nn.LSTM layer in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an LSTM layer with input size 10 and hidden size 20.

PyTorch

lstm = nn.LSTM(input_size=[1], hidden_size=20)

Drag options to blanks, or click blank then click option'

A5

B10

C20

D15

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing input_size with hidden_size.

Using a value that does not match the input data features.

The input_size parameter defines the number of expected features in the input. Here, it should be 10.

2fill in blank

medium

Complete the code to initialize hidden and cell states for an LSTM with batch size 3 and hidden size 5.

PyTorch

h0 = torch.zeros(1, [1], 5)
c0 = torch.zeros(1, [1], 5)

Drag options to blanks, or click blank then click option'

A10

B1

C5

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Using hidden size instead of batch size for the second dimension.

Confusing the order of dimensions.

The second dimension is the batch size, which should be 3 here.

3fill in blank

hard

Fix the error in the code to run input through the LSTM layer.

PyTorch

output, (hn, cn) = lstm([1])

Drag options to blanks, or click blank then click option'

Ainput_tensor

Bx

Cinputs

Dinput

Attempts:

3 left

💡 Hint

Common Mistakes

Using undefined variable names.

Passing the wrong variable to the LSTM.

The variable input_tensor is the correct input tensor to pass to the LSTM.

4fill in blank

hard

Fill both blanks to create an LSTM with 2 layers and batch_first enabled.

PyTorch

lstm = nn.LSTM(input_size=10, hidden_size=20, num_layers=[1], batch_first=[2])

Drag options to blanks, or click blank then click option'

A2

BTrue

CFalse

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Setting num_layers to 1 instead of 2.

Forgetting to set batch_first to True when input shape is batch first.

num_layers=2 creates two stacked LSTM layers. batch_first=True makes input shape (batch, seq, feature).

5fill in blank

hard

Fill all three blanks to extract the last hidden state from the LSTM output.

PyTorch

last_hidden = hn[[1], [2], [3]]

Drag options to blanks, or click blank then click option'

A-1

B0

C1

D2

Attempts:

3 left

💡 Hint

Common Mistakes

Using positive indices for the last layer instead of -1.

Mixing up batch and hidden unit indices.

hn[-1, 0, 1] accesses the last layer's hidden state for the first batch element and second hidden unit.

Practice

1. What is the primary purpose of the nn.LSTM layer in PyTorch?

easy

A. To process and remember information from sequences over time

B. To perform image classification using convolution

C. To reduce the dimensionality of data using PCA

D. To generate random numbers for initialization

Solution

1. Step 1: Understand the role of LSTM

   LSTM stands for Long Short-Term Memory, a type of recurrent neural network layer designed to handle sequence data and remember information over time.

2. Step 2: Match purpose with options

   Among the options, only processing and remembering sequence information matches the LSTM's purpose.

3. Final Answer:

   To process and remember information from sequences over time -> Option A

4. Quick Check:

   LSTM purpose = sequence memory [OK]

Hint: LSTM = sequence memory layer, not image or random [OK]

Common Mistakes:

• Confusing LSTM with convolutional layers
• Thinking LSTM reduces data dimension like PCA
• Assuming LSTM generates random numbers

2. Which of the following is the correct way to create an LSTM layer in PyTorch with input size 10 and hidden size 20?

easy

A. nn.LSTM(input=10, hidden=20)

B. nn.LSTM(20, 10)

C. nn.LSTM(10, 20)

D. nn.LSTM(hidden_size=10, input_size=20)

Solution

1. Step 1: Recall nn.LSTM constructor parameters

   The first argument is input_size (features per input), the second is hidden_size (features in hidden state).

2. Step 2: Match correct syntax

   nn.LSTM(10, 20) uses nn.LSTM(10, 20) which correctly sets input_size=10 and hidden_size=20.

3. Final Answer:

   nn.LSTM(10, 20) -> Option C

4. Quick Check:

   Constructor order = input_size, hidden_size [OK]

Hint: First arg input size, second hidden size in nn.LSTM() [OK]

Common Mistakes:

• Swapping input_size and hidden_size
• Using wrong keyword arguments
• Confusing parameter names

3. Given the code below, what is the shape of output after running the LSTM?

import torch
import torch.nn as nn
lstm = nn.LSTM(input_size=5, hidden_size=3, num_layers=1)
inputs = torch.randn(4, 2, 5)  # seq_len=4, batch=2, input_size=5
output, (hn, cn) = lstm(inputs)

medium

A. (4, 2, 3)

B. (2, 4, 3)

C. (4, 3, 2)

D. (2, 3, 4)

Solution

1. Step 1: Understand LSTM input and output shapes

   The input shape is (seq_len, batch, input_size). The output shape is (seq_len, batch, hidden_size).

2. Step 2: Apply given dimensions

   Input shape is (4, 2, 5), hidden_size=3, so output shape is (4, 2, 3).

3. Final Answer:

   (4, 2, 3) -> Option A

4. Quick Check:

   Output shape = (seq_len, batch, hidden_size) [OK]

Hint: Output shape matches (seq_len, batch, hidden_size) [OK]

Common Mistakes:

• Mixing batch and sequence dimensions
• Confusing input_size with hidden_size
• Assuming output shape swaps batch and seq_len

4. What is wrong with this code snippet that tries to create an LSTM layer?

import torch.nn as nn
lstm = nn.LSTM(10)

medium

A. The input size must be a tuple, not an integer

B. It misses the hidden_size argument, causing an error

C. LSTM requires a batch size argument at creation

D. The code is correct and runs without error

Solution

1. Step 1: Check nn.LSTM constructor requirements

   nn.LSTM requires at least two positional arguments: input_size and hidden_size.

2. Step 2: Identify missing argument

   The code only provides input_size=10, missing hidden_size, so it will raise a TypeError.

3. Final Answer:

   It misses the hidden_size argument, causing an error -> Option B

4. Quick Check:

   nn.LSTM needs input_size and hidden_size [OK]

Hint: nn.LSTM needs two sizes: input and hidden [OK]

Common Mistakes:

• Thinking batch size is needed at layer creation
• Assuming input_size can be a tuple
• Believing code runs without error

5. You want to build a model that processes sequences of length 6 with 8 features each. You want the LSTM to output a sequence with 12 features per time step. Which of the following LSTM layer initializations is correct to achieve this?

hard

A. nn.LSTM(input_size=12, hidden_size=8)

B. nn.LSTM(input_size=8, hidden_size=6)

C. nn.LSTM(input_size=6, hidden_size=8)

D. nn.LSTM(input_size=8, hidden_size=12)

Solution

1. Step 1: Identify input_size and hidden_size meanings

   input_size is the number of features per time step in the input sequence. hidden_size is the number of features in the output per time step.

2. Step 2: Match given sequence and desired output

   Input sequences have 8 features, so input_size=8. Desired output features per time step is 12, so hidden_size=12.

3. Final Answer:

   nn.LSTM(input_size=8, hidden_size=12) -> Option D

4. Quick Check:

   Input features = 8, output features = 12 [OK]

Hint: Input size = input features, hidden size = output features [OK]

Common Mistakes:

• Confusing sequence length with input_size
• Swapping input_size and hidden_size
• Using sequence length as hidden_size