def train_gru():
logging.info('GRU original ...')
num_epochs, num_steps, batch_size, lr, clipping_theta = 160, 35, 32, 1e2, 1e-2
pred_period, pred_len, prefixes = 40, 50, ['分开', '不分开']
train_and_predict_rnn(gru, get_params, init_gru_state, num_hiddens,
vocab_size, device, corpus_indices, idx_to_char,
char_to_idx, False, num_epochs, num_steps, lr,
clipping_theta, batch_size, pred_period, pred_len,
prefixes)
logging.info('GRU with PyTorch ...')
lr = 1e-2 # 注意调整学习率
gru_layer = nn.GRU(input_size=vocab_size, hidden_size=num_hiddens)
model = RNNModel(gru_layer, vocab_size).to(device)
train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes)
def train():
num_hiddens = 256
num_epochs, num_steps, batch_size, lr, clipping_theta = 160, 35, 32, 1e-2, 1e-2
pred_period, pred_len, prefixes = 40, 50, ['分开', '不分开']
logging.info('深度循环神经网络...')
logging.info('2 层深度循环神经网络...')
gru_layer = nn.LSTM(input_size=vocab_size,
hidden_size=num_hiddens,
num_layers=2)
model = RNNModel(gru_layer, vocab_size).to(device)
train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes)
logging.info('6 层深度循环神经网络...')
gru_layer = nn.LSTM(input_size=vocab_size,
hidden_size=num_hiddens,
num_layers=6)
model = RNNModel(gru_layer, vocab_size).to(device)
train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes)
logging.info('双向循环神经网络...')
gru_layer = nn.GRU(input_size=vocab_size,
hidden_size=num_hiddens,
bidirectional=True)
model = RNNModel(gru_layer, vocab_size).to(device)
train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes)
import time
import math
import numpy as np
import torch
from torch import nn, optim
import torch.nn.functional as F
import sys
sys.path.append("..")
import utils as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
(corpus_indices, char_to_idx, idx_to_char, vocab_size) = d2l.load_data_jay_lyrics()
num_inputs, num_hiddens, num_outputs = vocab_size, 256, vocab_size
print('will use', device)
num_epochs, num_steps, batch_size, lr, clipping_theta = 160, 35, 32, 1e2, 1e-2
pred_period, pred_len, prefixes = 40, 50, ['分开', '不分开']
lr = 1e-2 # 注意调整学习率
gru_layer = nn.GRU(input_size=vocab_size, hidden_size=num_hiddens)
model = d2l.RNNModel(gru_layer, vocab_size).to(device)
d2l.train_and_predict_rnn_pytorch(model, num_hiddens, vocab_size, device,
corpus_indices, idx_to_char, char_to_idx,
num_epochs, num_steps, lr, clipping_theta,
batch_size, pred_period, pred_len, prefixes)
