Roolback of 268229dfedd5ee95ddb8028625a3b27ae5361a50
PiperOrigin-RevId: 276513946
Change-Id: Idb7f04f12f462bc5f17e5928de686ed251138553
diff --git a/tensorflow/python/keras/distribute/keras_utils_test.py b/tensorflow/python/keras/distribute/keras_utils_test.py
index 63ad04b..c476912 100644
--- a/tensorflow/python/keras/distribute/keras_utils_test.py
+++ b/tensorflow/python/keras/distribute/keras_utils_test.py
@@ -259,7 +259,8 @@
dataset = keras_test_lib.get_dataset(distribution)
exception_error_message = (
- '`validation_split` argument is not supported when ')
+ '`validation_split` argument is not supported when input `x`'
+ ' is a dataset or a dataset iterator.+')
# Test with validation split
with self.assertRaisesRegexp(ValueError, exception_error_message):
diff --git a/tensorflow/python/keras/engine/data_adapter.py b/tensorflow/python/keras/engine/data_adapter.py
index bf1be80..46d8944 100644
--- a/tensorflow/python/keras/engine/data_adapter.py
+++ b/tensorflow/python/keras/engine/data_adapter.py
@@ -415,9 +415,6 @@
as Numpy, but it ignores any case where all the inputs are Tensors or Numpy
arrays (because that case is handled by the base TensorLikeDataAdapter).
- It ignores scipy sparse matrices and Composite Tensors because those are
- handled by the CompositeTensorDataAdapter.
-
It also does not handle lists/tuples of scalars, because those are handled
by the ListsOfScalarsDataAdapter.
"""
@@ -437,8 +434,7 @@
hasattr(v, "__len__")
)
- if (not TensorLikeDataAdapter.can_handle(x, y) and
- not CompositeTensorDataAdapter.can_handle(x, y)):
+ if not TensorLikeDataAdapter.can_handle(x, y):
return all(_is_array_like(v) for v in flat_inputs)
else:
return False
diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py
index 82549db..307a04d 100644
--- a/tensorflow/python/keras/engine/training.py
+++ b/tensorflow/python/keras/engine/training.py
@@ -155,8 +155,7 @@
self._compile_distribution = False
self._run_eagerly = None
- self._experimental_run_tf_function = (
- ops.executing_eagerly_outside_functions())
+ self._experimental_run_tf_function = False
def get_weights(self):
"""Retrieves the weights of the model.
@@ -309,18 +308,11 @@
'Session arguments: %s' % (self._function_kwargs,))
self._set_optimizer(optimizer)
- is_any_keras_optimizer_v1 = any(
- (isinstance(opt, optimizers.Optimizer)
- and not isinstance(opt, optimizers.TFOptimizer)
- ) for opt in nest.flatten(self.optimizer))
-
- if is_any_keras_optimizer_v1 and ops.executing_eagerly_outside_functions():
- raise ValueError('`tf.compat.v1.keras` Optimizer (', optimizer, ') is '
- 'not supported when eager execution is enabled. Use a '
- '`tf.keras` Optimizer instead, or disable eager '
- 'execution.')
+ is_any_optimizer_v1 = any(isinstance(opt, optimizers.Optimizer)
+ for opt in nest.flatten(self.optimizer))
if ((target_tensors is not None)
+ or is_any_optimizer_v1
or not ops.executing_eagerly_outside_functions()):
# Fallback out of things that aren't supported with v2 loops
self._experimental_run_tf_function = False
@@ -3315,11 +3307,6 @@
"""
if issparse is not None and issparse(value):
if ops.is_dense_tensor_like(expected_input):
- if ops.executing_eagerly_outside_functions():
- # In TF2 we do not silently densify sparse matrices.
- raise ValueError('A SciPy sparse matrix was passed to a model '
- 'that expects dense inputs. Please densify your '
- 'inputs first, such as by calling `x.toarray().')
return value.toarray()
else:
sparse_coo = value.tocoo()
diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py
index 27acdf2..be1b2e8 100644
--- a/tensorflow/python/keras/engine/training_eager.py
+++ b/tensorflow/python/keras/engine/training_eager.py
@@ -157,7 +157,6 @@
weights = mask
else:
# Update dimensions of weights to match with mask if possible.
- weights = math_ops.cast(weights, outs[i].dtype)
mask, _, weights = (
tf_losses_utils.squeeze_or_expand_dimensions(
mask, sample_weight=weights))
diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py
index 5a9a857..05a033a 100644
--- a/tensorflow/python/keras/engine/training_test.py
+++ b/tensorflow/python/keras/engine/training_test.py
@@ -644,31 +644,10 @@
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 4))
- # Test execution on inputs that are lists of scalars.
- # TF2 and TF1 have slightly different semantics:
- if (testing_utils.should_run_tf_function()
- or testing_utils.should_run_eagerly()):
- # In TF2 to avoid any ambiguity when there are nested lists
- # the entire input gets converted to a
- # single numpy array (& it only works in the case of a single io model)
- model.fit(np.ndarray.tolist(input_a_np),
- np.ndarray.tolist(input_b_np),
- epochs=2,
- batch_size=5,
- verbose=2)
- else:
- # In TF1 there was logic to try disambiguating between the individual
- # inputs when lists are nested. This allowed multi-io functional models
- # to support lists of scalars as input, but it caused ambiguity issues
- # for subclass models & made it trickier to pass multi-dimensional inputs
- # as lists of scalars to single io models. This was an excessive amount
- # of complexity for what boiled down to a convenience method we were
- # mainly just using for writing tests.
- model.fit([np.ndarray.tolist(input_a_np)],
- [np.ndarray.tolist(input_b_np)],
- epochs=2,
- batch_size=5,
- verbose=2)
+ model.fit([np.ndarray.tolist(input_a_np)], [np.ndarray.tolist(input_b_np)],
+ epochs=2,
+ batch_size=5,
+ verbose=2)
@keras_parameterized.run_all_keras_modes
def test_evaluate_predict_on_arrays(self):
@@ -858,43 +837,12 @@
model = MyModel()
self.assertIn('{"a": {}}', model.to_json())
- @keras_parameterized.run_all_keras_modes(always_skip_v1=True)
+ @keras_parameterized.run_all_keras_modes
def test_training_on_sparse_data_with_dense_placeholders(self):
- if scipy_sparse is None:
- return
+ # TODO(kaftan) Test seems to not work, file ticket
+ if testing_utils.should_run_eagerly() and context.executing_eagerly():
+ self.skipTest('Skipping running model eagerly.')
- test_inputs = [
- scipy_sparse.random(6, 3, density=0.25).tocsr() for _ in range(2)
- ]
- test_outputs = [
- scipy_sparse.random(6, i, density=0.25).tocsr() for i in range(3, 5)
- ]
- in1 = keras.layers.Input(shape=(3,))
- in2 = keras.layers.Input(shape=(3,))
- out1 = keras.layers.Dropout(0.5, name='dropout')(in1)
- out2 = keras.layers.Dense(4, name='dense_1')(in2)
- model = keras.Model([in1, in2], [out1, out2])
- model.experimental_run_tf_function = testing_utils.should_run_tf_function()
-
- with self.assertRaisesRegexp(ValueError, 'Please densify'):
- model.predict(test_inputs, batch_size=2)
- optimizer = 'rmsprop'
- model.compile(
- optimizer,
- 'mse',
- metrics=['mae', metrics_module.CategoricalAccuracy()],
- run_eagerly=testing_utils.should_run_eagerly(),
- experimental_run_tf_function=testing_utils.should_run_tf_function())
-
- with self.assertRaisesRegexp(ValueError, 'Please densify'):
- model.fit(test_inputs, test_outputs,
- epochs=1, batch_size=2)
-
- with self.assertRaisesRegexp(ValueError, 'Please densify'):
- model.evaluate(test_inputs, test_outputs, batch_size=2)
-
- @tf_test_util.run_deprecated_v1
- def test_training_on_sparse_data_with_dense_placeholders_v1(self):
if scipy_sparse is None:
return
@@ -910,17 +858,23 @@
out2 = keras.layers.Dense(4, name='dense_1')(in2)
model = keras.Model([in1, in2], [out1, out2])
model.predict(test_inputs, batch_size=2)
- optimizer = 'rmsprop'
+ optimizer = RMSPropOptimizer(learning_rate=0.001)
model.compile(
optimizer,
'mse',
- metrics=['mae', metrics_module.CategoricalAccuracy()])
+ metrics=['mae', metrics_module.CategoricalAccuracy()],
+ run_eagerly=testing_utils.should_run_eagerly(),
+ experimental_run_tf_function=testing_utils.should_run_tf_function())
model.fit(test_inputs, test_outputs,
epochs=1, batch_size=2, validation_split=0.5)
model.evaluate(test_inputs, test_outputs, batch_size=2)
@keras_parameterized.run_all_keras_modes
def test_compile_with_sparse_placeholders(self):
+ # TODO(kaftan) Test seems to not work, file ticket
+ if testing_utils.should_run_eagerly() and context.executing_eagerly():
+ self.skipTest('Skipping running model eagerly.')
+
input_layer = keras.layers.Input(shape=(10,), sparse=True)
weights = variables_lib.Variable(
np.ones((10, 1)).astype(np.float32), name='weights')
@@ -929,7 +883,7 @@
model = keras.Model([input_layer], output_layer)
model.compile(
loss='binary_crossentropy',
- optimizer='adam',
+ optimizer=keras.optimizers.Adam(lr=0.0001),
metrics=['accuracy'],
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.should_run_tf_function())
@@ -2478,8 +2432,12 @@
class TestTrainingWithDataTensors(keras_parameterized.TestCase):
- @tf_test_util.run_deprecated_v1
+ @keras_parameterized.run_all_keras_modes
def test_training_and_eval_methods_on_symbolic_tensors_single_io(self):
+ # TODO(kaftan) Test seems to not work, file ticket
+ if context.executing_eagerly():
+ self.skipTest('Skipping eager execution.')
+
x = keras.layers.Input(shape=(3,), name='input')
y = keras.layers.Dense(4, name='dense')(x)
model = keras.Model(x, y)
@@ -2489,7 +2447,9 @@
model.compile(
optimizer,
loss,
- metrics=['mae', metrics_module.CategoricalAccuracy()])
+ metrics=['mae', metrics_module.CategoricalAccuracy()],
+ run_eagerly=testing_utils.should_run_eagerly(),
+ experimental_run_tf_function=testing_utils.should_run_tf_function())
inputs = keras.backend.zeros(shape=(10, 3))
targets = keras.backend.zeros(shape=(10, 4))
@@ -2518,8 +2478,12 @@
epochs=1, steps_per_epoch=2, verbose=0,
validation_data=(inputs, targets), validation_steps=2)
- @tf_test_util.run_deprecated_v1
+ @keras_parameterized.run_all_keras_modes
def test_training_and_eval_methods_on_symbolic_tensors_multi_io(self):
+ # TODO(kaftan) Test seems to not work, file ticket
+ if context.executing_eagerly():
+ self.skipTest('Skipping eager execution.')
+
a = keras.layers.Input(shape=(3,), name='input_a')
b = keras.layers.Input(shape=(3,), name='input_b')
@@ -2537,7 +2501,9 @@
optimizer,
loss,
metrics=['mae', metrics_module.CategoricalAccuracy()],
- loss_weights=loss_weights)
+ loss_weights=loss_weights,
+ run_eagerly=testing_utils.should_run_eagerly(),
+ experimental_run_tf_function=testing_utils.should_run_tf_function())
input_a_tf = keras.backend.zeros(shape=(10, 3))
input_b_tf = keras.backend.zeros(shape=(10, 3))
@@ -2751,7 +2717,6 @@
out = model.predict(None, steps=3)
self.assertEqual(out.shape, (10 * 3, 4))
- @keras_parameterized.run_all_keras_modes
def test_model_with_partial_loss(self):
with self.cached_session():
a = keras.Input(shape=(3,), name='input_a')
@@ -2919,7 +2884,6 @@
self.assertEqual(out[0].shape, (10 * 3, 4))
self.assertEqual(out[1].shape, (10 * 3, 4))
- @keras_parameterized.run_all_keras_modes
def test_target_tensors(self):
with self.cached_session():
# single-output, as list
@@ -3239,29 +3203,32 @@
@keras_parameterized.run_all_keras_modes
def test_metrics_masking(self):
- np.random.seed(1337)
- model = keras.models.Sequential()
- model.add(keras.layers.Masking(mask_value=0, input_shape=(2, 1)))
- model.add(
- keras.layers.TimeDistributed(
- keras.layers.Dense(1, kernel_initializer='ones')))
- model.compile(
- RMSPropOptimizer(learning_rate=0.001),
- loss='mse',
- weighted_metrics=['accuracy'],
- run_eagerly=testing_utils.should_run_eagerly(),
- experimental_run_tf_function=testing_utils.should_run_tf_function())
+ if testing_utils.should_run_eagerly():
+ self.skipTest('b/120495761')
+ with self.cached_session():
+ np.random.seed(1337)
+ model = keras.models.Sequential()
+ model.add(keras.layers.Masking(mask_value=0, input_shape=(2, 1)))
+ model.add(
+ keras.layers.TimeDistributed(
+ keras.layers.Dense(1, kernel_initializer='ones')))
+ model.compile(
+ RMSPropOptimizer(learning_rate=0.001),
+ loss='mse',
+ weighted_metrics=['accuracy'],
+ run_eagerly=testing_utils.should_run_eagerly(),
+ experimental_run_tf_function=testing_utils.should_run_tf_function())
- # verify that masking is applied.
- x = np.array([[[1], [1]], [[1], [1]], [[0], [0]]])
- y = np.array([[[1], [1]], [[0], [1]], [[1], [1]]])
- scores = model.train_on_batch(x, y)
- self.assertArrayNear(scores, [0.25, 0.75], 0.1)
+ # verify that masking is applied.
+ x = np.array([[[1], [1]], [[1], [1]], [[0], [0]]])
+ y = np.array([[[1], [1]], [[0], [1]], [[1], [1]]])
+ scores = model.train_on_batch(x, y)
+ self.assertArrayNear(scores, [0.25, 0.75], 0.1)
- # verify that masking is combined with sample weights.
- w = np.array([3, 2, 4])
- scores = model.train_on_batch(x, y, sample_weight=w)
- self.assertArrayNear(scores, [0.3328, 0.8], 0.001)
+ # verify that masking is combined with sample weights.
+ w = np.array([3, 2, 4])
+ scores = model.train_on_batch(x, y, sample_weight=w)
+ self.assertArrayNear(scores, [0.3328, 0.8], 0.001)
@keras_parameterized.run_all_keras_modes
def test_add_metric_with_tensor_on_model(self):
diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py
index 3f660d8..4c5f9d4 100644
--- a/tensorflow/python/keras/engine/training_utils.py
+++ b/tensorflow/python/keras/engine/training_utils.py
@@ -1063,7 +1063,6 @@
weights = mask
else:
# Update dimensions of weights to match with mask.
- weights = math_ops.cast(weights, dtype=y_pred.dtype)
mask, _, weights = tf_losses_utils.squeeze_or_expand_dimensions(
mask, sample_weight=weights)
weights *= mask
diff --git a/tensorflow/python/keras/engine/training_v2_utils.py b/tensorflow/python/keras/engine/training_v2_utils.py
index bab2774..9454402 100644
--- a/tensorflow/python/keras/engine/training_v2_utils.py
+++ b/tensorflow/python/keras/engine/training_v2_utils.py
@@ -360,7 +360,7 @@
# If `model._distribution_strategy` is True, then we are in a replica context
# at this point.
- inputs = training_utils.cast_to_model_input_dtypes(inputs, model)
+ inputs = training_utils.cast_if_floating_dtype(inputs)
if isinstance(inputs, collections.Sequence):
# Unwrap lists with only one input, as we do when training on batch
if len(inputs) == 1:
diff --git a/tensorflow/python/keras/layers/gru_test.py b/tensorflow/python/keras/layers/gru_test.py
index 76890c4..be486bc 100644
--- a/tensorflow/python/keras/layers/gru_test.py
+++ b/tensorflow/python/keras/layers/gru_test.py
@@ -44,7 +44,6 @@
'return_sequences': True},
input_shape=(num_samples, timesteps, embedding_dim))
- @tf_test_util.run_v2_only
def test_float64_GRU(self):
num_samples = 2
timesteps = 3
diff --git a/tensorflow/python/keras/layers/gru_v2_test.py b/tensorflow/python/keras/layers/gru_v2_test.py
index 1bccffa..0135091 100644
--- a/tensorflow/python/keras/layers/gru_v2_test.py
+++ b/tensorflow/python/keras/layers/gru_v2_test.py
@@ -343,7 +343,6 @@
'return_sequences': True},
input_shape=(num_samples, timesteps, embedding_dim))
- @test_util.run_v2_only
def test_float64_GRU(self):
num_samples = 2
timesteps = 3
diff --git a/tensorflow/python/keras/layers/lstm_test.py b/tensorflow/python/keras/layers/lstm_test.py
index a4dcf39..78e515d 100644
--- a/tensorflow/python/keras/layers/lstm_test.py
+++ b/tensorflow/python/keras/layers/lstm_test.py
@@ -23,7 +23,6 @@
from tensorflow.python import keras
from tensorflow.python.eager import context
-from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
@@ -45,7 +44,6 @@
'return_sequences': True},
input_shape=(num_samples, timesteps, embedding_dim))
- @tf_test_util.run_v2_only
def test_float64_LSTM(self):
num_samples = 2
timesteps = 3
diff --git a/tensorflow/python/keras/layers/lstm_v2_test.py b/tensorflow/python/keras/layers/lstm_v2_test.py
index d0759f9..20f21c8 100644
--- a/tensorflow/python/keras/layers/lstm_v2_test.py
+++ b/tensorflow/python/keras/layers/lstm_v2_test.py
@@ -570,7 +570,6 @@
},
input_shape=(num_samples, timesteps, embedding_dim))
- @test_util.run_v2_only
def test_float64_LSTM(self):
num_samples = 2
timesteps = 3
diff --git a/tensorflow/python/keras/layers/recurrent_test.py b/tensorflow/python/keras/layers/recurrent_test.py
index e48c3a8..29ad229 100644
--- a/tensorflow/python/keras/layers/recurrent_test.py
+++ b/tensorflow/python/keras/layers/recurrent_test.py
@@ -1477,7 +1477,7 @@
input_layer, initial_state=initial_states)
model = keras.Model(input_layer, rnn_output)
model.compile(
- optimizer='rmsprop', loss='mse',
+ optimizer=keras.optimizers.RMSprop(), loss='mse',
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.should_run_tf_function())
return model
diff --git a/tensorflow/python/keras/layers/simplernn_test.py b/tensorflow/python/keras/layers/simplernn_test.py
index bbd8c8d..731e312 100644
--- a/tensorflow/python/keras/layers/simplernn_test.py
+++ b/tensorflow/python/keras/layers/simplernn_test.py
@@ -22,7 +22,6 @@
from tensorflow.python import keras
from tensorflow.python.eager import context
-from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import keras_parameterized
from tensorflow.python.keras import testing_utils
from tensorflow.python.platform import test
@@ -43,7 +42,6 @@
'return_sequences': True},
input_shape=(num_samples, timesteps, embedding_dim))
- @tf_test_util.run_v2_only
def test_float64_SimpleRNN(self):
num_samples = 2
timesteps = 3
diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py
index c96ce16..cedccd6 100644
--- a/tensorflow/python/keras/layers/wrappers_test.py
+++ b/tensorflow/python/keras/layers/wrappers_test.py
@@ -376,8 +376,6 @@
layer=[keras.layers.LSTM,
keras.layers.Dense]))
def test_TimeDistributed_with_ragged_input(self, layer):
- if testing_utils.should_run_tf_function():
- self.skipTest('b/143103634')
np.random.seed(100)
layer = layer(4)
ragged_data = ragged_factory_ops.constant(
@@ -389,9 +387,6 @@
x_ragged = keras.Input(shape=(None, 2, 1), dtype='float32', ragged=True)
y_ragged = keras.layers.TimeDistributed(layer)(x_ragged)
model_1 = keras.models.Model(x_ragged, y_ragged)
- model_1._experimental_run_tf_function = (
- testing_utils.should_run_tf_function())
- model_1._run_eagerly = testing_utils.should_run_eagerly()
output_ragged = model_1.predict(ragged_data, steps=1)
x_dense = keras.Input(shape=(None, 2, 1), dtype='float32')
@@ -399,9 +394,6 @@
y_dense = keras.layers.TimeDistributed(layer)(masking)
model_2 = keras.models.Model(x_dense, y_dense)
dense_data = ragged_data.to_tensor()
- model_2._experimental_run_tf_function = (
- testing_utils.should_run_tf_function())
- model_2._run_eagerly = testing_utils.should_run_eagerly()
output_dense = model_2.predict(dense_data, steps=1)
output_ragged = ragged_tensor.convert_to_tensor_or_ragged_tensor(
diff --git a/tensorflow/python/keras/saving/hdf5_format_test.py b/tensorflow/python/keras/saving/hdf5_format_test.py
index 9655741..e8da051a 100644
--- a/tensorflow/python/keras/saving/hdf5_format_test.py
+++ b/tensorflow/python/keras/saving/hdf5_format_test.py
@@ -315,7 +315,7 @@
name='d1'))
ref_model.add(keras.layers.Dense(num_classes, name='d2'))
ref_model.compile(loss=keras.losses.MSE,
- optimizer='rmsprop',
+ optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
f_ref_model = h5py.File(h5_path, 'w')
@@ -327,7 +327,7 @@
input_dim=input_dim, name='d1'))
model.add(keras.layers.Dense(num_classes, name='d2'))
model.compile(loss=keras.losses.MSE,
- optimizer='rmsprop',
+ optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
with self.assertRaisesRegexp(ValueError,
r'Layer #0 \(named \"d1\"\) expects 1 '
diff --git a/tensorflow/python/keras/saving/save_test.py b/tensorflow/python/keras/saving/save_test.py
index 9f9edf5..58dd58b 100644
--- a/tensorflow/python/keras/saving/save_test.py
+++ b/tensorflow/python/keras/saving/save_test.py
@@ -103,7 +103,7 @@
model.compile(
loss=keras.losses.MSE,
- optimizer='rmsprop',
+ optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
config = model.to_json()
@@ -145,7 +145,7 @@
model.compile(
loss=keras.losses.MSE,
- optimizer='rmsprop',
+ optimizer=keras.optimizers.RMSprop(lr=0.0001),
metrics=[keras.metrics.categorical_accuracy])
config = model.to_json()
