pytorch练习

1、使用梯度下降法拟合y = sin(x)

import numpy as np
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import time
import os
from skimage import io, transform
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import pandas as pd
import math

import warnings
plt.ion()
np.random.seed(2)


if __name__ == '__main__':
    # landmarks_frame = pd.read_csv('data/faces/face_landmarks.csv')
    # n = 65
    # image_name = landmarks_frame.iloc[n, 0]
    # landmarks_frame = landmarks_frame.iloc[n, 1:].as_matrix()
    # landmarks_frame = landmarks_frame.astype('float').reshape(-1, 2)
    x = np.linspace(-math.pi, math.pi, 2000)
    y = np.sin(x)

    a = np.random.randn()
    b = np.random.randn()
    c = np.random.randn()
    d = np.random.randn()

    learning_rate = 1e-6
    for t in range(10000):
        y_pred = a + b * x + c * x ** 2 +d * x ** 3
        loss = np.square(y_pred - y).sum()
        if t % 100 == 99:
            print(t, loss)
        grad_y_pred = 2.0 * (y_pred - y)
        grad_a = grad_y_pred.sum()
        grad_b = (grad_y_pred * x).sum()
        grad_c = (grad_y_pred * x ** 2).sum()
        grad_d = (grad_y_pred * x ** 3).sum()

        a -= learning_rate * grad_a
        b -= learning_rate * grad_b
        c -= learning_rate * grad_c
        d -= learning_rate * grad_d

    print(f'Result: y = {a} + {b} x + {c} x^2 + {d} X ^ 3')

    # a = np.random.rand(2, 2)
    # print(a)
    # device = torch.device("cuda:0")

2、使用pytorch的自动求导：

import numpy as np
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import time
import os
from skimage import io, transform
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import pandas as pd
import math

import warnings
plt.ion()
np.random.seed(2)


if __name__ == '__main__':
    dtype = torch.float
    # landmarks_frame = pd.read_csv('data/faces/face_landmarks.csv')
    # n = 65
    # image_name = landmarks_frame.iloc[n, 0]
    # landmarks_frame = landmarks_frame.iloc[n, 1:].as_matrix()
    # landmarks_frame = landmarks_frame.astype('float').reshape(-1, 2)
    x = torch.linspace(-math.pi, math.pi, 2000, dtype=dtype)
    y = torch.sin(x)

    a = torch.randn((), dtype=dtype, requires_grad=True)
    b = torch.randn((), dtype=dtype, requires_grad=True)
    c = torch.randn((), dtype=dtype, requires_grad=True)
    d = torch.randn((), dtype=dtype, requires_grad=True)

    learning_rate = 1e-6
    for t in range(10000):
        y_pred = a + b * x + c * x ** 2 + d * x ** 3
        loss = (y_pred - y).pow(2).sum()
        if t % 100 == 99:
            print(t, loss.item())
        loss.backward()
        # grad_y_pred = 2.0 * (y_pred - y)
        # grad_a = grad_y_pred.sum()
        # grad_b = (grad_y_pred * x).sum()
        # grad_c = (grad_y_pred * x ** 2).sum()
        # grad_d = (grad_y_pred * x ** 3).sum()
        with torch.no_grad():
            a -= learning_rate * a.grad
            b -= learning_rate * b.grad
            c -= learning_rate * c.grad
            d -= learning_rate * d.grad

            a.grad = None
            b.grad = None
            c.grad = None
            d.grad = None

    print(f'Result: y = {a} + {b} x + {c} x^2 + {d} X ^ 3')

    # a = np.random.rand(2, 2)
    # print(a)
    # device = torch.device("cuda:0")

3、采用神经网络预测：

import numpy as np
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import time
import os
from skimage import io, transform
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import pandas as pd
import math

import warnings
plt.ion()




if __name__ == '__main__':
    device = 'cpu'
    dtype = torch.float
    x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
    print(x.shape)
    y = torch.sin(x)
    p = torch.tensor([1, 2, 3])
    xx = x.unsqueeze(-1).pow(p)
    model = torch.nn.Sequential(
        torch.nn.Linear(3, 1),
        torch.nn.Flatten(0, 1)
    )
    loss_fn = torch.nn.MSELoss(reduction='sum')

    learning_rate = 1e-6

    for t in range(2000):
        y_pred = model(xx)
        loss = loss_fn(y_pred, y)
        if t % 100 == 99:
            print(t, loss.item())
        model.zero_grad()
        loss.backward()
        with torch.no_grad():
            for parameters in model.parameters():
                parameters -= learning_rate * parameters.grad
    linear_layer = model[0]
    print(
        f'Result: y = {linear_layer.bias.item()} + {linear_layer.weight[:, 0].item()} x + {linear_layer.weight[:, 1].item()} x^2 + {linear_layer.weight[:, 2].item()} x^3')
    # a = np.random.rand(2, 2)
    # print(a)
    # device = torch.device("cuda:0")

4、采用自动计算loss和更新参数等：

import numpy as np
import torch
import torchvision
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import time
import os
from skimage import io, transform
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import pandas as pd
import math
import random

import warnings

plt.ion()


class DynamicNet(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.a = torch.nn.Parameter(torch.randn(()))
        self.b = torch.nn.Parameter(torch.randn(()))
        self.c = torch.nn.Parameter(torch.randn(()))
        self.d = torch.nn.Parameter(torch.randn(()))
        self.e = torch.nn.Parameter(torch.randn(()))

    def forward(self, x):
        y = self.a + self.b * x + self.c * x ** 2 + self.d * x ** 3
        for exp in range(4, random.randint(4, 6)):
            y = y + self.e * x ** exp
        return y

    def string(self):
        return f'y = {self.a.item()} + {self.b.item()} x + {self.c.item()} x^2 + {self.d.item()} x^3 + {self.e.item()} x^4 ? + {self.e.item()} x^5 ?'


if __name__ == '__main__':
    device = 'cpu'
    dtype = torch.float
    x = torch.linspace(-math.pi, math.pi, 2000, device=device, dtype=dtype)
    y = torch.sin(x)
    p = torch.tensor([1, 2, 3])
    model = DynamicNet()
    criterion = torch.nn.MSELoss(reduction='sum')
    learning_rate = 1e-8
    optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9)
    for t in range(30000):
        y_pred = model(x)
        loss = criterion(y_pred, y)
        if t % 2000 == 1999:
            print(t, loss.item())
        model.zero_grad()
        loss.backward()
        optimizer.step()
    print(f'Result: {model.string()}')