[Tensorflow] RNN - 01. Spam Prediction with BasicRNNCell
Ref: http://blog.csdn.net/mebiuw/article/details/60780813
Ref: https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767 [Nice]
Ref: https://medium.com/@erikhallstrm/tensorflow-rnn-api-2bb31821b185 [Nice]
Code Analysis
Download and pre-preprocess
# Implementing an RNN in Tensorflow #---------------------------------- # # We implement an RNN in Tensorflow to predict spam/ham from texts # # Jeffrey: the data process for nlp here is advanced. import os import re import io import numpy as np import matplotlib.pyplot as plt import tensorflow as tf from zipfile import ZipFile import urllib.request from tensorflow.python.framework import ops ops.reset_default_graph() # Start a graph sess = tf.Session() # Set RNN parameters epochs = 30 batch_size = 250 max_sequence_length = 40 rnn_size = 10 embedding_size = 50 min_word_frequency = 10 learning_rate = 0.0005 dropout_keep_prob = tf.placeholder(tf.float32) # Download or open data data_dir = 'temp' 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' page = urllib.request.urlopen(zip_url) html_content = page.read() z = ZipFile(io.BytesIO(html_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)) 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)] # Create a text cleaning function def clean_text(text_string): text_string = re.sub(r'([^\s\w]|_|[0-9])+', '', text_string) 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] #Jeffrey #print("[x]:", text_data_train[:10][:10]) #print("[y]:", text_data_target[:10])
Stage result:
print("[x]:", text_data_train[:10])
print("[y]:", text_data_target[:10])
[x]: ['go until jurong point crazy available only in bugis n great world la e buffet cine there got amore wat', 'ok lar joking wif u oni', 'free entry in a wkly comp to win fa cup final tkts st may text fa to to receive entry questionstd txt ratetcs apply overs', 'u dun say so early hor u c already then say', 'nah i dont think he goes to usf he lives around here though', 'freemsg hey there darling its been weeks now and no word back id like some fun you up for it still tb ok xxx std chgs to send to rcv', 'even my brother is not like to speak with me they treat me like aids patent', 'as per your request melle melle oru minnaminunginte nurungu vettam has been set as your callertune for all callers press to copy your friends callertune', 'winner as a valued network customer you have been selected to receivea prize reward to claim call claim code kl valid hours only', 'had your mobile months or more u r entitled to update to the latest colour mobiles with camera for free call the mobile update co free on']
[y]: [1 1 0 1 1 0 1 1 0 0]
Change texts into numeric vectors
# Change texts into numeric vectors
vocab_processor = tf.contrib.learn.preprocessing.VocabularyProcessor(max_sequence_length, min_frequency=min_word_frequency)
text_processed = np.array(list(vocab_processor.fit_transform(text_data_train)))
# Shuffle and split data
text_processed = np.array(text_processed)
text_data_target = np.array([1 if x=='ham' else 0 for x in text_data_target])
Stage result: one-hotting encoding
#Jeffrey
#print("[text_processed]:", text_processed.shape)
#print("[text_data_target]:", text_data_target.shape)
##[text_processed]: (5574, 40)
##[text_data_target]: (5574,)
#print("[text_processed]:", text_processed)
#print("[text_data_target]:", text_data_target)
[text_processed]:
[[ 44 455 0 ..., 0 0 0]
[ 47 315 0 ..., 0 0 0]
[ 46 465 9 ..., 0 0 0]
...,
[ 0 59 9 ..., 0 0 0]
[ 5 493 108 ..., 0 0 0]
[ 0 40 474 ..., 0 0 0]]
[text_data_target]:
[1 1 0 ..., 1 1 1]
Term statistics
shuffled_ix = np.random.permutation(np.arange(len(text_data_target)))
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:]
print(vocab_processor.vocabulary_)
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)))
[text_processed]: (5574, 40)
[text_data_target]: (5574,)
Build Graph
############################################################################### # 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) # Here, this x_data is ids! <---- [termIdx1, termIdx2, ...]
#embedding_output_expanded = tf.expand_dims(embedding_output, -1)
Create our embedding matrix and embedding lookup operation for the x-input data:embedding_mat.
# Define the RNN cell
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) # parameters are variables, waiting for constant later.
# Get output of RNN sequence
output = tf.transpose(output, [1, 0, 2])
last = tf.gather(output, int(output.get_shape()[0]) - 1)
API: rnn_cell
from Tensorflow RNN源代码解析笔记1：RNNCell的基本实现
在Tensorflow中，定义了一个RNNCell的抽象类，具体的所有不同类型的RNN Cell都是基于这个类的.
在Tensorflow中，将会基于整个RNNCell实现一系列常用的RNNCell，比如LSTM和GRU，并且将会支持包含Dropout等在内的特性，同时也支持构建多层的RNN网络。
class BasicRNNCell(RNNCell):
"""The most basic RNN cell.
Args:
num_units: int, The number of units in the RNN cell.
activation: Nonlinearity to use. Default: `tanh`.
reuse: (optional) Python boolean describing whether to reuse variables
in an existing scope. If not `True`, and the existing scope already has
the given variables, an error is raised.
"""
def __init__(self, num_units, activation=None, reuse=None):
super(BasicRNNCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def call(self, inputs, state):
"""Most basic RNN: output = new_state = act(W * input + U * state + B)."""
output = self._activation(_linear([inputs, state], self._num_units, True))
return output, output
学习参数：
From: YJango的循环神经网络——介绍
所有时刻的权重矩阵都是共享的。这是递归网络相对于前馈网络而言最为突出的优势。
递归神经网络是在时间结构上存在共享特性的神经网络变体。时间结构共享是递归网络的核心中的核心。
h_state:
# Variables
weight = tf.Variable(tf.truncated_normal([rnn_size, 2], stddev=0.1))
bias = tf.Variable(tf.constant(0.1, shape=[2]))
logits_out = tf.nn.softmax(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.initialize_all_variables()
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)))
# Sort x_train and y_train based on shuffled_ix
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 each batch.
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()
