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PyTorch教程之從零開(kāi)始的遞歸神經(jīng)網(wǎng)絡(luò)實(shí)現(xiàn)

1857400 2023-06-05 | pdf | 0.36 MB | 次下載 | 免費(fèi)

資料介紹

我們現(xiàn)在準(zhǔn)備好從頭開(kāi)始實(shí)施 RNN。特別是，我們將訓(xùn)練此 RNN 作為字符級(jí)語(yǔ)言模型（參見(jiàn) 第 9.4 節(jié)），并按照第 9.2 節(jié)中概述的數(shù)據(jù)處理步驟，在由 HG Wells 的《時(shí)間機(jī)器》的整個(gè)文本組成的語(yǔ)料庫(kù)上對(duì)其進(jìn)行訓(xùn)練. 我們首先加載數(shù)據(jù)集。

						%matplotlib inline
import math
import torch
from torch import nn
from torch.nn import functional as F
from d2l import torch as d2l

						 

						%matplotlib inline
import math
from mxnet import autograd, gluon, np, npx
from d2l import mxnet as d2l

npx.set_np()

						 

						%matplotlib inline
import math
import jax
from flax import linen as nn
from jax import numpy as jnp
from d2l import jax as d2l

						 

						%matplotlib inline
import math
import tensorflow as tf
from d2l import tensorflow as d2l

						 

9.5.1. 循環(huán)神經(jīng)網(wǎng)絡(luò)模型

我們首先定義一個(gè)類(lèi)來(lái)實(shí)現(xiàn) RNN 模型（第 9.4.2 節(jié)）。請(qǐng)注意，隱藏單元的數(shù)量num_hiddens是一個(gè)可調(diào)的超參數(shù)。

							class RNNScratch(d2l.Module): #@save
  """The RNN model implemented from scratch."""
  def __init__(self, num_inputs, num_hiddens, sigma=0.01):
    super().__init__()
    self.save_hyperparameters()
    self.W_xh = nn.Parameter(
      torch.randn(num_inputs, num_hiddens) * sigma)
    self.W_hh = nn.Parameter(
      torch.randn(num_hiddens, num_hiddens) * sigma)
    self.b_h = nn.Parameter(torch.zeros(num_hiddens))

							 

							class RNNScratch(d2l.Module): #@save
  """The RNN model implemented from scratch."""
  def __init__(self, num_inputs, num_hiddens, sigma=0.01):
    super().__init__()
    self.save_hyperparameters()
    self.W_xh = np.random.randn(num_inputs, num_hiddens) * sigma
    self.W_hh = np.random.randn(
      num_hiddens, num_hiddens) * sigma
    self.b_h = np.zeros(num_hiddens)

							 

							class RNNScratch(nn.Module): #@save
  """The RNN model implemented from scratch."""
  num_inputs: int
  num_hiddens: int
  sigma: float = 0.01

  def setup(self):
    self.W_xh = self.param('W_xh', nn.initializers.normal(self.sigma),
                (self.num_inputs, self.num_hiddens))
    self.W_hh = self.param('W_hh', nn.initializers.normal(self.sigma),
                (self.num_hiddens, self.num_hiddens))
    self.b_h = self.param('b_h', nn.initializers.zeros, (self.num_hiddens))

							 

							class RNNScratch(d2l.Module): #@save
  """The RNN model implemented from scratch."""
  def __init__(self, num_inputs, num_hiddens, sigma=0.01):
    super().__init__()
    self.save_hyperparameters()
    self.W_xh = tf.Variable(tf.random.normal(
      (num_inputs, num_hiddens)) * sigma)
    self.W_hh = tf.Variable(tf.random.normal(
      (num_hiddens, num_hiddens)) * sigma)
    self.b_h = tf.Variable(tf.zeros(num_hiddens))

							 

下面的方法forward定義了如何計(jì)算任何時(shí)間步的輸出和隱藏狀態(tài)，給定當(dāng)前輸入和模型在前一個(gè)時(shí)間步的狀態(tài)。請(qǐng)注意，RNN 模型循環(huán)遍歷的最外層維度inputs，一次更新隱藏狀態(tài)。這里的模型使用了tanh激活函數(shù)（第 5.1.2.3 節(jié)）。

							@d2l.add_to_class(RNNScratch) #@save
def forward(self, inputs, state=None):
  if state is None:
    # Initial state with shape: (batch_size, num_hiddens)
    state = torch.zeros((inputs.shape[1], self.num_hiddens),
             device=inputs.device)
  else:
    state, = state
  outputs = []
  for X in inputs: # Shape of inputs: (num_steps, batch_size, num_inputs)
    state = torch.tanh(torch.matmul(X, self.W_xh) +
             torch.matmul(state, self.W_hh) + self.b_h)
    outputs.append(state)
  return outputs, state

							 

							@d2l.add_to_class(RNNScratch) #@save
def forward(self, inputs, state=None):
  if state is None:
    # Initial state with shape: (batch_size, num_hiddens)
    state = np.zeros((inputs.shape[1], self.num_hiddens),
             ctx=inputs.ctx)
  else:
    state, = state
  outputs = []
  for X in inputs: # Shape of inputs: (num_steps, batch_size, num_inputs)
    state = np.tanh(np.dot(X, self.W_xh) +
             np.dot(state, self.W_hh) + self.b_h)
    outputs.append(state)
  return outputs, state

							 

							@d2l.add_to_class(RNNScratch) #@save
def __call__(self, inputs, state=None):
  if state is not None:
    state, = state
  outputs = []
  for X in inputs: # Shape of inputs: (num_steps, batch_size, num_inputs)
    state = jnp.tanh(jnp.matmul(X, self.W_xh) + (
      jnp.matmul(state, self.W_hh) if state is not None else 0)
             + self.b_h)
    outputs.append(state)
  return outputs, state

							 

							@d2l.add_to_class(RNNScratch) #@save
def forward(self, inputs, state=None):
  if state is None:
    # Initial state with shape: (batch_size, num_hiddens)
    state = tf.zeros((inputs.shape[1], self.num_hiddens))
  else:
    state, = state
    state = tf.reshape(state, (-1, self.num_hiddens))
  outputs = []
  for X in inputs: # Shape of inputs: (num_steps, batch_size, num_inputs)
    state = tf.tanh(tf.matmul(X, self.W_xh) +
             tf.matmul(state, self.W_hh) + self.b_h)
    outputs.append(state)
  return outputs, state

							 

我們可以將一小批輸入序列輸入 RNN 模型，如下所示。

							batch_size, num_inputs, num_hiddens, num_steps = 2, 16, 32, 100
rnn = RNNScratch(num_inputs, num_hiddens)
X = torch.ones((num_steps, batch_size, num_inputs))
outputs, state = rnn(X)

							 

							batch_size, num_inputs, num_hiddens, num_steps = 2, 16, 32, 100
rnn = RNNScratch(num_inputs, num_hiddens)
X = np.ones((num_steps, batch_size, num_inputs))
outputs, state = rnn(X)

							 

							batch_size, num_inputs, num_hiddens, num_steps = 2, 16, 32, 100
rnn = RNNScratch(num_inputs, num_hiddens)
X = jnp.ones((num_steps, batch_size, num_inputs))
(output
						