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#pytorch神经网络搭建主要就是如下步骤：

1.分类

import torchimport torch.nn.functional as Fimport matplotlib.pyplot as plt# torch.manual_seed(1)    # reproducible# make fake datan_data = torch.ones(100, 2)x0 = torch.normal(2*n_data, 1)      # class0 x data (tensor), shape=(100, 2)y0 = torch.zeros(100)               # class0 y data (tensor), shape=(100, 1)x1 = torch.normal(-2*n_data, 1)     # class1 x data (tensor), shape=(100, 2)y1 = torch.ones(100)                # class1 y data (tensor), shape=(100, 1)x = torch.cat((x0, x1), 0).type(torch.FloatTensor)  # shape (200, 2) FloatTensor = 32-bit floatingy = torch.cat((y0, y1), ).type(torch.LongTensor)    # shape (200,) LongTensor = 64-bit integer# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors# x, y = Variable(x), Variable(y)# plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=y.data.numpy(), s=100, lw=0, cmap='RdYlGn')# plt.show()class Net(torch.nn.Module):    def __init__(self, n_feature, n_hidden, n_output):        super(Net, self).__init__()        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer        self.out = torch.nn.Linear(n_hidden, n_output)   # output layer    def forward(self, x):        x = F.relu(self.hidden(x))      # activation function for hidden layer        x = self.out(x)        return xnet = Net(n_feature=2, n_hidden=10, n_output=2)     # define the networkprint(net)  # net architectureoptimizer = torch.optim.SGD(net.parameters(), lr=0.02)loss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hottedplt.ion()   # something about plottingfor t in range(100):    out = net(x)                 # input x and predict based on x    loss = loss_func(out, y)     # must be (1. nn output, 2. target), the target label is NOT one-hotted    optimizer.zero_grad()   # clear gradients for next train    loss.backward()         # backpropagation, compute gradients    optimizer.step()        # apply gradients    if t % 2 == 0:        # plot and show learning process        plt.cla()        prediction = torch.max(out, 1)[1]        pred_y = prediction.data.numpy()        target_y = y.data.numpy()        plt.scatter(x.data.numpy()[:, 0], x.data.numpy()[:, 1], c=pred_y, s=100, lw=0, cmap='RdYlGn')        accuracy = float((pred_y == target_y).astype(int).sum()) / float(target_y.size)        plt.text(1.5, -4, 'Accuracy=%.2f' % accuracy, fontdict={'size': 20, 'color':  'red'})        plt.pause(0.1)plt.ioff()plt.show()

2，回归

import torchimport torch.nn.functional as Fimport matplotlib.pyplot as plt# torch.manual_seed(1)    # reproduciblex = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)  # x data (tensor), shape=(100, 1)y = x.pow(2) + 0.2*torch.rand(x.size())                 # noisy y data (tensor), shape=(100, 1)# torch can only train on Variable, so convert them to Variable# The code below is deprecated in Pytorch 0.4. Now, autograd directly supports tensors# x, y = Variable(x), Variable(y)plt.scatter(x.data.numpy(), y.data.numpy())plt.show()#第一种模型搭建方式class Net(torch.nn.Module):    def __init__(self, n_feature, n_hidden, n_output):        super(Net, self).__init__()        self.hidden = torch.nn.Linear(n_feature, n_hidden)   # hidden layer        self.predict = torch.nn.Linear(n_hidden, n_output)   # output layer    def forward(self, x):        x = F.relu(self.hidden(x))      # activation function for hidden layer        x = self.predict(x)             # linear output        return x#第二种模型搭建方式class Net(torch.nn.Module):    def __init__(self, n_feature, n_hidden, n_output):        super(Net, self).__init__()        self.mode1 = torch.nn.Sequential(            torch.nn.Linear(n_feature, n_hidden),            torch.nn.ReLU(),            torch.nn.Linear(n_hidden, n_output)        )    def forward(self, x):        # x = F.relu(self.hidden(x))      # activation function for hidden layer        x = self.mode1(x)             # linear output        return xnet = Net(n_feature=1, n_hidden=10, n_output=1)     # define the networkprint(net)  # net architectureoptimizer = torch.optim.SGD(net.parameters(), lr=0.2)loss_func = torch.nn.MSELoss()  # this is for regression mean squared lossplt.ion()   # something about plottingfor t in range(200):    prediction = net(x)     # input x and predict based on x    loss = loss_func(prediction, y)     # must be (1. nn output, 2. target)    optimizer.zero_grad()   # clear gradients for next train    loss.backward()         # backpropagation, compute gradients    optimizer.step()        # apply gradients    if t % 5 == 0:        # plot and show learning process        plt.cla()        plt.scatter(x.data.numpy(), y.data.numpy())        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color':  'red'})        plt.pause(0.1)plt.ioff()plt.show()

import torchfrom torch import nn, optimfrom torch.autograd import Variableimport numpy as npimport matplotlib.pyplot as pltx_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168],                    [9.779], [6.182], [7.59], [2.167], [7.042],                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573],                    [3.366], [2.596], [2.53], [1.221], [2.827],                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)x_train = torch.from_numpy(x_train)y_train = torch.from_numpy(y_train)# Linear Regression Modelclass LinearRegression(nn.Module):    def __init__(self):        super(LinearRegression, self).__init__()        self.linear = nn.Linear(1, 1)  # input and output is 1 dimension    def forward(self, x):        out = self.linear(x)        return outmodel = LinearRegression()# 定义loss和优化函数criterion = nn.MSELoss()optimizer = optim.SGD(model.parameters(), lr=1e-4)# 开始训练num_epochs = 1000for epoch in range(num_epochs):    inputs = Variable(x_train)    target = Variable(y_train)    # forward    out = model(inputs)    loss = criterion(out, target)    # backward    optimizer.zero_grad()    loss.backward()    optimizer.step()    if (epoch+1) % 20 == 0:        print('Epoch[{}/{}], loss: {:.6f}'.format(epoch+1, num_epochs, loss.item()))              # .format(epoch+1, num_epochs, loss.data[0]))model.eval()predict = model(Variable(x_train))predict = predict.data.numpy()plt.plot(x_train.numpy(), y_train.numpy(), 'ro', label='Original data')plt.plot(x_train.numpy(), predict, label='Fitting Line')# 显示图例plt.legend()plt.show()

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