Cookbook — Tensors¶
1. Create a tensor from a list, numpy array, or scalar¶
Intent: Bootstrap input data.
import numpy as np
from SneppX_ALG import Tensor
t1 = Tensor([1.0, 2.0, 3.0]) # from list
t2 = Tensor(np.random.randn(4, 8)) # from numpy
t3 = Tensor(7.0, shape=(2, 2)) # scalar broadcast to a shape
t4 = Tensor.zeros((4, 8)) # zeros
t5 = Tensor.ones((4, 8)) # ones
t6 = Tensor.randn((4, 8)) # N(0,1)
t7 = Tensor.eye(4) # identity
Notes: Tensor auto-detects the C backend; the NumPy fallback is always
active. Pass dtype="float32" (default) or "int64".
2. Convert to / from numpy¶
Intent: Interop with the rest of the Python ecosystem.
from SneppX_ALG import Tensor
import numpy as np
arr = np.random.randn(3, 3).astype(np.float32)
t = Tensor.from_numpy(arr)
back = t.numpy() # np.ndarray
assert np.allclose(back, arr)
Notes: .numpy() returns a copy; mutating it does not mutate the
Tensor. CPU-safe.
3. Reshape / transpose / view¶
Intent: Change tensor layout without copying (where possible).
from SneppX_ALG import Tensor
t = Tensor.randn((2, 3, 4))
t.reshape((6, 4)) # alias of view
t.view(-1, 4) # last-dim preserved
t.permute(1, 0, 2) # -> (3, 2, 4)
t.transpose(0, 1) # swap axes 0 and 1
t.T # 2-D shortcut for transpose(0,1)
t.squeeze(0) # drop size-1 dim
t.unsqueeze(0) # insert dim at front
Notes: Requires C backend for autograd-tracking view; pure-NumPy path
works for the math. CPU-safe for inference.
4. Element-wise math and activations¶
Intent: Apply pointwise ops.
from SneppX_ALG import Tensor
t = Tensor.randn((4, 4))
_ = t + t # __add__ (also -, *, /, __matmul__ for @)
_ = t.relu()
_ = t.gelu()
_ = t.silu()
_ = t.sigmoid()
_ = t.tanh()
_ = t.exp()
_ = t.log()
_ = t.sqrt()
_ = -t # __neg__
_ = t ** 2 # __pow__
Notes: All activations route through autograd_ops (CPU NumPy fallback
when no C backend). GPU: uses CUDA kernels if _HAS_CUDA (needs build with
SNEPPX_BUILD_CUDA=ON).
5. Reduction ops¶
Intent: Collapse dimensions.
from SneppX_ALG import Tensor
t = Tensor.randn((4, 8))
m = t.mean() # scalar Tensor
s = t.sum(dim=1) # -> (4,)
v = t.var(dim=1) # variance
st = t.std(dim=1) # std
mn = t.min() # python float
mx = t.max() # python float
Notes: mean/sum are differentiable; min/max (scalar) are not.
6. Matrix ops and concatenation¶
Intent: Combine and project tensors.
from SneppX_ALG import Tensor
a = Tensor.randn((3, 4))
b = Tensor.randn((4, 5))
c = a @ b # matmul -> (3, 5)
xs = [Tensor.randn((2, 4)) for _ in range(3)]
cat = Tensor.cat(xs, dim=0) # -> (6, 4)
stk = Tensor.stack(xs, dim=0) # -> (3, 2, 4)
Notes: Tensor.cat/stack are static; also available as module-level
cat/stack.
7. Convolution and pooling¶
Intent: CNN-style ops on CPU.
from SneppX_ALG import Tensor
x = Tensor.randn((1, 3, 32, 32)) # NCHW
k = Tensor.randn((16, 3, 3, 3)) # out_ch, in_ch, kh, kw
y = x.conv2d(k, stride_h=1, stride_w=1, pad_h=1, pad_w=1) # -> (1,16,32,32)
p = x.pool2d(kernel_h=2, kernel_w=2) # 2x2 avg pool
Notes: conv2d uses scipy.signal on the NumPy fallback; CUDA path uses
conv2d_kernel when available. CPU-safe.
8. Normalization primitives¶
Intent: Layer / group / batch normalization as tensor methods.
from SneppX_ALG import Tensor
x = Tensor.randn((2, 4, 8))
gamma = Tensor.ones((8,))
beta = Tensor.zeros((8,))
ln = x.layer_norm(gamma, beta, eps=1e-5)
gn = x.group_norm(gamma, beta, num_groups=2, eps=1e-5)
bn = x.batch_norm(gamma, beta, rm, rv, eps=1e-5)
Notes: Pass Tensor or raw np.ndarray for statistics.
9. Save / load a tensor¶
Intent: Persist a single tensor to disk.
from SneppX_ALG import Tensor
t = Tensor.randn((4, 8))
t.save("/tmp/t.npy") # .npy
loaded = Tensor.load("/tmp/t.npy")
Notes: Uses np.save/np.load. For full model state use
model.save_checkpoint (see Checkpointing).
10. Detach and requires_grad¶
Intent: Break the graph for inference; mark params.
from SneppX_ALG import Tensor
x = Tensor.randn((4, 4), requires_grad=True)
y = x @ x # y.grad_fn set
leaf = y.detach() # no grad tracking
x.requires_grad_(True)
Notes: Requires C backend for a functional graph; otherwise backward()
raises. CPU-safe to construct the graph.