import torch #similar to numpy
import torch.autograd #differntiation lib
import torch.nn as nn# neural networks lib with autograd integration
import torch.nn.functional as F #functional implementation of nn(lower level, not trainable)
import torch.optim as optim # standard op methods like gradient descent
torch.manual_seed(123)
x_1 = torch.randn(2, 5)
y_1 = torch.randn(3, 5)
z_1 = torch.cat((x_1, y_1)) #0 is along first listed axis, 1 2nd etc
Reshape
x = torch.randn(3200, 3, 28, 28) #3200 photos of 28*28 RGB
# We want a batch, infer second dimesion with -1
x_reshaped = x.view(32, -1, 3, 28, 28)
print(x_reshaped.shape)
Numpy
They share their underlying memory locations so a change to one will change the other
a = torch.ones(5)
b = a.numpy()
a.add_(1)
print(a)
print(b)
Out:
tensor([ 2., 2., 2., 2., 2.])
a = torch.from_numpy(b)
Computation Graphs and Automatic Differentiation
Not fixed like in other things like tensorflow autograd. Variable keeps track of how it was created
# Variables wrap tensor objects
x = autograd.Variable(torch.Tensor([1,2,3]), requires_grad=True)
y = autograd.Variable(torch.Tensor([3,4,5]), requires_grad=True)
z = x + y
print(z.data)
You can access data with .data attr
Can do all operations with x autograd
See comp graph
operation= z.grad_fn
print(operation)
s = z.sum()
print(s)
print(s.grad_fn)
S knows enough about itself to determine its derivative backpropagation calculates gradients with respect to every variable. .backward Run backprop starting from it, running multiple times will accumulate it in .grad prop below. Must keep in data autograd to do grad stuff
s.backward(retain_graph=True)
print(x)
print(x.grad) #value if we differentiate
print(y.grad)
