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Update tensor_tutorial.py #2911

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920f148
Update tensor_tutorial.py
akhil-maker Jun 7, 2024
6e9fbe7
Update tensor_tutorial.py
akhil-maker Jun 9, 2024
3400007
Update tensor_tutorial.py
akhil-maker Jun 9, 2024
61ff89f
Update tensor_tutorial.py
akhil-maker Jun 10, 2024
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38 changes: 38 additions & 0 deletions beginner_source/blitz/tensor_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,6 +30,7 @@
data = [[1, 2], [3, 4]]
x_data = torch.tensor(data)


######################################################################
# **From a NumPy array**
#
Expand All @@ -51,6 +52,16 @@


######################################################################
# **Tensor as Diagonal Matrix
#
# Like NumPy operations, the eye function creates a diagonal matrix of which the diagonal elements have ones and
# off diagonal elements have zeros

x_eye = torch.eye(3, 4)
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Why the eye operator is interesting here? What does it adds to the tutorial?

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The eye operator is an interesting operator for tensors that shows there are operations defined in tensors also just like numpy with which one can create a diagonal matrix easily with a one line code.

print(f"Diagonal Tensor: \n {x_eye} \n")

######################################################################
#
# **With random or constant values:**
#
# ``shape`` is a tuple of tensor dimensions. In the functions below, it determines the dimensionality of the output tensor.
Expand Down Expand Up @@ -94,6 +105,33 @@
# Tensor Operations
# ~~~~~~~~~~~~~~~~~
#
# **Check for a tensor**
#
# We can check whether an object in python is a tensor or not. Typically, is_tensor function checks
# and is_storage function checkes whether an object is a pytorch storage object.
x = [1, 2, 3, 4, 5]

print(f"x is tensor: {torch.is_tensor(x)}\n")
print(f"x is pytorch storage object: {torch.is_storage(x)}\n")

y = torch.randn(1, 2, 3, 4, 5)
print(f"y is tensor: {torch.is_tensor(y)}\n")
print(f"y is pytorch storage object: {torch.is_storage(y)}\n")

######################################################################
# Here x is not a tensor
# while y is a tensor but it is not stored.
#
# **Count Elements in a Tensor**
#
# To check the total number of elements in an input tensor object, the numerical element function can be used.
print(f"Total number of elements in tensor y: {torch.numel(y)}\n")
#
data = [[1, 2], [3, 4]]
x_data = torch.tensor(data)
print(f"Total number of elements in 2D tensor: {torch.numel(x_data)}\n")
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Why is x_data suddenly here?

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Actually, I thought it would be good if we can reuse the previously defined data again but I think we should define x_data again so as to make it easy for the viewer.
Relevant code is changed

#
#
# Over 100 tensor operations, including transposing, indexing, slicing,
# mathematical operations, linear algebra, random sampling, and more are
# comprehensively described
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