer.

B. Model is not moved to GPU before forward pass.

C. Input tensor shape does not match Linear layer input size.

D. Forward method is missing return statement.

Solution

1. Step 1: Check input tensor shape vs Linear layer input

   The Linear layer expects input features of size 5, but input tensor has shape (4, 4), so feature size is 4, which mismatches.

2. Step 2: Confirm other options

   GPU usage is optional, activation functions are not mandatory, and forward method correctly returns output.

3. Final Answer:

   Input tensor shape does not match Linear layer input size. -> Option C

4. Quick Check:

   Input features must match Linear input size [OK]

Hint: Check input feature size matches Linear layer input [OK]

Common Mistakes:

• Ignoring input size mismatch errors
• Assuming GPU is required for forward pass
• Thinking activation is always needed

5. You have a PyTorch model with two linear layers chained in the forward pass:

class ChainNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(3, 4)
        self.fc2 = nn.Linear(4, 2)
    def forward(self, x):
        x = self.fc1(x)
        x = torch.relu(x)
        x = self.fc2(x)
        return x

model = ChainNet()
x = torch.tensor([[1.0, 2.0, 3.0]])
output = model(x)
print(output)

What is the shape of output and why?

hard

A. torch.Size([1, 4]) because the first layer outputs 4 features.

B. torch.Size([1, 2]) because input batch size is 1 and final layer outputs 2 features.

C. torch.Size([3, 2]) because input has 3 features and output has 2 features.

D. torch.Size([4, 2]) because the two layers output sizes multiply.

Solution

1. Step 1: Analyze input and layer sizes

   Input tensor shape is (1, 3) -- batch size 1, feature size 3. First layer maps 3 to 4 features, second layer maps 4 to 2 features.

2. Step 2: Determine output shape after forward pass

   After passing through both layers, output shape is (1, 2) because batch size stays the same and final layer outputs 2 features.

3. Final Answer:

   torch.Size([1, 2]) because input batch size is 1 and final layer outputs 2 features. -> Option B

4. Quick Check:

   Batch size unchanged, output features = final layer output [OK]

Hint: Output shape = (batch size, last layer output features) [OK]

Common Mistakes:

• Multiplying output sizes instead of chaining
• Confusing batch size with feature size
• Ignoring activation does not change shape