[Fix] Upgrade for tensorflow 1.0 with tf_upgrade.py

EricFang2014 · Feb 27, 2017 · b3df4da · b3df4da
1 parent 5eb2183
commit b3df4da
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Showing 2 changed files with 8 additions and 8 deletions.
diff --git a/dense_classifier.py b/dense_classifier.py
@@ -323,8 +323,8 @@ def inference(inputs, is_train=True):
   sparse_labels = tf.reshape(batch_labels, [-1, 1])
   derived_size = tf.shape(batch_labels)[0]
   indices = tf.reshape(tf.range(0, derived_size, 1), [-1, 1])
-  concated = tf.concat(concat_dim=1, values=[indices, sparse_labels])
-  outshape = tf.pack([derived_size, LABEL_SIZE])
+  concated = tf.concat(axis=1, values=[indices, sparse_labels])
+  outshape = tf.stack([derived_size, LABEL_SIZE])
   new_batch_labels = tf.sparse_to_dense(concated, outshape, 1.0, 0.0)
   _, train_auc = tf.contrib.metrics.streaming_auc(train_softmax,
                                                   new_batch_labels)
@@ -343,8 +343,8 @@ def inference(inputs, is_train=True):
   sparse_labels = tf.reshape(validate_batch_labels, [-1, 1])
   derived_size = tf.shape(validate_batch_labels)[0]
   indices = tf.reshape(tf.range(0, derived_size, 1), [-1, 1])
-  concated = tf.concat(concat_dim=1, values=[indices, sparse_labels])
-  outshape = tf.pack([derived_size, LABEL_SIZE])
+  concated = tf.concat(axis=1, values=[indices, sparse_labels])
+  outshape = tf.stack([derived_size, LABEL_SIZE])
   new_validate_batch_labels = tf.sparse_to_dense(concated, outshape, 1.0, 0.0)
   _, validate_auc = tf.contrib.metrics.streaming_auc(validate_softmax,
                                                      new_validate_batch_labels)

diff --git a/sparse_classifier.py b/sparse_classifier.py
@@ -281,8 +281,8 @@ def inference(sparse_ids, sparse_values, is_train=True):
   sparse_labels = tf.reshape(batch_labels, [-1, 1])
   derived_size = tf.shape(batch_labels)[0]
   indices = tf.reshape(tf.range(0, derived_size, 1), [-1, 1])
-  concated = tf.concat(concat_dim=1, values=[indices, sparse_labels])
-  outshape = tf.pack([derived_size, LABEL_SIZE])
+  concated = tf.concat(axis=1, values=[indices, sparse_labels])
+  outshape = tf.stack([derived_size, LABEL_SIZE])
   new_train_batch_labels = tf.sparse_to_dense(concated, outshape, 1.0, 0.0)
   _, train_auc = tf.contrib.metrics.streaming_auc(train_softmax,
                                                   new_train_batch_labels)
@@ -302,8 +302,8 @@ def inference(sparse_ids, sparse_values, is_train=True):
   sparse_labels = tf.reshape(validate_batch_labels, [-1, 1])
   derived_size = tf.shape(validate_batch_labels)[0]
   indices = tf.reshape(tf.range(0, derived_size, 1), [-1, 1])
-  concated = tf.concat(concat_dim=1, values=[indices, sparse_labels])
-  outshape = tf.pack([derived_size, LABEL_SIZE])
+  concated = tf.concat(axis=1, values=[indices, sparse_labels])
+  outshape = tf.stack([derived_size, LABEL_SIZE])
   new_validate_batch_labels = tf.sparse_to_dense(concated, outshape, 1.0, 0.0)
   _, validate_auc = tf.contrib.metrics.streaming_auc(validate_softmax,
                                                      new_validate_batch_labels)