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Update pages/3_WithExercises.py
Browse files- pages/3_WithExercises.py +12 -12
pages/3_WithExercises.py
CHANGED
@@ -81,7 +81,7 @@ print("Third column:", column)
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# Modifying elements
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# Changing the first element of the tensor to 10
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tensor[0, 0] = 10
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print("Modified tensor
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'''
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},
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"Exercise 3: Tensor Reshaping and Transposing": {
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@@ -94,16 +94,16 @@ tensor = torch.tensor([[1, 2, 3], [4, 5, 6]])
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# Reshaping the tensor
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# Changing the shape of the tensor to (3, 2)
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reshaped_tensor = tensor.view(3, 2)
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print("Reshaped tensor
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# Another way to reshape using reshape()
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reshaped_tensor2 = tensor.reshape(-1) # Flattening the tensor
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print("Reshaped tensor (flattened)
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# Transposing the tensor
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# Swapping the dimensions of the tensor (transpose rows and columns)
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transposed_tensor = tensor.t()
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print("Transposed tensor
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# Using permute for higher-dimensional tensors
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tensor_3d = torch.randn(2, 3, 4) # Creating a random 3D tensor
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@@ -129,7 +129,7 @@ print("Matrix multiplication result:\n", matmul_result)
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tensor3 = torch.tensor([1, 2, 3])
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tensor4 = torch.tensor([[1], [2], [3]])
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broadcast_result = tensor3 + tensor4
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print("Broadcasting result
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# Element-wise functions
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# Applying sine function element-wise
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@@ -152,32 +152,32 @@ print("Square root of tensor3:", sqrt_result)
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# Creating tensors filled with zeros and ones
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zeros_tensor = torch.zeros(3, 3)
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print("Zeros tensor
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ones_tensor = torch.ones(3, 3)
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print("Ones tensor
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# Randomly initialized tensors
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# Uniform distribution in the range [0, 1)
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rand_tensor = torch.rand(3, 3)
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print("Uniform random tensor
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# Normal distribution with mean 0 and variance 1
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randn_tensor = torch.randn(3, 3)
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print("Normal random tensor
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# Random integers in the range [0, 10)
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randint_tensor = torch.randint(0, 10, (3, 3))
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print("Random integer tensor
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# Initializing tensors with specific distributions
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# Normal distribution with custom mean and standard deviation
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normal_tensor = torch.normal(mean=0, std=1, size=(3, 3))
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print("Normal distribution tensor
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# Uniform distribution in a custom range [0, 1)
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uniform_tensor = torch.empty(3, 3).uniform_(0, 1)
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print("Uniform distribution tensor
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'''
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},
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"Exercise 6: Tensor Arithmetic Operations": {
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# Modifying elements
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# Changing the first element of the tensor to 10
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tensor[0, 0] = 10
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print("Modified tensor:", tensor)
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'''
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},
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"Exercise 3: Tensor Reshaping and Transposing": {
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# Reshaping the tensor
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# Changing the shape of the tensor to (3, 2)
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reshaped_tensor = tensor.view(3, 2)
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print("Reshaped tensor:", reshaped_tensor)
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# Another way to reshape using reshape()
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reshaped_tensor2 = tensor.reshape(-1) # Flattening the tensor
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print("Reshaped tensor (flattened):", reshaped_tensor2)
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# Transposing the tensor
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# Swapping the dimensions of the tensor (transpose rows and columns)
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transposed_tensor = tensor.t()
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print("Transposed tensor:", transposed_tensor)
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# Using permute for higher-dimensional tensors
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tensor_3d = torch.randn(2, 3, 4) # Creating a random 3D tensor
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tensor3 = torch.tensor([1, 2, 3])
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tensor4 = torch.tensor([[1], [2], [3]])
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broadcast_result = tensor3 + tensor4
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print("Broadcasting result:", broadcast_result)
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# Element-wise functions
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# Applying sine function element-wise
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# Creating tensors filled with zeros and ones
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zeros_tensor = torch.zeros(3, 3)
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print("Zeros tensor:", zeros_tensor)
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ones_tensor = torch.ones(3, 3)
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print("Ones tensor:", ones_tensor)
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# Randomly initialized tensors
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# Uniform distribution in the range [0, 1)
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rand_tensor = torch.rand(3, 3)
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print("Uniform random tensor:", rand_tensor)
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# Normal distribution with mean 0 and variance 1
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randn_tensor = torch.randn(3, 3)
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print("Normal random tensor:", randn_tensor)
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# Random integers in the range [0, 10)
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randint_tensor = torch.randint(0, 10, (3, 3))
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print("Random integer tensor:", randint_tensor)
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# Initializing tensors with specific distributions
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# Normal distribution with custom mean and standard deviation
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normal_tensor = torch.normal(mean=0, std=1, size=(3, 3))
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print("Normal distribution tensor:", normal_tensor)
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# Uniform distribution in a custom range [0, 1)
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uniform_tensor = torch.empty(3, 3).uniform_(0, 1)
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print("Uniform distribution tensor:", uniform_tensor)
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'''
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},
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"Exercise 6: Tensor Arithmetic Operations": {
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