import torch

from torch.autograd import Variable

import torch.nn.functional as F

import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible

# make fake data

n_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 floating

y = 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 x

net = Net(n_feature=2, n_hidden=10, n_output=2)     # define the network,输入两个特征

print(net)  # net architecture

optimizer = torch.optim.SGD(net.parameters(), lr=0.02)

loss_func = torch.nn.CrossEntropyLoss()  # the target label is NOT an one-hotted

#分类输出的为概率

plt.ion()   # something about plotting

for t in range(100):

    out = net(x)                 # input x and predict based on x,输出原值不是概率，需要用激活函数转化为概率

    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(F.softmax(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()