commit cc93f3ca1c13e78c4ea905abc2c6a5870773ca73
author A. Unique TensorFlower <gardener@tensorflow.org> Fri Oct 04 14:29:42 2019 -0700
committer TensorFlower Gardener <gardener@tensorflow.org> Fri Oct 04 22:56:21 2019 -0700
tree 26088eb48a6e9bb2b6d870dccfd7773b285e3e7d
parent 135a29c383c0629322bd3511cb7216e6ee1cb479

PR #32847: Add a unit test for training and validation callbacks

Imported from GitHub PR #32847

The test is to check that the progress bar shown Keras during the training process is working properly when training and validating with inputs of unknown sizes.

Copybara import of the project:

  - 391206709057bedcadb511e305012de801c5992d Add a unit test for training and validation callbacks by Ivan Ukhov <ivan.ukhov@gmail.com>
  - 2f4d26ba6a99a04c974b83a572430d5684759f05 Merge 391206709057bedcadb511e305012de801c5992d into 18f70... by Ivan Ukhov <ivan.ukhov@gmail.com>

COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/32847 from IvanUkhov:shared-callbacks 391206709057bedcadb511e305012de801c5992d
PiperOrigin-RevId: 272958417

tensorflow/python/keras/callbacks_test.py

diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py
index 12f22e7..2c556de 100644
--- a/tensorflow/python/keras/callbacks_test.py
+++ b/tensorflow/python/keras/callbacks_test.py
@@ -319,6 +319,37 @@
       self.assertRegexpMatches(printed.contents(), expected_log)
 
   @keras_parameterized.run_with_all_model_types
+  @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
+  def test_progbar_logging_training_validation(self):
+    model = self._get_model(input_shape=(2,))
+
+    def generator():
+      for _ in range(100):
+        yield [1, 1], 1
+
+    training = dataset_ops.Dataset \
+        .from_generator(
+            generator=generator,
+            output_types=('float64', 'float64'),
+            output_shapes=([2], [])) \
+        .batch(2) \
+        .repeat()
+    validation = dataset_ops.Dataset \
+        .from_generator(
+            generator=generator,
+            output_types=('float64', 'float64'),
+            output_shapes=([2], [])) \
+        .batch(2)
+    expected_log = (
+        r'(?s).*1/2.*20/20.*- loss:.*- my_acc:.*- val_loss:.*- val_my_acc:'
+        r'.*2/2.*20/20.*- loss:.*- my_acc:.*- val_loss:.*- val_my_acc:.*')
+
+    with self.captureWritesToStream(sys.stdout) as printed:
+      model.fit(
+          x=training, validation_data=validation, epochs=2, steps_per_epoch=20)
+      self.assertRegexpMatches(printed.contents(), expected_log)
+
+  @keras_parameterized.run_with_all_model_types
   def test_ModelCheckpoint(self):
     if h5py is None:
       return  # Skip test if models cannot be saved.