# -*- coding: utf-8 -*-
import random
import torch


class DynamicNet(torch.nn.Module):
  def __init__(self, D_in, H, D_out):
    """
    這里構(gòu)造了幾個向前傳播過程中用到的線性函數(shù)
    """
    super(DynamicNet, self).__init__()
    self.input_linear = torch.nn.Linear(D_in, H)
    self.middle_linear = torch.nn.Linear(H, H)
    self.output_linear = torch.nn.Linear(H, D_out)

  def forward(self, x):
    """
    For the forward pass of the model, we randomly choose either 0, 1, 2, or 3
    and reuse the middle_linear Module that many times to compute hidden layer
    representations.

    Since each forward pass builds a dynamic computation graph, we can use normal
    Python control-flow operators like loops or conditional statements when
    defining the forward pass of the model.

    Here we also see that it is perfectly safe to reuse the same Module many
    times when defining a computational graph. This is a big improvement from Lua
    Torch, where each Module could be used only once.
    這里中間層每次向前過程中都是隨機(jī)添加0-3層，而且中間層都是使用的同一個線性層，這樣計(jì)算時，權(quán)值也是用的同一個。
    """
    h_relu = self.input_linear(x).clamp(min=0)
    for _ in range(random.randint(0, 3)):
      h_relu = self.middle_linear(h_relu).clamp(min=0)
    y_pred = self.output_linear(h_relu)
    return y_pred


    # N is batch size; D_in is input dimension;
    # H is hidden dimension; D_out is output dimension.
    N, D_in, H, D_out = 64, 1000, 100, 10

    # Create random Tensors to hold inputs and outputs
    x = torch.randn(N, D_in)
    y = torch.randn(N, D_out)

    # Construct our model by instantiating the class defined above
    model = DynamicNet(D_in, H, D_out)

    # Construct our loss function and an Optimizer. Training this strange model with
    # vanilla stochastic gradient descent is tough, so we use momentum
    criterion = torch.nn.MSELoss(reduction='sum')
    optimizer = torch.optim.SGD(model.parameters(), lr=1e-4, momentum=0.9)
    for t in range(500):
      # Forward pass: Compute predicted y by passing x to the model
      y_pred = model(x)

      # Compute and print loss
      loss = criterion(y_pred, y)
      print(t, loss.item())

      # Zero gradients, perform a backward pass, and update the weights.
      optimizer.zero_grad()
      loss.backward()
      optimizer.step()