Merge pull request xiaohu2015#8 from zhangbo2008/master

Add files via upload

Author: xiaohu2015

examples/cnn_setence_classification/text_cnn.py

@@ -81,7 +81,7 @@ def __init__(self, seq_len, vocab_size, embedding_size, filter_sizes, num_filter
                 pooled_outputs.append(pool_output)   # [None, 1, 1, num_filters]
         # Combine all pooled features
         num_filters_total = num_filters * len(filter_sizes)
-        self.h_pool = tf.concat(3, pooled_outputs)  # [None, 1, 1, num_filters_total]
+        self.h_pool = tf.concat( pooled_outputs,3)  # [None, 1, 1, num_filters_total]
         self.h_pool_flat = tf.reshape(self.h_pool, shape=[-1, num_filters_total]) # [None, num_filters_total]
 
         # The dropout layer
@@ -100,7 +100,7 @@ def __init__(self, seq_len, vocab_size, embedding_size, filter_sizes, num_filter
 
         # The loss
         with tf.name_scope("loss"):
-            losses = tf.nn.softmax_cross_entropy_with_logits(self.scores, self.y)
+            losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.y)
             self.loss = tf.reduce_mean(losses) + L2_loss * l2_reg_lambda
 
         # Accuracy

examples/cnn_setence_classification/train_cnn.py

@@ -1,74 +1,74 @@
-"""
-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))
-
+"""
+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
+import pudb;pu.db
+
+# 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))
+