<jupyter_start><jupyter_text>This is my intro to pyTorch<jupyter_code>from __future__ import print_function
import torch
import numpy as np
import pandas as pd
x = torch.zeros(5, 3)
print(x)

x = torch.rand(5, 3)
print('\n',x)

x = torch.ones(5, 3)
print('\n',x)

y = torch.rand(5, 3)
print('\n',x + y)

print('\n',torch.add(x, y))
a = torch.ones(5)
print(a)
b = a.numpy()
print(b)
x = torch.ones(2, 2, requires_grad=True)
print(x)
y = x + 2
print(y)
print(y.grad_fn)
z = y * y * 3
out = z.mean()

print(z, out)

print(out.backward(),'\n')
print(x.grad)
v = torch.tensor([0.1, 1.0, 0.0001], dtype=torch.float)
y.backward(v)

print(x.grad)print(x.requires_grad)
print((x ** 2).requires_grad)

with torch.no_grad():
    print((x ** 2).requires_grad)
x = torch.randn(3, requires_grad=True)

y = x * 2
while y.data.norm() < 1000:
    y = y * 2

print(y)<jupyter_output>tensor([ 252.4171, -911.1869,  913.0145], grad_fn=<MulBackward0>)<jupyter_text>A typical training procedure for a neural network is as follows:Define the neural network that has some learnable parameters (or weights)Iterate over a dataset of inputsProcess input through the networkCompute the loss (how far is the output from being correct)Propagate gradients back into the network’s parametersUpdate the weights of the network, typically using a simple update rule: weight = weight - learning_rate * gradient<jupyter_code>import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 3x3 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 3)
        self.conv2 = nn.Conv2d(6, 16, 3)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 6 * 6, 120)  # 6*6 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net)
params = list(net.parameters())
print(len(params))
print(params[0].size())  # conv1's .weight
input = torch.randn(1, 1, 32, 32)
out = net(input)
print(out)
net.zero_grad()
out.backward(torch.randn(1, 10))
output = net(input)
target = torch.randn(10)  # a dummy target, for example
target = target.view(1, -1)  # make it the same shape as output
criterion = nn.MSELoss()

loss = criterion(output, target)
print(loss)
print(loss.grad_fn)  # MSELoss
print(loss.grad_fn.next_functions[0][0])  # Linear
print(loss.grad_fn.next_functions[0][0].next_functions[0][0])  # ReLU
net.zero_grad()     # zeroes the gradient buffers of all parameters

print('conv1.bias.grad before backward')
print(net.conv1.bias.grad)

loss.backward()

print('conv1.bias.grad after backward')
print(net.conv1.bias.grad)
learning_rate = 0.01
for f in net.parameters():
    f.data.sub_(f.grad.data * learning_rate)
import torch.optim as optim

# create your optimizer
optimizer = optim.SGD(net.parameters(), lr=0.01)

# in your training loop:
optimizer.zero_grad()   # zero the gradient buffers
output = net(input)
loss = criterion(output, target)
loss.backward()
optimizer.step()    # Does the update<jupyter_output><empty_output>
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