compatible with tensorflow 1.0

text_cnn.py

@@ -21,10 +21,10 @@ def __init__(
 
         # Embedding layer
         with tf.device('/cpu:0'), tf.name_scope("embedding"):
-            W = tf.Variable(
+            self.W = tf.Variable(
                 tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
                 name="W")
-            self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x)
+            self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
             self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
 
         # Create a convolution + maxpool layer for each filter size
@@ -54,7 +54,7 @@ def __init__(
 
         # Combine all the pooled features
         num_filters_total = num_filters * len(filter_sizes)
-        self.h_pool = tf.concat(3, pooled_outputs)
+        self.h_pool = tf.concat(pooled_outputs, 3)
         self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
 
         # Add dropout
@@ -75,7 +75,7 @@ def __init__(
 
         # CalculateMean cross-entropy loss
         with tf.name_scope("loss"):
-            losses = tf.nn.softmax_cross_entropy_with_logits(self.scores, self.input_y)
+            losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
             self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
 
         # Accuracy