解释:class GruRNN(nn.Module):
时间: 2024-02-22 21:43:32 浏览: 18
class GruRNN(nn.Module)是一个PyTorch中的类,用于实现Gated Recurrent Unit(GRU)这种循环神经网络模型。GRU是一种常用于自然语言处理和序列数据处理的模型,其结构相对于其他循环神经网络具有更少的参数和计算量。在该类中,定义了GRU需要的各种参数和网络的前向传播过程,可以通过实例化该类来创建一个GRU模型并进行训练或预测。
相关问题
import torch import torch.nn as nn import torch.optim as optim import numpy as np 定义基本循环神经网络模型 class RNNModel(nn.Module): def init(self, rnn_type, input_size, hidden_size, output_size, num_layers=1): super(RNNModel, self).init() self.rnn_type = rnn_type self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.encoder = nn.Embedding(input_size, hidden_size) if rnn_type == 'RNN': self.rnn = nn.RNN(hidden_size, hidden_size, num_layers) elif rnn_type == 'GRU': self.rnn = nn.GRU(hidden_size, hidden_size, num_layers) self.decoder = nn.Linear(hidden_size, output_size) def forward(self, input, hidden): input = self.encoder(input) output, hidden = self.rnn(input, hidden) output = output.view(-1, self.hidden_size) output = self.decoder(output) return output, hidden def init_hidden(self, batch_size): if self.rnn_type == 'RNN': return torch.zeros(self.num_layers, batch_size, self.hidden_size) elif self.rnn_type == 'GRU': return torch.zeros(self.num_layers, batch_size, self.hidden_size) 定义数据集 with open('汉语音节表.txt', encoding='utf-8') as f: chars = f.readline() chars = list(chars) idx_to_char = list(set(chars)) char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)]) corpus_indices = [char_to_idx[char] for char in chars] 定义超参数 input_size = len(idx_to_char) hidden_size = 256 output_size = len(idx_to_char) num_layers = 1 batch_size = 32 num_steps = 5 learning_rate = 0.01 num_epochs = 100 定义模型、损失函数和优化器 model = RNNModel('RNN', input_size, hidden_size, output_size, num_layers) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=learning_rate) 训练模型 for epoch in range(num_epochs): model.train() hidden = model.init_hidden(batch_size) loss = 0 for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps): optimizer.zero_grad() hidden = hidden.detach() output, hidden = model(X, hidden) loss = criterion(output, Y.view(-1)) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0) optimizer.step() if epoch % 10 == 0: print(f"Epoch {epoch}, Loss: {loss.item()}")请正确缩进代码
下面是正确缩进的代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# 定义基本循环神经网络模型
class RNNModel(nn.Module):
def __init__(self, rnn_type, input_size, hidden_size, output_size, num_layers=1):
super(RNNModel, self).__init__()
self.rnn_type = rnn_type
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layers = num_layers
self.encoder = nn.Embedding(input_size, hidden_size)
if rnn_type == 'RNN':
self.rnn = nn.RNN(hidden_size, hidden_size, num_layers)
elif rnn_type == 'GRU':
self.rnn = nn.GRU(hidden_size, hidden_size, num_layers)
self.decoder = nn.Linear(hidden_size, output_size)
def forward(self, input, hidden):
input = self.encoder(input)
output, hidden = self.rnn(input, hidden)
output = output.view(-1, self.hidden_size)
output = self.decoder(output)
return output, hidden
def init_hidden(self, batch_size):
if self.rnn_type == 'RNN':
return torch.zeros(self.num_layers, batch_size, self.hidden_size)
elif self.rnn_type == 'GRU':
return torch.zeros(self.num_layers, batch_size, self.hidden_size)
# 定义数据集
with open('汉语音节表.txt', encoding='utf-8') as f:
chars = f.readline()
chars = list(chars)
idx_to_char = list(set(chars))
char_to_idx = dict([(char, i) for i, char in enumerate(idx_to_char)])
corpus_indices = [char_to_idx[char] for char in chars]
# 定义超参数
input_size = len(idx_to_char)
hidden_size = 256
output_size = len(idx_to_char)
num_layers = 1
batch_size = 32
num_steps = 5
learning_rate = 0.01
num_epochs = 100
# 定义模型、损失函数和优化器
model = RNNModel('RNN', input_size, hidden_size, output_size, num_layers)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
for epoch in range(num_epochs):
model.train()
hidden = model.init_hidden(batch_size)
loss = 0
for X, Y in data_iter_consecutive(corpus_indices, batch_size, num_steps):
optimizer.zero_grad()
hidden = hidden.detach()
output, hidden = model(X, hidden)
loss = criterion(output, Y.view(-1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
if epoch % 10 == 0:
print(f"Epoch {epoch}, Loss: {loss.item()}")
```
解决AttributeError: module 'tensorflow._api.v2.compat.v1.nn.rnn_cell' has no attribute 'seq2seq'
在 TensorFlow 2.0 中,`tf.nn.rnn_cell` 模块已经被移除了,因此 `tf.nn.rnn_cell.seq2seq` 也不再可用。相反,您可以使用 `tf.keras.layers` 中的相应函数来构建您的模型。下面是一个使用 `tf.keras` 实现 seq2seq 模型的示例:
``` python
import tensorflow as tf
# 定义编码器
class Encoder(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):
super(Encoder, self).__init__()
self.batch_sz = batch_sz
self.enc_units = enc_units
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(self.enc_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
def call(self, x, hidden):
x = self.embedding(x)
output, state = self.gru(x, initial_state = hidden)
return output, state
def initialize_hidden_state(self):
return tf.zeros((self.batch_sz, self.enc_units))
# 定义注意力层
class BahdanauAttention(tf.keras.layers.Layer):
def __init__(self, units):
super(BahdanauAttention, self).__init__()
self.W1 = tf.keras.layers.Dense(units)
self.W2 = tf.keras.layers.Dense(units)
self.V = tf.keras.layers.Dense(1)
def call(self, query, values):
# query: 上一时间步的隐藏状态,shape=(batch_size, hidden_size)
# values: 编码器的输出,shape=(batch_size, max_length, hidden_size)
hidden_with_time_axis = tf.expand_dims(query, 1)
score = self.V(tf.nn.tanh(
self.W1(values) + self.W2(hidden_with_time_axis)))
# attention_weights shape == (batch_size, max_length, 1)
attention_weights = tf.nn.softmax(score, axis=1)
# context_vector shape after sum == (batch_size, hidden_size)
context_vector = attention_weights * values
context_vector = tf.reduce_sum(context_vector, axis=1)
return context_vector, attention_weights
# 定义解码器
class Decoder(tf.keras.Model):
def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):
super(Decoder, self).__init__()
self.batch_sz = batch_sz
self.dec_units = dec_units
self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)
self.gru = tf.keras.layers.GRU(self.dec_units, return_sequences=True, return_state=True, recurrent_initializer='glorot_uniform')
self.fc = tf.keras.layers.Dense(vocab_size)
# 用于注意力
self.attention = BahdanauAttention(self.dec_units)
def call(self, x, hidden, enc_output):
# enc_output shape == (batch_size, max_length, hidden_size)
context_vector, attention_weights = self.attention(hidden, enc_output)
# x shape after passing through embedding == (batch_size, 1, embedding_dim)
x = self.embedding(x)
# 将上一时间步的隐藏状态和注意力向量拼接起来作为输入传给 GRU
x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)
# 将拼接后的向量传给 GRU
output, state = self.gru(x)
# output shape == (batch_size * 1, hidden_size)
output = tf.reshape(output, (-1, output.shape[2]))
# output shape == (batch_size, vocab)
x = self.fc(output)
return x, state, attention_weights
# 定义损失函数和优化器
optimizer = tf.keras.optimizers.Adam()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')
def loss_function(real, pred):
mask = tf.math.logical_not(tf.math.equal(real, 0))
loss_ = loss_object(real, pred)
mask = tf.cast(mask, dtype=loss_.dtype)
loss_ *= mask
return tf.reduce_mean(loss_)
# 定义训练步骤
@tf.function
def train_step(inp, targ, enc_hidden):
loss = 0
with tf.GradientTape() as tape:
enc_output, enc_hidden = encoder(inp, enc_hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([tokenizer.word_index['<start>']] * BATCH_SIZE, 1)
# teacher forcing - 将目标词作为下一个输入传给解码器
for t in range(1, targ.shape[1]):
# 将编码器的输出和上一时间步的隐藏状态传给解码器
predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)
loss += loss_function(targ[:, t], predictions)
# 使用 teacher forcing
dec_input = tf.expand_dims(targ[:, t], 1)
batch_loss = (loss / int(targ.shape[1]))
variables = encoder.trainable_variables + decoder.trainable_variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
return batch_loss
# 定义预测函数
def evaluate(sentence):
attention_plot = np.zeros((max_length_targ, max_length_inp))
sentence = preprocess_sentence(sentence)
inputs = [tokenizer.word_index[i] for i in sentence.split(' ')]
inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')
inputs = tf.convert_to_tensor(inputs)
result = ''
hidden = [tf.zeros((1, units))]
enc_out, enc_hidden = encoder(inputs, hidden)
dec_hidden = enc_hidden
dec_input = tf.expand_dims([tokenizer.word_index['<start>']], 0)
for t in range(max_length_targ):
predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)
# 存储注意力权重以便后面制图
attention_weights = tf.reshape(attention_weights, (-1, ))
attention_plot[t] = attention_weights.numpy()
predicted_id = tf.argmax(predictions[0]).numpy()
result += tokenizer.index_word[predicted_id] + ' '
if tokenizer.index_word[predicted_id] == '<end>':
return result, sentence, attention_plot
# 将预测的 ID 作为下一个解码器输入的 ID
dec_input = tf.expand_dims([predicted_id], 0)
return result, sentence, attention_plot
```
在上面的代码中,我们使用了 `tf.keras.layers` 中的 `Embedding`、`GRU` 和 `Dense` 层来构建编码器和解码器,使用 `tf.keras.optimizers.Adam` 作为优化器,使用 `tf.keras.losses.SparseCategoricalCrossentropy` 作为损失函数。同时,我们还定义了一个 `BahdanauAttention` 层来实现注意力机制。