00. PyTorch Fundamentals Exercises¶

1. Documentation reading¶

A big part of deep learning (and learning to code in general) is getting familiar with the documentation of a certain framework you're using. We'll be using the PyTorch documentation a lot throughout the rest of this course. So I'd recommend spending 10-minutes reading the following (it's okay if you don't get some things for now, the focus is not yet full understanding, it's awareness):

• The documentation on torch.Tensor.
• The documentation on torch.cuda.

In [1]:

# No code solution (reading)

# No code solution (reading)

2. Create a random tensor with shape (7, 7).¶

In [2]:

# Import torch


# Create random tensor

# Import torch # Create random tensor

3. Perform a matrix multiplication on the tensor from 2 with another random tensor with shape (1, 7) (hint: you may have to transpose the second tensor).¶

In [3]:

# Create another random tensor

# Perform matrix multiplication

# Create another random tensor # Perform matrix multiplication

4. Set the random seed to 0 and do 2 & 3 over again.¶

The output should be:

(tensor([[1.8542],
         [1.9611],
         [2.2884],
         [3.0481],
         [1.7067],
         [2.5290],
         [1.7989]]), torch.Size([7, 1]))

In [4]:

# Set manual seed


# Create two random tensors


# Matrix multiply tensors

# Set manual seed # Create two random tensors # Matrix multiply tensors

5. Speaking of random seeds, we saw how to set it with torch.manual_seed() but is there a GPU equivalent? (hint: you'll need to look into the documentation for torch.cuda for this one)¶

• If there is, set the GPU random seed to 1234.

In [5]:

# Set random seed on the GPU

# Set random seed on the GPU

6. Create two random tensors of shape (2, 3) and send them both to the GPU (you'll need access to a GPU for this). Set torch.manual_seed(1234) when creating the tensors (this doesn't have to be the GPU random seed). The output should be something like:¶

Device: cuda
(tensor([[0.0290, 0.4019, 0.2598],
         [0.3666, 0.0583, 0.7006]], device='cuda:0'),
 tensor([[0.0518, 0.4681, 0.6738],
         [0.3315, 0.7837, 0.5631]], device='cuda:0'))

In [6]:

# Set random seed


# Check for access to GPU

# Create two random tensors on GPU

# Set random seed # Check for access to GPU # Create two random tensors on GPU

7. Perform a matrix multiplication on the tensors you created in 6 (again, you may have to adjust the shapes of one of the tensors).¶

The output should look like:

(tensor([[0.3647, 0.4709],
         [0.5184, 0.5617]], device='cuda:0'), torch.Size([2, 2]))

In [7]:

# Perform matmul on tensor_A and tensor_B

# Perform matmul on tensor_A and tensor_B

8. Find the maximum and minimum values of the output of 7.¶

In [8]:

# Find max

# Find min

# Find max # Find min

9. Find the maximum and minimum index values of the output of 7.¶

In [9]:

# Find arg max


# Find arg min

# Find arg max # Find arg min

10. Make a random tensor with shape (1, 1, 1, 10) and then create a new tensor with all the 1 dimensions removed to be left with a tensor of shape (10). Set the seed to 7 when you create it and print out the first tensor and it's shape as well as the second tensor and it's shape.¶

The output should look like:

tensor([[[[0.5349, 0.1988, 0.6592, 0.6569, 0.2328, 0.4251, 0.2071, 0.6297,
           0.3653, 0.8513]]]]) torch.Size([1, 1, 1, 10])
tensor([0.5349, 0.1988, 0.6592, 0.6569, 0.2328, 0.4251, 0.2071, 0.6297, 0.3653,
        0.8513]) torch.Size([10])

In [10]:

# Set seed


# Create random tensor


# Remove single dimensions


# Print out tensors and their shapes

# Set seed # Create random tensor # Remove single dimensions # Print out tensors and their shapes