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Fix for Keras logging

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PiperOrigin-RevId: 221099213
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A. Unique TensorFlower 2018-11-12 08:51:32 -08:00 committed by TensorFlower Gardener
parent fc44600e5c
commit e7d988404a
2 changed files with 17 additions and 0 deletions
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1
tensorflow/python/keras/engine/training_arrays.py
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@ -274,6 +274,7 @@ def model_iteration(model,
# TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready. # TODO(omalleyt): Handle ProgBar as part of Callbacks once hooks are ready.
progbar = _get_progbar(model, count_mode) progbar = _get_progbar(model, count_mode)
progbar.params = callbacks.params progbar.params = callbacks.params
progbar.params['verbose'] = verbose
# Find beforehand arrays that need sparse-to-dense conversion. # Find beforehand arrays that need sparse-to-dense conversion.
if issparse is not None: if issparse is not None:

16
tensorflow/python/keras/engine/training_test.py
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@ -18,9 +18,12 @@ from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
import io
import logging import logging
import sys
import numpy as np import numpy as np
import six
from tensorflow.python import keras from tensorflow.python import keras
from tensorflow.python.data.ops import dataset_ops 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) scores = model.train_on_batch(x, y, sample_weight=w)
self.assertArrayNear(scores, [0.2, 0.8], 0.1) 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): def test_losses_in_defun(self):
with context.eager_mode(): with context.eager_mode():
layer = keras.layers.Dense(1, kernel_regularizer='l1') layer = keras.layers.Dense(1, kernel_regularizer='l1')