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Author: xiaohu2015

examples/cnn_setence_classification/data_helpers.py

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+import numpy as np
+import re
+import itertools
+from collections import Counter
+
+
+def clean_str(string):
+    """
+    Tokenization/string cleaning for all datasets except for SST.
+    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py
+    """
+    string = re.sub(r"[^A-Za-z0-9(),!?\'\`]", " ", string)
+    string = re.sub(r"\'s", " \'s", string)
+    string = re.sub(r"\'ve", " \'ve", string)
+    string = re.sub(r"n\'t", " n\'t", string)
+    string = re.sub(r"\'re", " \'re", string)
+    string = re.sub(r"\'d", " \'d", string)
+    string = re.sub(r"\'ll", " \'ll", string)
+    string = re.sub(r",", " , ", string)
+    string = re.sub(r"!", " ! ", string)
+    string = re.sub(r"\(", " \( ", string)
+    string = re.sub(r"\)", " \) ", string)
+    string = re.sub(r"\?", " \? ", string)
+    string = re.sub(r"\s{2,}", " ", string)
+    return string.strip().lower()
+
+
+def load_data_and_labels(positive_data_file, negative_data_file):
+    """
+    Loads MR polarity data from files, splits the data into words and generates labels.
+    Returns split sentences and labels.
+    """
+    # Load data from files
+    positive_examples = list(open(positive_data_file, "r", encoding="utf-8").readlines())
+    positive_examples = [s.strip() for s in positive_examples]
+    negative_examples = list(open(negative_data_file, "r", encoding="utf-8").readlines())
+    negative_examples = [s.strip() for s in negative_examples]
+    # Split by words
+    x_text = positive_examples + negative_examples
+    x_text = [clean_str(sent) for sent in x_text]
+    # Generate labels
+    positive_labels = [[0, 1] for _ in positive_examples]
+    negative_labels = [[1, 0] for _ in negative_examples]
+    y = np.concatenate([positive_labels, negative_labels], 0)
+    return [x_text, y]
+
+
+def batch_iter(data, batch_size, num_epochs, shuffle=True):
+    """
+    Generates a batch iterator for a dataset.
+    """
+    data = np.array(data)
+    data_size = len(data)
+    num_batches_per_epoch = int((len(data)-1)/batch_size) + 1
+    for epoch in range(num_epochs):
+        # Shuffle the data at each epoch
+        if shuffle:
+            shuffle_indices = np.random.permutation(np.arange(data_size))
+            shuffled_data = data[shuffle_indices]
+        else:
+            shuffled_data = data
+        for batch_num in range(num_batches_per_epoch):
+            start_index = batch_num * batch_size
+            end_index = min((batch_num + 1) * batch_size, data_size)
+            yield shuffled_data[start_index:end_index]

examples/cnn_setence_classification/test_cnn.py

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+"""
+Test the TextRNN class 
+2016/12/22
+"""
+import os
+import sys
+import numpy as np
+import tensorflow as tf
+from sklearn.model_selection import train_test_split
+from tensorflow.contrib import learn
+
+from data_helpers import load_data_and_labels, batch_iter
+from text_cnn import TextCNN
+
+
+# Load original data
+path = sys.path[0]
+pos_filename = path + "/data/rt-polarity.pos"
+neg_filename = path + "/data/rt-polarity.neg"
+
+X_data, y_data = load_data_and_labels(pos_filename, neg_filename)
+max_document_length = max([len(sen.split(" ")) for sen in X_data])
+print("Max_document_length:,", max_document_length)
+# Create the vacabulary
+vocab_processor = learn.preprocessing.VocabularyProcessor(max_document_length)
+# The idx data
+x = np.array(list(vocab_processor.fit_transform(X_data)), dtype=np.float32)
+y = np.array(y_data, dtype=np.int32)
+vocabulary_size = len(vocab_processor.vocabulary_)
+print("The size of vocabulary:", vocabulary_size)
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=1111)
+print("X_train shape {0}, y_train shape {1}".format(X_train.shape, y_train.shape))
+print("X_test shape {0}, y_test shape {1}".format(X_test.shape, y_test.shape))
+
+# The parameters of RNN
+seq_len = X_train.shape[1]
+vocab_size = vocabulary_size
+embedding_size = 128
+filter_sizes = [2, 3, 4]
+num_filters = 128
+num_classes = y_train.shape[1]
+l2_reg_lambda = 0.0
+
+# Construct RNN model
+text_rnn_model = TextCNN(seq_len=seq_len, vocab_size=vocab_size, embedding_size=embedding_size, filter_sizes=
+                        filter_sizes, num_filters=num_filters, num_classes=num_classes)
+loss = text_rnn_model.loss
+train_op = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)
+accuracy = text_rnn_model.accuracy
+# The parameters for training
+batch_size = 64
+training_epochs = 10
+dispaly_every = 1
+dropout_keep_prob = 0.5
+
+batch_num = int(X_train.shape[0]/batch_size)
+
+sess = tf.Session()
+sess.run(tf.global_variables_initializer())
+print("Starting training...")
+for epoch in range(training_epochs):
+    avg_cost = 0
+    for batch in range(batch_num):
+        _, cost = sess.run([train_op, loss], feed_dict={text_rnn_model.x: X_train[batch*batch_size:(batch+1)*batch_size],
+                                    text_rnn_model.y: y_train[batch*batch_size:(batch+1)*batch_size],
+                                    text_rnn_model.dropout_keep_prob:dropout_keep_prob})
+        avg_cost += cost
+    if epoch % dispaly_every == 0:
+        cost, acc = sess.run([loss, accuracy], feed_dict={text_rnn_model.x: X_test,
+                                    text_rnn_model.y: y_test,
+                                    text_rnn_model.dropout_keep_prob: 1.0})
+        print("\nEpoch {0} : loss {1}, accuracy {2}".format(epoch, cost, acc))
+