Fix for Keras logging

PiperOrigin-RevId: 221099213

tensorflow/python/keras/engine/training_arrays.py

@@ -274,6 +274,7 @@ def model_iteration(model,
   # TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
   progbar = _get_progbar(model, count_mode)
   progbar.params = callbacks.params
+  progbar.params['verbose'] = verbose
 
   # Find beforehand arrays that need sparse-to-dense conversion.
   if issparse is not None:

tensorflow/python/keras/engine/training_test.py

@@ -18,9 +18,12 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
+import io
 import logging
+import sys
 
 import numpy as np
+import six
 
 from tensorflow.python import keras
 from tensorflow.python.data.ops import dataset_ops
@@ -2222,6 +2225,19 @@ class TestTrainingWithMetrics(test.TestCase):
       scores = model.train_on_batch(x, y, sample_weight=w)
       self.assertArrayNear(scores, [0.2, 0.8], 0.1)
 
+  @tf_test_util.run_in_graph_and_eager_modes
+  def test_logging(self):
+    mock_stdout = io.BytesIO() if six.PY2 else io.StringIO()
+    model = keras.models.Sequential()
+    model.add(keras.layers.Dense(10, activation='relu'))
+    model.add(keras.layers.Dense(1, activation='sigmoid'))
+    model.compile(
+        RMSPropOptimizer(learning_rate=0.001), loss='binary_crossentropy')
+    with test.mock.patch.object(sys, 'stdout', mock_stdout):
+      model.fit(
+          np.ones((10, 10), 'float32'), np.ones((10, 1), 'float32'), epochs=10)
+    self.assertTrue('Epoch 5/10' in mock_stdout.getvalue())
+
   def test_losses_in_defun(self):
     with context.eager_mode():
       layer = keras.layers.Dense(1, kernel_regularizer='l1')