RNN_Regression

"""
View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/
My Youtube Channel: https://www.youtube.com/user/MorvanZhou
Dependencies:
torch: 0.4
matplotlib
numpy
"""
from torchsummary import summary
from torchviz import make_dot
import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible

# Hyper Parameters
TIME_STEP = 10  # rnn time step
INPUT_SIZE = 1  # rnn input size
LR = 0.02  # learning rate

# show data
# steps = np.linspace(0, np.pi * 2, 100, dtype=np.float32)  # float32 for converting torch FloatTensor
# 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,  # rnn hidden unit
            num_layers=1,  # number of rnn layer
            batch_first=True,  # input & output will has batch size as 1s dimension. e.g. (batch, time_step, input_size)
        )
        self.out = nn.Linear(32, 1)

    def forward(self, x, h_state): # 因为 hidden state 是连续的, 所以我们要一直传递这一个 state
        # x (batch, time_step, input_size)
        # h_state (n_layers, batch, hidden_size)
        # r_out (batch, time_step, hidden_size) 1*10*32
        # 在rnn分类中 是批量放入28条数据 每一条输出的h_n,h_c自动传入下一条，自动了27次，最后一次输出了
        # 但在回归问题中 每个batch数据需要有之前的数据，所以上一个batch的h_state显式传给了下个batch
        # 一个batch内的h_state是隐式传递的 batch间的h_state是显式传递的
        r_out, h_state = self.rnn(x, h_state)
        print(torch._shape_as_tensor(r_out)) #tensor([ 1, 10, 32])

        outs = []  # save all predictions
        for time_step in range(r_out.size(1)):  # calculate output for each time step 从1到10(TIME_STEP)
            outs.append(self.out(r_out[:, time_step, :]))
        #r_out 为1*10*32，r_out[:, time_step, :]为1*1*32，过全连接得到1*1*1 TIME_STEP个append之后
        # 相比上一个回归中的输出 这里outs存储的是每一个时间步的输出
        # print(len(outs)) #为10
        return torch.stack(outs, dim=1), h_state #从10*1*1变为1*10*1
        # torch.Size([1, 10, 1]) time_step

        # instead, for simplicity, you can replace above codes by follows
        # r_out = r_out.view(-1, 32)
        # outs = self.out(r_out)
        # outs = outs.view(-1, TIME_STEP, 1)
        # return outs, h_state

        # or even simpler, since nn.Linear can accept inputs of any dimension
        # and returns outputs with same dimension except for the last
        # outs = self.out(r_out)
        # return outs


rnn = RNN()
print(rnn)

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

h_state = None  # for initial hidden state

plt.figure(1, figsize=(12, 5))
plt.ion()  # continuously plot

for step in range(85):
    start, end = step * np.pi, (step + 1) * np.pi  # time range
    # use sin predicts cos
    steps = np.linspace(start, end, TIME_STEP, dtype=np.float32,
                        endpoint=False)  # float32 for converting torch FloatTensor
    x_np = np.sin(steps)
    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 output
    #print(prediction.size())
    # !! next step is important !!
    # !!  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)  # calculate loss
    optimizer.zero_grad()  # clear gradients for this training step
    loss.backward()  # backpropagation, compute gradients
    optimizer.step()  # apply gradients

    # plotting
    plt.plot(steps, y_np.flatten(), 'r-')
    plt.plot(steps, prediction.data.numpy().flatten(), 'b-')
    plt.draw();
    plt.pause(0.05)

#summary(rnn, input_size=(1,10,1)) # input_size=(channels, H, W)

# 可视化网络2
vis_graph = make_dot(prediction, params=dict(rnn.named_parameters()))
vis_graph.view()
plt.ioff()
plt.show()

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