pytorch学习笔记

参考莫烦python：

cnn:

import torch
import torch.utils.data as Data
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torch.nn as nn
import torchvision

EPOCH = 1
BATCH_SIZE = 50
LR = 0.01
DOWNLOAD_MNIST = False

train_data = torchvision.datasets.MNIST(
    root = './minst/',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST
)

# plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_data_labels[0])
# plt.show()

train_loader = Data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)

test_data = torchvision.datasets.MNIST(root='./minst/', train=False)
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential( #1 * 16 * 16
            nn.Conv2d(
                in_channels=1,
                out_channels=16,
                kernel_size=5,
                stride=1,
                padding=2,
            ), #16 * 28 * 28
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2), #16 * 14 * 14
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 5, 1, 2), #32 * 14 * 14
            nn.ReLU(),
            nn.MaxPool2d(2) #32 * 7 * 7
        )
        self.out = nn.Linear(32 * 7 * 7, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.conv2(x)
        x = x.view(x.size(0), -1) #压缩图像成一维
        output = self.out(x)
        return output

cnn = CNN()
print(cnn)

optimizer = torch.optim.Adam(cnn.parameters(), lr=LR)   # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()   # the target label is not one-hotted

# training and testing
for epoch in range(EPOCH):
    for step, (b_x, b_y) in enumerate(train_loader):   # 分配 batch data, normalize x when iterate train_loader
        output = cnn(b_x)               # cnn output
        loss = loss_func(output, b_y)   # cross entropy loss
        optimizer.zero_grad()           # clear gradients for this training step
        loss.backward()                 # backpropagation, compute gradients
        optimizer.step()                # apply gradients

        if step % 50 == 0:
            test_output = cnn(test_x)
            pred_y = torch.max(test_output, 1)[1].data.squeeze()
            accuracy = sum(pred_y == test_y) / float(test_y.size(0))
            print('Epoch:', epoch, '|train loss: %.4f' % loss.data)
            print('|accuracy: %.4f' % accuracy)

test_output = cnn(test_x[:10])
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number')

 rnn：

import torch
import torch.utils.data as Data
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torch.nn as nn
import torchvision

EPOCH = 1
BATCH_SIZE = 64
TIME_STEP = 28
INPUT_SIZE = 28
LR = 0.01
DOWNLOAD_MNIST = False

train_data = torchvision.datasets.MNIST(
    root='./minst/',
    train=True,
    transform=torchvision.transforms.ToTensor(),
    download=DOWNLOAD_MNIST
)

# plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_data_labels[0])
# plt.show()

train_loader = Data.DataLoader(train_data, batch_size=BATCH_SIZE, shuffle=True)

test_data = torchvision.datasets.MNIST(root='./minst/', train=False)
test_x = torch.unsqueeze(test_data.test_data, dim=1).type(torch.FloatTensor)[:2000]/255.   # shape from (2000, 28, 28) to (2000, 1, 28, 28), value in range(0,1)
test_y = test_data.test_labels[:2000]

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()
        self.rnn = nn.LSTM(
            input_size=INPUT_SIZE,
            hidden_size=64,
            num_layers=1,
            batch_first=True,
        )
        self.out = nn.Linear(64, 10)

    def forward(self, x):
        r_out, (h_n, h_c) = self.rnn(x, None)
        out = self.out(r_out[:, -1, :])
        return out

rnn = RNN()
print(rnn)

optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all cnn parameters
loss_func = nn.CrossEntropyLoss()   # the target label is not one-hotted

# training and testing
for epoch in range(EPOCH):
    for step, (x, b_y) in enumerate(train_loader):   # 分配 batch data, normalize x when iterate train_loader
        b_x = x.view(-1, 28, 28)
        output = rnn(b_x)               # cnn output
        loss = loss_func(output, b_y)   # cross entropy loss
        optimizer.zero_grad()           # clear gradients for this training step
        loss.backward()                 # backpropagation, compute gradients
        optimizer.step()                # apply gradients

        if step % 50 == 0:
            test_output = rnn(test_x.view(-1, 28, 28))
            pred_y = torch.max(test_output, 1)[1].data.squeeze()
            accuracy = sum(pred_y == test_y) / float(test_y.size(0))
            print('Epoch:', epoch, '|train loss: %.4f' % loss.data)
            print('|accuracy: %.4f' % accuracy)

test_output = rnn(test_x[:10].view(-1, 28, 28))
pred_y = torch.max(test_output, 1)[1].data.numpy().squeeze()
print(pred_y, 'prediction number')
print(test_y[:10].numpy(), 'real number')

 rnn（回归）

import torch
import torch.utils.data as Data
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torch.nn as nn
import torchvision
import numpy as np

EPOCH = 1
BATCH_SIZE = 64
TIME_STEP = 28
INPUT_SIZE = 1
LR = 0.02
DOWNLOAD_MNIST = False

# train_data = torchvision.datasets.MNIST(
#     root='./minst/',
#     train=True,
#     transform=torchvision.transforms.ToTensor(),
#     download=DOWNLOAD_MNIST
# )

# plt.imshow(train_data.train_data[0].numpy(), cmap='gray')
# plt.title('%i' % train_data.train_data_labels[0])
# plt.show()

steps = np.linspace(0, np.pi * 2, 100, dtype=np.float32)
# x_np = np.sin(steps)
# y_np = np.cos(steps)
# plt.plot(steps, y_np, 'r-', label='target(cos)')
# plt.plot(steps, x_np, 'b-', label='input(sin)')
# plt.legend(loc='best')
# plt.show()

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()
        self.rnn = nn.RNN(
            input_size=INPUT_SIZE,
            hidden_size=32,
            num_layers=1,
            batch_first=True
        )
        self.out = nn.Linear(32, 1)

    def forward(self, x, h_state):
        r_out, h_state = self.rnn(x, h_state)
        outs = []
        for time_step in range(r_out.size(1)):
            outs.append(self.out(r_out[:, time_step, :]))
        return torch.stack(outs, dim=1), h_state

rnn = RNN()
print(rnn)

optimizer = torch.optim.Adam(rnn.parameters(), lr=LR)   # optimize all rnn parameters
loss_func = nn.MSELoss()

h_state = None   # 要使用初始 hidden state, 可以设成 None

for step in range(100):
    start, end = step * np.pi, (step+1)*np.pi   # time steps
    # sin 预测 cos
    steps = np.linspace(start, end, 10, dtype=np.float32)
    x_np = np.sin(steps)    # float32 for converting torch FloatTensor
    y_np = np.cos(steps)

    x = torch.from_numpy(x_np[np.newaxis, :, np.newaxis])    # shape (batch, time_step, input_size)
    y = torch.from_numpy(y_np[np.newaxis, :, np.newaxis])

    prediction, h_state = rnn(x, h_state)   # rnn 对于每个 step 的 prediction, 还有最后一个 step 的 h_state
    # !!  下一步十分重要 !!
    h_state = h_state.data  # 要把 h_state 重新包装一下才能放入下一个 iteration, 不然会报错

    loss = loss_func(prediction, y)     # cross entropy loss
    optimizer.zero_grad()               # clear gradients for this training step
    loss.backward()                     # backpropagation, compute gradients
    optimizer.step()                    # apply gradients
    if step % 5 == 0:
        tmp = torch.squeeze(prediction)
        plt.plot(steps, y_np, 'r-', label='target(cos)')
        plt.plot(steps, tmp.detach().numpy(), 'b-', label='output')
        plt.legend(loc='best')
        # plt.show()
        plt.ion()
        plt.pause(1)
        plt.close()