为什么 Pytorch 是动态 Dynamic

在构建神经网络时，我们常会遭遇部分复杂情形。一般而言，神经网络构建好之后便可以不再变动，但是在部分情况下，我们需要变动神经网络的结构以完成某些特殊要求，这时候Pytorch的优势便显示出来了，因为Pytorch本身就是动态的。当然，Pytorch也自带某些动态的神经网络结构，如Dynamic RNN等

1. 准备初始参数

import torch
from torch import nn
import numpy as np
import matplotlib.pyplot as plt

# torch.manual_seed(1)    # reproducible

# Hyper Parameters
INPUT_SIZE = 1          # rnn input size / image width
LR = 0.02               # learning rate

2. 构建普通RNN模型

class RNN(nn.Module):
    def __init__(self):
        super(RNN, self).__init__()

        self.rnn = nn.RNN(
            input_size=1,
            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):
        # x (batch, time_step, input_size)
        # h_state (n_layers, batch, hidden_size)
        # r_out (batch, time_step, output_size)
        r_out, h_state = self.rnn(x, h_state)

        outs = []                                   # this is where you can find torch is dynamic
        for time_step in range(r_out.size(1)):      # calculate output for each time step
            outs.append(self.out(r_out[:, time_step, :]))
        return torch.stack(outs, dim=1), h_state


rnn = RNN()
print(rnn)

3. 选择优化器和损失函数

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

4. 初始化状态和plt图像

h_state = None   # for initial hidden state

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

5. Dynamic RNN条件

################ static time steps ##########
# for step in range(60):
#     start, end = step * np.pi, (step+1)*np.pi   # time steps
#     # use sin predicts cos
#     steps = np.linspace(start, end, 10, dtype=np.float32)

################ dynamic time steps #########
step = 0
for i in range(60):
    dynamic_steps = np.random.randint(1, 4)  # has random time steps
    start, end = step * np.pi, (step + dynamic_steps) * np.pi  # different time steps length
    step += dynamic_steps

    # use sin predicts cos
    steps = np.linspace(start, end, 10 * dynamic_steps, dtype=np.float32)

6. 训练和优化

6.1 数据预处理

    print(len(steps))       # print how many time step feed to RNN

    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 output
    # !! next step is important !!
    h_state = h_state.data        # repack the hidden state, break the connection from last iteration

6.2 优化

    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

6.3 训练过程可视化

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

plt.ioff()
plt.show()