自然语言处理之----RNN(Recurrent Neural Network)
循环神经网络
- 3-1. TextRNN - Predict Next Step
-
Paper Finding Structure In Time
- TextRNN-Tensor.py
'''
code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
sentences = [ "i like dog", "i love coffee", "i hate milk"]
word_list = " ".join(sentences).split()
word_list = list(set(word_list))
word_dict = {w: i for i, w in enumerate(word_list)}
number_dict = {i: w for i, w in enumerate(word_list)}
n_class = len(word_dict)
# TextRNN Parameter
n_step = 2 # number of cells(= number of Step)
n_hidden = 5 # number of hidden units in one cell
def make_batch(sentences):
input_batch = []
target_batch = []
for sen in sentences:
word = sen.split()
input = [word_dict[n] for n in word[:-1]]
target = word_dict[word[-1]]
input_batch.append(np.eye(n_class)[input])
target_batch.append(np.eye(n_class)[target])
return input_batch, target_batch
# Model
X = tf.placeholder(tf.float32, [None, n_step, n_class]) # [batch_size, n_step, n_class]
Y = tf.placeholder(tf.float32, [None, n_class]) # [batch_size, n_class]
W = tf.Variable(tf.random_normal([n_hidden, n_class]))
b = tf.Variable(tf.random_normal([n_class]))
cell = tf.nn.rnn_cell.BasicRNNCell(n_hidden)
outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
# outputs : [batch_size, n_step, n_hidden]
outputs = tf.transpose(outputs, [1, 0, 2]) # [n_step, batch_size, n_hidden]
outputs = outputs[-1] # [batch_size, n_hidden]
model = tf.matmul(outputs, W) + b # model : [batch_size, n_class]
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
prediction = tf.cast(tf.argmax(model, 1), tf.int32)
# Training
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
input_batch, target_batch = make_batch(sentences)
for epoch in range(5000):
_, loss = sess.run([optimizer, cost], feed_dict={X: input_batch, Y: target_batch})
if (epoch + 1)%1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
input = [sen.split()[:2] for sen in sentences]
predict = sess.run([prediction], feed_dict={X: input_batch})
print([sen.split()[:2] for sen in sentences], '->', [number_dict[n] for n in predict[0]])
- TextRNN-Torch.py
'''
code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
dtype = torch.FloatTensor
sentences = [ "i like dog", "i love coffee", "i hate milk"]
word_list = " ".join(sentences).split()
word_list = list(set(word_list))
word_dict = {w: i for i, w in enumerate(word_list)}
number_dict = {i: w for i, w in enumerate(word_list)}
n_class = len(word_dict)
# TextRNN Parameter
batch_size = len(sentences)
n_step = 2 # number of cells(= number of Step)
n_hidden = 5 # number of hidden units in one cell
def make_batch(sentences):
input_batch = []
target_batch = []
for sen in sentences:
word = sen.split()
input = [word_dict[n] for n in word[:-1]]
target = word_dict[word[-1]]
input_batch.append(np.eye(n_class)[input])
target_batch.append(target)
return input_batch, target_batch
# to Torch.Tensor
input_batch, target_batch = make_batch(sentences)
input_batch = Variable(torch.Tensor(input_batch))
target_batch = Variable(torch.LongTensor(target_batch))
class TextRNN(nn.Module):
def __init__(self):
super(TextRNN, self).__init__()
self.rnn = nn.RNN(input_size=n_class, hidden_size=n_hidden)
self.W = nn.Parameter(torch.randn([n_hidden, n_class]).type(dtype))
self.b = nn.Parameter(torch.randn([n_class]).type(dtype))
def forward(self, hidden, X):
X = X.transpose(0, 1) # X : [n_step, batch_size, n_class]
outputs, hidden = self.rnn(X, hidden)
# outputs : [n_step, batch_size, num_directions(=1) * n_hidden]
# hidden : [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
outputs = outputs[-1] # [batch_size, num_directions(=1) * n_hidden]
model = torch.mm(outputs, self.W) + self.b # model : [batch_size, n_class]
return model
model = TextRNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training
for epoch in range(5000):
optimizer.zero_grad()
# hidden : [num_layers * num_directions, batch, hidden_size]
hidden = Variable(torch.zeros(1, batch_size, n_hidden))
# input_batch : [batch_size, n_step, n_class]
output = model(hidden, input_batch)
# output : [batch_size, n_class], target_batch : [batch_size] (LongTensor, not one-hot)
loss = criterion(output, target_batch)
if (epoch + 1) % 1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
loss.backward()
optimizer.step()
input = [sen.split()[:2] for sen in sentences]
# Predict
hidden = Variable(torch.zeros(1, batch_size, n_hidden))
predict = model(hidden, input_batch).data.max(1, keepdim=True)[1]
print([sen.split()[:2] for sen in sentences], '->', [number_dict[n.item()] for n in predict.squeeze()])
- 3-2. TextLSTM - Autocomplete
-
Paper Long Short-Term Memory
TextLSTM-Tensor.py
'''
code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
char_arr = [c for c in 'abcdefghijklmnopqrstuvwxyz']
word_dict = {n: i for i, n in enumerate(char_arr)}
number_dict = {i: w for i, w in enumerate(char_arr)}
n_class = len(word_dict) # number of class(=number of vocab)
seq_data = ['make', 'need', 'coal', 'word', 'love', 'hate', 'live', 'home', 'hash', 'star']
# TextLSTM Parameters
n_step = 3
n_hidden = 128
def make_batch(seq_data):
input_batch, target_batch = [], []
for seq in seq_data:
input = [word_dict[n] for n in seq[:-1]] # 'm', 'a' , 'k' is input
target = word_dict[seq[-1]] # 'e' is target
input_batch.append(np.eye(n_class)[input])
target_batch.append(np.eye(n_class)[target])
return input_batch, target_batch
# Model
X = tf.placeholder(tf.float32, [None, n_step, n_class]) # [batch_size, n_step, n_class]
Y = tf.placeholder(tf.float32, [None, n_class]) # [batch_size, n_class]
W = tf.Variable(tf.random_normal([n_hidden, n_class]))
b = tf.Variable(tf.random_normal([n_class]))
cell = tf.nn.rnn_cell.BasicLSTMCell(n_hidden)
outputs, states = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
# outputs : [batch_size, n_step, n_hidden]
outputs = tf.transpose(outputs, [1, 0, 2]) # [n_step, batch_size, n_hidden]
outputs = outputs[-1] # [batch_size, n_hidden]
model = tf.matmul(outputs, W) + b # model : [batch_size, n_class]
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
prediction = tf.cast(tf.argmax(model, 1), tf.int32)
# Training
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
input_batch, target_batch = make_batch(seq_data)
for epoch in range(1000):
_, loss = sess.run([optimizer, cost], feed_dict={X: input_batch, Y: target_batch})
if (epoch + 1)%100 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
inputs = [sen[:3] for sen in seq_data]
predict = sess.run([prediction], feed_dict={X: input_batch})
print(inputs, '->', [number_dict[n] for n in predict[0]])
TextLSTM-Torch.py
'''
code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
dtype = torch.FloatTensor
char_arr = [c for c in 'abcdefghijklmnopqrstuvwxyz']
word_dict = {n: i for i, n in enumerate(char_arr)}
number_dict = {i: w for i, w in enumerate(char_arr)}
n_class = len(word_dict) # number of class(=number of vocab)
seq_data = ['make', 'need', 'coal', 'word', 'love', 'hate', 'live', 'home', 'hash', 'star']
# TextLSTM Parameters
n_step = 3
n_hidden = 128
def make_batch(seq_data):
input_batch, target_batch = [], []
for seq in seq_data:
input = [word_dict[n] for n in seq[:-1]] # 'm', 'a' , 'k' is input
target = word_dict[seq[-1]] # 'e' is target
input_batch.append(np.eye(n_class)[input])
target_batch.append(target)
return Variable(torch.Tensor(input_batch)), Variable(torch.LongTensor(target_batch))
class TextLSTM(nn.Module):
def __init__(self):
super(TextLSTM, self).__init__()
self.lstm = nn.LSTM(input_size=n_class, hidden_size=n_hidden)
self.W = nn.Parameter(torch.randn([n_hidden, n_class]).type(dtype))
self.b = nn.Parameter(torch.randn([n_class]).type(dtype))
def forward(self, X):
input = X.transpose(0, 1) # X : [n_step, batch_size, n_class]
hidden_state = Variable(torch.zeros(1, len(X), n_hidden)) # [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
cell_state = Variable(torch.zeros(1, len(X), n_hidden)) # [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
outputs, (_, _) = self.lstm(input, (hidden_state, cell_state))
outputs = outputs[-1] # [batch_size, n_hidden]
model = torch.mm(outputs, self.W) + self.b # model : [batch_size, n_class]
return model
input_batch, target_batch = make_batch(seq_data)
model = TextLSTM()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
output = model(input_batch)
# Training
for epoch in range(1000):
optimizer.zero_grad()
output = model(input_batch)
loss = criterion(output, target_batch)
if (epoch + 1) % 100 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
loss.backward()
optimizer.step()
inputs = [sen[:3] for sen in seq_data]
predict = model(input_batch).data.max(1, keepdim=True)[1]
print(inputs, '->', [number_dict[n.item()] for n in predict.squeeze()])
- 3-3. Bi-LSTM - Predict Next Word in Long Sentence
Bi-LSTM-Tensor.py
'''
code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
sentence = (
'Lorem ipsum dolor sit amet consectetur adipisicing elit '
'sed do eiusmod tempor incididunt ut labore et dolore magna '
'aliqua Ut enim ad minim veniam quis nostrud exercitation'
)
word_dict = {w: i for i, w in enumerate(list(set(sentence.split())))}
number_dict = {i: w for i, w in enumerate(list(set(sentence.split())))}
n_class = len(word_dict)
n_step = len(sentence.split())
n_hidden = 5
def make_batch(sentence):
input_batch = []
target_batch = []
words = sentence.split()
for i, word in enumerate(words[:-1]):
input = [word_dict[n] for n in words[:(i + 1)]]
input = input + [0] * (n_step - len(input))
target = word_dict[words[i + 1]]
input_batch.append(np.eye(n_class)[input])
target_batch.append(np.eye(n_class)[target])
return input_batch, target_batch
# Bi-LSTM Model
X = tf.placeholder(tf.float32, [None, n_step, n_class])
Y = tf.placeholder(tf.float32, [None, n_class])
W = tf.Variable(tf.random_normal([n_hidden * 2, n_class]))
b = tf.Variable(tf.random_normal([n_class]))
lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(n_hidden)
lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(n_hidden)
# outputs : [batch_size, len_seq, n_hidden], states : [batch_size, n_hidden]
outputs, _ = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell,lstm_bw_cell, X, dtype=tf.float32)
outputs = tf.concat([outputs[0], outputs[1]], 2) # output[0] : lstm_fw, output[1] : lstm_bw
outputs = tf.transpose(outputs, [1, 0, 2]) # [n_step, batch_size, n_hidden]
outputs = outputs[-1] # [batch_size, n_hidden]
model = tf.matmul(outputs, W) + b
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=model, labels=Y))
optimizer = tf.train.AdamOptimizer(0.001).minimize(cost)
prediction = tf.cast(tf.argmax(model, 1), tf.int32)
# Training
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
input_batch, target_batch = make_batch(sentence)
for epoch in range(10000):
_, loss = sess.run([optimizer, cost], feed_dict={X: input_batch, Y: target_batch})
if (epoch + 1)%1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
predict = sess.run([prediction], feed_dict={X: input_batch})
print(sentence)
print([number_dict[n] for n in [pre for pre in predict[0]]])
Bi-LSTM-Torch.py
'''
code by Tae Hwan Jung(Jeff Jung) @graykode
'''
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import torch.nn.functional as F
dtype = torch.FloatTensor
sentence = (
'Lorem ipsum dolor sit amet consectetur adipisicing elit '
'sed do eiusmod tempor incididunt ut labore et dolore magna '
'aliqua Ut enim ad minim veniam quis nostrud exercitation'
)
word_dict = {w: i for i, w in enumerate(list(set(sentence.split())))}
number_dict = {i: w for i, w in enumerate(list(set(sentence.split())))}
n_class = len(word_dict)
max_len = len(sentence.split())
n_hidden = 5
def make_batch(sentence):
input_batch = []
target_batch = []
words = sentence.split()
for i, word in enumerate(words[:-1]):
input = [word_dict[n] for n in words[:(i + 1)]]
input = input + [0] * (max_len - len(input))
target = word_dict[words[i + 1]]
input_batch.append(np.eye(n_class)[input])
target_batch.append(target)
return Variable(torch.Tensor(input_batch)), Variable(torch.LongTensor(target_batch))
class BiLSTM(nn.Module):
def __init__(self):
super(BiLSTM, self).__init__()
self.lstm = nn.LSTM(input_size=n_class, hidden_size=n_hidden, bidirectional=True)
self.W = nn.Parameter(torch.randn([n_hidden * 2, n_class]).type(dtype))
self.b = nn.Parameter(torch.randn([n_class]).type(dtype))
def forward(self, X):
input = X.transpose(0, 1) # input : [n_step, batch_size, n_class]
hidden_state = Variable(torch.zeros(1*2, len(X), n_hidden)) # [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
cell_state = Variable(torch.zeros(1*2, len(X), n_hidden)) # [num_layers(=1) * num_directions(=1), batch_size, n_hidden]
outputs, (_, _) = self.lstm(input, (hidden_state, cell_state))
outputs = outputs[-1] # [batch_size, n_hidden]
model = torch.mm(outputs, self.W) + self.b # model : [batch_size, n_class]
return model
input_batch, target_batch = make_batch(sentence)
model = BiLSTM()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training
for epoch in range(10000):
optimizer.zero_grad()
output = model(input_batch)
loss = criterion(output, target_batch)
if (epoch + 1) % 1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))
loss.backward()
optimizer.step()
predict = model(input_batch).data.max(1, keepdim=True)[1]
print(sentence)
print([number_dict[n.item()] for n in predict.squeeze()])