Fix bug that prevented iterations variable from updating when training an Estimator that is created from a Keras model.

PiperOrigin-RevId: 211886643

1 files changed, 70 insertions, 32 deletions

diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py
index 290c4604ce..7e5a0c80a7 100644
--- a/tensorflow/python/estimator/keras_test.py
+++ b/tensorflow/python/estimator/keras_test.py
@@ -26,20 +26,23 @@ import numpy as np
 
 from tensorflow.core.protobuf import config_pb2
 from tensorflow.python import keras
+from tensorflow.python.data.ops import dataset_ops
 from tensorflow.python.estimator import keras as keras_lib
+from tensorflow.python.estimator import model_fn as model_fn_lib
 from tensorflow.python.estimator import run_config as run_config_lib
-from tensorflow.python.estimator.inputs import numpy_io
 from tensorflow.python.framework import ops
 from tensorflow.python.framework import test_util
 from tensorflow.python.keras import testing_utils
 from tensorflow.python.keras.optimizers import SGD
 from tensorflow.python.ops import variable_scope
+from tensorflow.python.ops import variables
 from tensorflow.python.ops.parsing_ops import gen_parsing_ops
 from tensorflow.python.platform import gfile
 from tensorflow.python.platform import test
 from tensorflow.python.summary.writer import writer_cache
 from tensorflow.python.training import rmsprop
 from tensorflow.python.training import session_run_hook
+from tensorflow.python.training import training_util
 
 
 try:
@@ -90,6 +93,15 @@ def simple_subclassed_model():
   return SimpleModel()
 
 
+def gen_input_fn(x, y=None, batch_size=128, num_epochs=1, shuffle=False):
+  def input_fn():
+    ds = dataset_ops.Dataset.from_tensor_slices((x, y) if y is not None else x)
+    if shuffle:
+      ds = ds.shuffle(1000)
+    return ds.repeat(num_epochs).batch(batch_size)
+  return input_fn
+
+
 def get_resource_for_simple_model(model_type='sequential',
                                   is_evaluate=False,):
   if model_type == 'sequential':
@@ -117,19 +129,19 @@ def get_resource_for_simple_model(model_type='sequential',
   y_train = keras.utils.to_categorical(y_train)
   y_test = keras.utils.to_categorical(y_test)
 
-  train_input_fn = numpy_io.numpy_input_fn(
+  train_input_fn = gen_input_fn(
       x=randomize_io_type(x_train, input_name),
       y=randomize_io_type(y_train, output_name),
       shuffle=False,
       num_epochs=None,
       batch_size=16)
 
-  evaluate_input_fn = numpy_io.numpy_input_fn(
+  evaluate_input_fn = gen_input_fn(
       x=randomize_io_type(x_test, input_name),
       y=randomize_io_type(y_test, output_name),
       num_epochs=1, shuffle=False)
 
-  predict_input_fn = numpy_io.numpy_input_fn(
+  predict_input_fn = gen_input_fn(
       x=randomize_io_type(x_test, input_name), num_epochs=1, shuffle=False)
 
   inference_input_fn = evaluate_input_fn if is_evaluate else predict_input_fn
@@ -203,7 +215,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
           optimizer='rmsprop',
           metrics=['mse', keras.metrics.categorical_accuracy])
 
-      with self.test_session():
+      with self.cached_session():
         est_keras = keras_lib.model_to_estimator(
             keras_model=keras_model, config=self._config)
         before_eval_results = est_keras.evaluate(
@@ -228,7 +240,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
           metrics=['mse', keras.metrics.categorical_accuracy])
 
       my_hook = MyHook()
-      with self.test_session():
+      with self.cached_session():
         est_keras = keras_lib.model_to_estimator(
             keras_model=keras_model, config=self._config)
         before_eval_results = est_keras.evaluate(
@@ -252,7 +264,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         optimizer=rmsprop.RMSPropOptimizer(1e-3),
         metrics=['mse', keras.metrics.categorical_accuracy])
     my_hook = MyHook()
-    with self.test_session():
+    with self.cached_session():
       keras_model.fit(x_train, y_train, epochs=1)
 
       keras_est = keras_lib.model_to_estimator(
@@ -274,7 +286,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
           optimizer=rmsprop.RMSPropOptimizer(1e-3),
           metrics=['mse', keras.metrics.categorical_accuracy])
 
-      with self.test_session():
+      with self.cached_session():
         est_keras = keras_lib.model_to_estimator(
             keras_model=keras_model,
             config=self._config)
@@ -297,7 +309,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         optimizer=rmsprop.RMSPropOptimizer(1e-3),
         metrics=['mse', keras.metrics.categorical_accuracy])
 
-    with self.test_session():
+    with self.cached_session():
       est_keras = keras_lib.model_to_estimator(
           keras_model=keras_model, config=self._config)
       est_keras.train(input_fn=train_input_fn, steps=_TRAIN_SIZE / 16)
@@ -316,7 +328,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         optimizer=rmsprop.RMSPropOptimizer(1e-3),
         metrics=['mse', keras.metrics.categorical_accuracy])
 
-    with self.test_session():
+    with self.cached_session():
       # Create state
       keras_model.train_on_batch(np.random.random((10,) + _INPUT_SIZE),
                                  np.random.random((10, _NUM_CLASS)))
@@ -343,7 +355,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         x_test, y_test), _, eval_input_fn = get_resource_for_simple_model(
             model_type='functional', is_evaluate=True)
 
-    with self.test_session():
+    with self.cached_session():
       metrics = [
           'binary_accuracy', 'binary_crossentropy', 'categorical_accuracy',
           'categorical_crossentropy', 'cosine_proximity', 'hinge',
@@ -357,7 +369,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       keras_model.fit(x_train, y_train, epochs=1)
       keras_eval = keras_model.evaluate(x_test, y_test, batch_size=32)
 
-    with self.test_session():
+    with self.cached_session():
       keras_est = keras_lib.model_to_estimator(
           keras_model=keras_model, config=self._config)
       est_eval = keras_est.evaluate(input_fn=eval_input_fn)
@@ -385,7 +397,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         x_test, _), _, pred_input_fn = get_resource_for_simple_model(
             model_type='sequential', is_evaluate=False)
 
-    with self.test_session():
+    with self.cached_session():
       keras_model.compile(
           loss='categorical_crossentropy',
           optimizer='adam',
@@ -393,7 +405,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       keras_model.fit(x_train, y_train, epochs=1)
       keras_pred = [np.argmax(y) for y in keras_model.predict(x_test)]
 
-    with self.test_session():
+    with self.cached_session():
       keras_est = keras_lib.model_to_estimator(
           keras_model=keras_model, config=self._config)
       est_pred = [
@@ -439,7 +451,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       output_dict = {'dense_2': c_test, 'dense_3': d_test}
       return input_dict, output_dict
 
-    with self.test_session():
+    with self.cached_session():
       model = multi_inputs_multi_outputs_model()
       est_keras = keras_lib.model_to_estimator(
           keras_model=model, config=self._config)
@@ -456,7 +468,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         x_test, _), _, pred_input_fn = get_resource_for_simple_model(
             model_type='functional', is_evaluate=False)
 
-    with self.test_session():
+    with self.cached_session():
       keras_model.compile(
           loss='categorical_crossentropy',
           optimizer='rmsprop',
@@ -466,7 +478,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       fname = os.path.join(self._base_dir, 'keras_model.h5')
       keras.models.save_model(keras_model, fname)
 
-    with self.test_session():
+    with self.cached_session():
       keras_est = keras_lib.model_to_estimator(
           keras_model_path=fname, config=self._config)
       est_pred = [
@@ -479,19 +491,19 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
     with self.assertRaisesRegexp(ValueError, 'Either'):
       keras_lib.model_to_estimator()
 
-    with self.test_session():
+    with self.cached_session():
       keras_model = simple_sequential_model()
       with self.assertRaisesRegexp(ValueError, 'not both'):
         keras_lib.model_to_estimator(
             keras_model=keras_model,
             keras_model_path=tempfile.mkdtemp(dir=self._base_dir))
 
-    with self.test_session():
+    with self.cached_session():
       keras_model = simple_sequential_model()
       with self.assertRaisesRegexp(ValueError, 'compiled'):
         keras_lib.model_to_estimator(keras_model=keras_model)
 
-    with self.test_session():
+    with self.cached_session():
       keras_model = simple_sequential_model()
       with self.assertRaisesRegexp(ValueError, 'not a local path'):
         keras_lib.model_to_estimator(
@@ -516,10 +528,10 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
     model = simple_functional_model()
     model.compile(
         loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
-    with self.test_session():
+    with self.cached_session():
       est_keras = keras_lib.model_to_estimator(
           keras_model=model, config=self._config)
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(KeyError,
                                    'Difference: .*invalid_input_name'):
         est_keras.train(input_fn=invald_input_name_input_fn, steps=100)
@@ -547,20 +559,20 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
     y_train = keras.utils.to_categorical(y_train, 2)
     input_name = keras_model.input_names[0]
     output_name = keras_model.output_names[0]
-    train_input_fn = numpy_io.numpy_input_fn(
+    train_input_fn = gen_input_fn(
         x=randomize_io_type(x_train, input_name),
         y=randomize_io_type(y_train, output_name),
         shuffle=False,
         num_epochs=None,
         batch_size=16)
     with self.assertRaisesRegexp(ValueError, 'relu6'):
-      with self.test_session():
+      with self.cached_session():
         est = keras_lib.model_to_estimator(
             keras_model=keras_model,
             model_dir=tempfile.mkdtemp(dir=self._base_dir))
         est.train(input_fn=train_input_fn, steps=1)
 
-    with self.test_session():
+    with self.cached_session():
       est = keras_lib.model_to_estimator(
           keras_model=keras_model,
           model_dir=tempfile.mkdtemp(dir=self._base_dir),
@@ -586,7 +598,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         }
     })
     with test.mock.patch.dict('os.environ', {'TF_CONFIG': tf_config}):
-      with self.test_session():
+      with self.cached_session():
         keras_lib.model_to_estimator(
             keras_model=keras_model,
             model_dir=tempfile.mkdtemp(dir=self._base_dir))
@@ -602,7 +614,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3)
       sess_config = config_pb2.ConfigProto(gpu_options=gpu_options)
       self._config._session_config = sess_config
-      with self.test_session():
+      with self.cached_session():
         keras_lib.model_to_estimator(
             keras_model=keras_model, config=self._config)
         self.assertEqual(
@@ -618,7 +630,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         optimizer='rmsprop',
         metrics=['mse', keras.metrics.categorical_accuracy])
 
-    with self.test_session():
+    with self.cached_session():
       est_keras = keras_lib.model_to_estimator(
           keras_model=keras_model, model_dir=self._base_dir,
           config=run_config_lib.RunConfig())
@@ -629,7 +641,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       self.assertEqual(self._base_dir, est_keras._config.model_dir)
       self.assertEqual(self._base_dir, est_keras._model_dir)
 
-    with self.test_session():
+    with self.cached_session():
       est_keras = keras_lib.model_to_estimator(
           keras_model=keras_model, model_dir=self._base_dir,
           config=None)
@@ -648,7 +660,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         optimizer='rmsprop',
         metrics=['mse', keras.metrics.categorical_accuracy])
 
-    with self.test_session():
+    with self.cached_session():
       with test.mock.patch.object(tempfile, 'mkdtemp', return_value=_TMP_DIR):
         est_keras = keras_lib.model_to_estimator(
             keras_model=keras_model,
@@ -663,7 +675,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         optimizer='rmsprop',
         metrics=['mse', keras.metrics.categorical_accuracy])
 
-    with self.test_session():
+    with self.cached_session():
       with self.assertRaisesRegexp(ValueError, '`model_dir` are set both in '
                                    'constructor and `RunConfig`'):
         keras_lib.model_to_estimator(
@@ -676,7 +688,7 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
         loss='categorical_crossentropy',
         optimizer=rmsprop.RMSPropOptimizer(1e-3),
         metrics=['mse', keras.metrics.categorical_accuracy])
-    with self.test_session():
+    with self.cached_session():
       keras_model.train_on_batch(
           np.random.random((10,) + _INPUT_SIZE),
           np.random.random((10, _NUM_CLASS)))
@@ -690,6 +702,32 @@ class TestKerasEstimator(test_util.TensorFlowTestCase):
       keras_lib.model_to_estimator(
           keras_model=keras_model, config=self._config)
 
+  def assert_increasing_global_step(self, optimizer):
+    keras_model, _, _, train_input_fn, _ = get_resource_for_simple_model(
+        model_type='sequential', is_evaluate=True)
+    keras_model.compile(
+        loss='categorical_crossentropy',
+        optimizer=optimizer,
+        metrics=['mse', keras.metrics.categorical_accuracy])
+    with self.cached_session() as sess:
+      keras_model_fn = keras_lib._create_keras_model_fn(keras_model)
+      global_step = training_util.create_global_step()
+      features, labels = train_input_fn().make_one_shot_iterator().get_next()
+      spec = keras_model_fn(features, labels, mode=model_fn_lib.ModeKeys.TRAIN)
+
+      sess.run(variables.global_variables_initializer())
+      sess.run(variables.local_variables_initializer())
+
+      self.assertEqual(global_step.eval(), 0)  # Sanity check
+      sess.run(spec.train_op)
+      self.assertEqual(global_step.eval(), 1)
+
+  def test_model_fn_increments_global_step_tf_optimizer(self):
+    self.assert_increasing_global_step(rmsprop.RMSPropOptimizer(1e-3))
+
+  def test_model_fn_increments_global_step_keras_optimizer(self):
+    self.assert_increasing_global_step('rmsprop')
+
 
 if __name__ == '__main__':
   test.main()