What is the difference between torch.tensor() and torch.Tensor() (capital T) for creating tensors?

This is a subtle but important PyTorch gotcha. torch.tensor() (lowercase, a function) infers dtype from the input data and copies it — the recommended way to create tensors from data. torch.Tensor() (uppercase, a class constructor) is an alias for torch.FloatTensor and behaves inconsistently depending on the argument type.

import torch

# ── torch.tensor() — RECOMMENDED, infers dtype, copies data
a = torch.tensor([1, 2, 3])
print(a.dtype)   # torch.int64 — inferred from Python ints

b = torch.tensor([1.0, 2.0, 3.0])
print(b.dtype)   # torch.float32 — inferred from Python floats

c = torch.tensor([1, 2, 3], dtype=torch.float32)  # explicit override
print(c.dtype)   # torch.float32

# ── torch.Tensor() — confusing, AVOID for creating tensors from data
d = torch.Tensor([1, 2, 3])
print(d.dtype)   # torch.float32 — ALWAYS float32, ignores int input!

e = torch.Tensor(3, 4)   # interprets ints as a SHAPE, not data!
print(e.shape)    # torch.Size([3, 4]) — uninitialised memory, random values

# Common gotcha: these look similar but behave VERY differently
f1 = torch.tensor(3)      # scalar tensor with value 3
f2 = torch.Tensor(3)      # tensor of SHAPE (3,) with garbage/uninitialised values!
print(f1)   # tensor(3)
print(f2)   # tensor([4.6e-41, 0.0, 1.4e-45])  — random uninitialised memory!

# Recommended explicit constructors for empty/typed tensors:
g = torch.empty(3, 4)               # uninitialised, explicit intent
h = torch.zeros(3, 4, dtype=torch.float32)
i = torch.ones(3, 4, dtype=torch.int64)

Rule of thumb: always use lowercase torch.tensor() when creating a tensor from existing data (a list, NumPy array, or scalar). Use torch.zeros(), torch.ones(), torch.empty(), or torch.rand() when you want a new tensor of a given shape. Avoid torch.Tensor() entirely in new code.

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