RNN适用场景
循环神经网络(Recurrent Neural Network)适合处理和预测时序数据
RNN的特点
RNN的隐藏层之间的节点是有连接的，他的输入是输入层的输出向量.extend(上一时刻隐藏层的状态向量)。
demo：单层全连接网络作为循环体的RNN
输入层维度：x
隐藏层维度：h
每个循环体的输入大小为：x+h
每个循环体的输出大小为：h
循环体的输出有两个用途：
- 下一时刻循环体的输入的一部分
- 经过另一个全连接神经网络，得到当前时刻的输出
序列长度
理论上RNN支持任意序列长度，但过长会导致优化时梯度消散的问题，因此一般都设定一个最大长度。超过该长度是，进行截断。
论文原文：On the difficulty of training Recurrent Neural Networks
长短时记忆网络（LSTM结构）
论文原文：Long Short-term memory
循环体：拥有输入门、遗忘门、输出门的特殊网络结构
遗忘门：决定忘记当前输入、上一时刻状态和上一时刻输出中的哪一部分
输入门：决定当前输入、上一时刻状态、上一时刻输出中，哪些部分将进入当前时刻的状态
RNN的变种
- 双向RNN
- 深层RNN
RNN的dropout
不同层的循环体之间使用dropout，同一层循环体之间不使用dropout
demo
import os
import re
import io
import requests
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from zipfile import ZipFile
from tensorflow.python.framework import ops
ops.reset_default_graph()
1. start a graph session and set RNN parameters
sess = tf.Session()
epochs = 20 # run 20 epochs. An epoch equals to all batches of this training set.
batch_size = 250
max_sequence_length = 25
rnn_size = 10 # The RNN will be of size 10 units.
embedding_size = 50 # every word will be embedded in a trainable vector of size 50
min_word_frequency = 10 # We will only consider words that appear at least 10 times in our vocabulary
learning_rate = 0.0005
dropout_keep_prob = tf.placeholder(tf.float32)
2. Download or open data
Check if it was already downloaded and, if so,read in the file.
Otherwise, download the data and save it
# Download or open data
data_dir = 'data'
data_file = 'text_data.txt'
if not os.path.exists(data_dir):
os.makedirs(data_dir)
if not os.path.isfile(os.path.join(data_dir, data_file)):
zip_url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00228/smsspamcollection.zip'
r = requests.get(zip_url)
z = ZipFile(io.BytesIO(r.content))
file = z.read('SMSSpamCollection')
# Format Data
text_data = file.decode()
text_data = text_data.encode('ascii',errors='ignore')
text_data = text_data.decode().split('\n')
# Save data to text file
with open(os.path.join(data_dir, data_file), 'w') as file_conn:
for text in text_data:
file_conn.write("{}\n".format(text)) # append "\n" to each row. Format method is from re lib.
else:
# Open data from text file
text_data = []
with open(os.path.join(data_dir, data_file), 'r') as file_conn:
for row in file_conn:
text_data.append(row)
text_data = text_data[:-1]
text_data = [x.split('\t') for x in text_data if len(x)>=1]
[text_data_target, text_data_train] = [list(x) for x in zip(*text_data)]
3. Create a text cleaning function then clean the data
def clean_text(text_string):
text_string = re.sub(r'([^\s\w]|_|[0-9])+', '', text_string) # \w匹配包括下划线的任何单词字符 [^\s\w]匹配空格开头字符串
text_string = " ".join(text_string.split())
text_string = text_string.lower()
return(text_string)
# Clean texts
text_data_train = [clean_text(x) for x in text_data_train]
4. Change texts into numeric vectors
This will convert a text to an appropriate list of indices
x_shuffled = text_processed[shuffled_ix]
y_shuffled = text_data_target[shuffled_ix]
# Split train/test set
ix_cutoff = int(len(y_shuffled)*0.80)
x_train, x_test = x_shuffled[:ix_cutoff], x_shuffled[ix_cutoff:]
y_train, y_test = y_shuffled[:ix_cutoff], y_shuffled[ix_cutoff:]
vocab_size = len(vocab_processor.vocabulary_)
print("Vocabulary Size: {:d}".format(vocab_size))
print("80-20 Train Test split: {:d} -- {:d}".format(len(y_train), len(y_test)))
# Create placeholders
x_data = tf.placeholder(tf.int32, [None, max_sequence_length])
y_output = tf.placeholder(tf.int32, [None])
# Create embedding
embedding_mat = tf.Variable(tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0))
embedding_output = tf.nn.embedding_lookup(embedding_mat, x_data)
#embedding_output_expanded = tf.expand_dims(embedding_output, -1)
# Define the RNN cell
#tensorflow change >= 1.0, rnn is put into tensorflow.contrib directory. Prior version not test.
if tf.__version__[0]>='1':
cell=tf.contrib.rnn.BasicRNNCell(num_units = rnn_size)
else:
cell = tf.nn.rnn_cell.BasicRNNCell(num_units = rnn_size)
output, state = tf.nn.dynamic_rnn(cell, embedding_output, dtype=tf.float32)
output = tf.nn.dropout(output, dropout_keep_prob)
# Get output of RNN sequence
output = tf.transpose(output, [1, 0, 2])
last = tf.gather(output, int(output.get_shape()[0]) - 1)
weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1))
bias = tf.Variable(tf.constant(0.1, shape=[2]))
logits_out = tf.matmul(last, weight) + bias
# Loss function
losses = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits_out, labels=y_output) # logits=float32, labels=int32
loss = tf.reduce_mean(losses)
accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits_out, 1), tf.cast(y_output, tf.int64)), tf.float32))
optimizer = tf.train.RMSPropOptimizer(learning_rate)
train_step = optimizer.minimize(loss)
init = tf.global_variables_initializer()
sess.run(init)
train_loss = []
test_loss = []
train_accuracy = []
test_accuracy = []
# Start training
for epoch in range(epochs):
# Shuffle training data
shuffled_ix = np.random.permutation(np.arange(len(x_train)))
x_train = x_train[shuffled_ix]
y_train = y_train[shuffled_ix]
num_batches = int(len(x_train)/batch_size) + 1
# TO DO CALCULATE GENERATIONS ExACTLY
for i in range(num_batches):
# Select train data
min_ix = i * batch_size
max_ix = np.min([len(x_train), ((i+1) * batch_size)])
x_train_batch = x_train[min_ix:max_ix]
y_train_batch = y_train[min_ix:max_ix]
# Run train step
train_dict = {x_data: x_train_batch, y_output: y_train_batch, dropout_keep_prob:0.5}
sess.run(train_step, feed_dict=train_dict)
# Run loss and accuracy for training
temp_train_loss, temp_train_acc = sess.run([loss, accuracy], feed_dict=train_dict)
train_loss.append(temp_train_loss)
train_accuracy.append(temp_train_acc)
# Run Eval Step
test_dict = {x_data: x_test, y_output: y_test, dropout_keep_prob:1.0}
temp_test_loss, temp_test_acc = sess.run([loss, accuracy], feed_dict=test_dict)
test_loss.append(temp_test_loss)
test_accuracy.append(temp_test_acc)
print('Epoch: {}, Test Loss: {:.2}, Test Acc: {:.2}'.format(epoch+1, temp_test_loss, temp_test_acc))
# Plot loss over time
epoch_seq = np.arange(1, epochs+1)
plt.plot(epoch_seq, train_loss, 'k--', label='Train Set')
plt.plot(epoch_seq, test_loss, 'r-', label='Test Set')
plt.title('Softmax Loss')
plt.xlabel('Epochs')
plt.ylabel('Softmax Loss')
plt.legend(loc='upper left')
plt.show()
# Plot accuracy over time
plt.plot(epoch_seq, train_accuracy, 'k--', label='Train Set')
plt.plot(epoch_seq, test_accuracy, 'r-', label='Test Set')
plt.title('Test Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy')
plt.legend(loc='upper left')
plt.show()
Vocabulary Size: 1124
80-20 Train Test split: 4459 -- 1115
C:\Users\Diane\Anaconda3\lib\site-packages\tensorflow\python\ops\gradients_impl.py???? UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.
"Converting sparse IndexedSlices to a dense Tensor of unknown shape. "
Epoch: 1, Test Loss: 0.53, Test Acc: 0.84
Epoch: 2, Test Loss: 0.52, Test Acc: 0.84
Epoch: 3, Test Loss: 0.49, Test Acc: 0.84
Epoch: 4, Test Loss: 0.46, Test Acc: 0.84
Epoch: 5, Test Loss: 0.43, Test Acc: 0.84
Epoch: 6, Test Loss: 0.4, Test Acc: 0.85
Epoch: 7, Test Loss: 0.38, Test Acc: 0.85
Epoch: 8, Test Loss: 0.37, Test Acc: 0.85
Epoch: 9, Test Loss: 0.36, Test Acc: 0.85
Epoch: 10, Test Loss: 0.36, Test Acc: 0.86
Epoch: 11, Test Loss: 0.35, Test Acc: 0.86
Epoch: 12, Test Loss: 0.35, Test Acc: 0.86
Epoch: 13, Test Loss: 0.34, Test Acc: 0.87
Epoch: 14, Test Loss: 0.34, Test Acc: 0.87
Epoch: 15, Test Loss: 0.33, Test Acc: 0.87
Epoch: 16, Test Loss: 0.33, Test Acc: 0.87
Epoch: 17, Test Loss: 0.32, Test Acc: 0.87
Epoch: 18, Test Loss: 0.32, Test Acc: 0.88
Epoch: 19, Test Loss: 0.31, Test Acc: 0.88
Epoch: 20, Test Loss: 0.3, Test Acc: 0.88
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