Clear softmax_v2 warning for image_retraining and speech_commands tutorials.

`tf.nn.softmax_cross_entropy_with_logits` and `tf.losses.softmax_cross_entropy` both throw the warning.

Almost everywhere it's used can simply be replaced by `tf.losses.sparse_softmax_cross_entropy`

PiperOrigin-RevId: 178253804

4 files changed, 17 insertions, 30 deletions

diff --git a/tensorflow/examples/image_retraining/retrain.py b/tensorflow/examples/image_retraining/retrain.py
index ebddfb20f4..ec22684eaf 100644
--- a/tensorflow/examples/image_retraining/retrain.py
+++ b/tensorflow/examples/image_retraining/retrain.py
@@ -539,10 +539,8 @@ def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
           sess, image_lists, label_name, image_index, image_dir, category,
           bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
           resized_input_tensor, bottleneck_tensor, architecture)
-      ground_truth = np.zeros(class_count, dtype=np.float32)
-      ground_truth[label_index] = 1.0
       bottlenecks.append(bottleneck)
-      ground_truths.append(ground_truth)
+      ground_truths.append(label_index)
       filenames.append(image_name)
   else:
     # Retrieve all bottlenecks.
@@ -555,10 +553,8 @@ def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
             sess, image_lists, label_name, image_index, image_dir, category,
             bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
             resized_input_tensor, bottleneck_tensor, architecture)
-        ground_truth = np.zeros(class_count, dtype=np.float32)
-        ground_truth[label_index] = 1.0
         bottlenecks.append(bottleneck)
-        ground_truths.append(ground_truth)
+        ground_truths.append(label_index)
         filenames.append(image_name)
   return bottlenecks, ground_truths, filenames
 
@@ -610,10 +606,8 @@ def get_random_distorted_bottlenecks(
     bottleneck_values = sess.run(bottleneck_tensor,
                                  {resized_input_tensor: distorted_image_data})
     bottleneck_values = np.squeeze(bottleneck_values)
-    ground_truth = np.zeros(class_count, dtype=np.float32)
-    ground_truth[label_index] = 1.0
     bottlenecks.append(bottleneck_values)
-    ground_truths.append(ground_truth)
+    ground_truths.append(label_index)
   return bottlenecks, ground_truths
 
 
@@ -774,9 +768,8 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
         shape=[None, bottleneck_tensor_size],
         name='BottleneckInputPlaceholder')
 
-    ground_truth_input = tf.placeholder(tf.float32,
-                                        [None, class_count],
-                                        name='GroundTruthInput')
+    ground_truth_input = tf.placeholder(
+        tf.int64, [None], name='GroundTruthInput')
 
   # Organizing the following ops as `final_training_ops` so they're easier
   # to see in TensorBoard
@@ -823,10 +816,8 @@ def add_final_training_ops(class_count, final_tensor_name, bottleneck_tensor,
   tf.summary.histogram('activations', final_tensor)
 
   with tf.name_scope('cross_entropy'):
-    cross_entropy = tf.nn.softmax_cross_entropy_with_logits(
+    cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
         labels=ground_truth_input, logits=logits)
-    with tf.name_scope('total'):
-      cross_entropy_mean = tf.reduce_mean(cross_entropy)
 
   tf.summary.scalar('cross_entropy', cross_entropy_mean)
 
@@ -852,8 +843,7 @@ def add_evaluation_step(result_tensor, ground_truth_tensor):
   with tf.name_scope('accuracy'):
     with tf.name_scope('correct_prediction'):
       prediction = tf.argmax(result_tensor, 1)
-      correct_prediction = tf.equal(
-          prediction, tf.argmax(ground_truth_tensor, 1))
+      correct_prediction = tf.equal(prediction, ground_truth_tensor)
     with tf.name_scope('accuracy'):
       evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   tf.summary.scalar('accuracy', evaluation_step)
@@ -1178,7 +1168,7 @@ def main(_):
     if FLAGS.print_misclassified_test_images:
       tf.logging.info('=== MISCLASSIFIED TEST IMAGES ===')
       for i, test_filename in enumerate(test_filenames):
-        if predictions[i] != test_ground_truth[i].argmax():
+        if predictions[i] != test_ground_truth[i]:
           tf.logging.info('%70s  %s' %
                           (test_filename,
                            list(image_lists.keys())[predictions[i]]))
diff --git a/tensorflow/examples/image_retraining/retrain_test.py b/tensorflow/examples/image_retraining/retrain_test.py
index 2de4c4ec99..8b8dd45fd7 100644
--- a/tensorflow/examples/image_retraining/retrain_test.py
+++ b/tensorflow/examples/image_retraining/retrain_test.py
@@ -87,7 +87,7 @@ class ImageRetrainingTest(test_util.TensorFlowTestCase):
   def testAddEvaluationStep(self):
     with tf.Graph().as_default():
       final = tf.placeholder(tf.float32, [1], name='final')
-      gt = tf.placeholder(tf.float32, [1], name='gt')
+      gt = tf.placeholder(tf.int64, [1], name='gt')
       self.assertIsNotNone(retrain.add_evaluation_step(final, gt))
 
   def testAddJpegDecoding(self):
diff --git a/tensorflow/examples/speech_commands/input_data.py b/tensorflow/examples/speech_commands/input_data.py
index 751652b330..e7db9cddf0 100644
--- a/tensorflow/examples/speech_commands/input_data.py
+++ b/tensorflow/examples/speech_commands/input_data.py
@@ -417,8 +417,7 @@ class AudioProcessor(object):
       sess: TensorFlow session that was active when processor was created.
 
     Returns:
-      List of sample data for the transformed samples, and list of labels in
-      one-hot form.
+      List of sample data for the transformed samples, and list of label indexes
     """
     # Pick one of the partitions to choose samples from.
     candidates = self.data_index[mode]
@@ -428,7 +427,7 @@ class AudioProcessor(object):
       sample_count = max(0, min(how_many, len(candidates) - offset))
     # Data and labels will be populated and returned.
     data = np.zeros((sample_count, model_settings['fingerprint_size']))
-    labels = np.zeros((sample_count, model_settings['label_count']))
+    labels = np.zeros(sample_count)
     desired_samples = model_settings['desired_samples']
     use_background = self.background_data and (mode == 'training')
     pick_deterministically = (mode != 'training')
@@ -483,7 +482,7 @@ class AudioProcessor(object):
       # Run the graph to produce the output audio.
       data[i - offset, :] = sess.run(self.mfcc_, feed_dict=input_dict).flatten()
       label_index = self.word_to_index[sample['label']]
-      labels[i - offset, label_index] = 1
+      labels[i - offset] = label_index
     return data, labels
 
   def get_unprocessed_data(self, how_many, model_settings, mode):
diff --git a/tensorflow/examples/speech_commands/train.py b/tensorflow/examples/speech_commands/train.py
index bec7dacd21..a4e80041f8 100644
--- a/tensorflow/examples/speech_commands/train.py
+++ b/tensorflow/examples/speech_commands/train.py
@@ -133,7 +133,7 @@ def main(_):
 
   # Define loss and optimizer
   ground_truth_input = tf.placeholder(
-      tf.float32, [None, label_count], name='groundtruth_input')
+      tf.int64, [None], name='groundtruth_input')
 
   # Optionally we can add runtime checks to spot when NaNs or other symptoms of
   # numerical errors start occurring during training.
@@ -144,9 +144,8 @@ def main(_):
 
   # Create the back propagation and training evaluation machinery in the graph.
   with tf.name_scope('cross_entropy'):
-    cross_entropy_mean = tf.reduce_mean(
-        tf.nn.softmax_cross_entropy_with_logits(
-            labels=ground_truth_input, logits=logits))
+    cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
+        labels=ground_truth_input, logits=logits)
   tf.summary.scalar('cross_entropy', cross_entropy_mean)
   with tf.name_scope('train'), tf.control_dependencies(control_dependencies):
     learning_rate_input = tf.placeholder(
@@ -154,10 +153,9 @@ def main(_):
     train_step = tf.train.GradientDescentOptimizer(
         learning_rate_input).minimize(cross_entropy_mean)
   predicted_indices = tf.argmax(logits, 1)
-  expected_indices = tf.argmax(ground_truth_input, 1)
-  correct_prediction = tf.equal(predicted_indices, expected_indices)
+  correct_prediction = tf.equal(predicted_indices, ground_truth_input)
   confusion_matrix = tf.confusion_matrix(
-      expected_indices, predicted_indices, num_classes=label_count)
+      ground_truth_input, predicted_indices, num_classes=label_count)
   evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
   tf.summary.scalar('accuracy', evaluation_step)