def test_generator_input_to_fit_eval_predict(self):
val_data = np.ones([10, 10], np.float32), np.ones([10, 1], np.float32)
def ones_generator():
while True:
yield np.ones([10, 10], np.float32), np.ones([10, 1],
np.float32)
model = testing_utils.get_small_mlp(num_hidden=10,
num_classes=1,
input_dim=10)
model.compile(rmsprop.RMSprop(0.001),
'binary_crossentropy',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(ones_generator(),
steps_per_epoch=2,
validation_data=val_data,
epochs=2)
model.evaluate(ones_generator(), steps=2)
model.predict(ones_generator(), steps=2)
# Test with a changing batch size
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer=rmsprop.RMSprop(1e-3),
metrics=['mae',
metrics_module.CategoricalAccuracy()])
model.fit_generator(custom_generator_changing_batch_size(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False)
model.fit_generator(
custom_generator_changing_batch_size(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False,
validation_data=custom_generator_changing_batch_size(),
validation_steps=10)
model.fit(custom_generator_changing_batch_size(),
steps_per_epoch=5,
validation_data=custom_generator_changing_batch_size(),
validation_steps=10,
epochs=2)
model.evaluate(custom_generator_changing_batch_size(), steps=5)
model.predict(custom_generator_changing_batch_size(), steps=5)
def test_standard_loader(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.activity_regularizer = regularizers.get('l2')
def eager_loss():
return tf.reduce_sum(model.weights[0])
model.add_loss(eager_loss)
# Call predict to ensure that all layers are built and inputs are set.
model.predict(np.random.random((1, 3)).astype(np.float32))
saved_model_dir = self._save_model_dir()
model.save(saved_model_dir, save_format='tf')
loaded = tf.saved_model.load(saved_model_dir)
self.evaluate(tf.compat.v1.variables_initializer(loaded.variables))
all_close = ['variables', 'trainable_variables',
'non_trainable_variables']
for attr in all_close:
self.assertAllClose(self.evaluate(getattr(model, attr)),
self.evaluate(getattr(loaded.keras_api, attr)))
self.assertLen(loaded.regularization_losses, 1)
expected_layers = len(model.layers)
self.assertEqual(expected_layers, len(loaded.keras_api.layers))
input_arr = tf.ones((4, 3))
self.assertAllClose(self.evaluate(model(input_arr)),
self.evaluate(loaded(input_arr, training=False)))
def test_generator_methods(self):
model = testing_utils.get_small_mlp(10, 4, 3)
optimizer = rmsprop.RMSprop(learning_rate=0.001)
model.compile(optimizer,
loss='mse',
metrics=['mae',
metrics_module.CategoricalAccuracy()],
run_eagerly=True)
x = np.random.random((10, 3))
y = np.random.random((10, 4))
def numpy_iterator():
while True:
yield x, y
model.fit_generator(numpy_iterator(), steps_per_epoch=3, epochs=1)
model.evaluate_generator(numpy_iterator(), steps=3)
def inference_numpy_iterator():
while True:
yield x
out = model.predict_generator(inference_numpy_iterator(), steps=3)
self.assertEqual(out.shape, (30, 4))
def test_model_fit_and_validation_with_missing_arg_errors(self):
model = testing_utils.get_small_mlp(10, 4, 3)
model.compile(optimizer=rmsprop.RMSprop(learning_rate=0.001),
loss='mse',
run_eagerly=True)
x = tf.zeros(shape=(10, 3))
y = tf.zeros(shape=(10, 4))
dataset = tf.data.Dataset.from_tensor_slices(
(x, y)).repeat(10).batch(5)
validation_dataset = tf.data.Dataset.from_tensor_slices(
(x, y)).repeat().batch(5) # Infinite dataset.
model.fit(dataset, epochs=1, verbose=0)
# Step argument is required for infinite datasets.
with self.assertRaises(ValueError):
model.fit(dataset,
steps_per_epoch=2,
epochs=1,
verbose=0,
validation_data=validation_dataset)
with self.assertRaises(ValueError):
model.fit(dataset,
steps_per_epoch=2,
epochs=1,
verbose=0,
validation_data=validation_dataset)
def test_fit_generator_method(self):
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer=rmsprop.RMSprop(1e-3),
metrics=['mae',
metrics_module.CategoricalAccuracy()])
model.fit_generator(custom_generator_threads(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
workers=4,
use_multiprocessing=True)
model.fit_generator(custom_generator(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False)
model.fit_generator(custom_generator(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False,
validation_data=custom_generator(),
validation_steps=10)
model.fit_generator(custom_generator(),
steps_per_epoch=5,
validation_data=custom_generator(),
validation_steps=1,
workers=0)
def test_compiled_model(self):
# TODO(b/134519980): Issue with model.fit if the model call function uses
# a tf.function (Graph mode only).
if not tf.executing_eagerly():
return
input_arr = np.random.random((1, 3))
target_arr = np.random.random((1, 4))
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
expected_predict = model.predict(input_arr)
# Compile and save model.
model.compile('rmsprop', 'mse')
saved_model_dir = self._save_model_dir()
model.save(saved_model_dir, save_format='tf')
loaded = keras_load.load(saved_model_dir)
actual_predict = loaded.predict(input_arr)
self.assertAllClose(expected_predict, actual_predict)
loss_before = loaded.evaluate(input_arr, target_arr)
loaded.fit(input_arr, target_arr)
loss_after = loaded.evaluate(input_arr, target_arr)
self.assertLess(loss_after, loss_before)
predict = loaded.predict(input_arr)
ckpt_path = os.path.join(self.get_temp_dir(), 'weights')
loaded.save_weights(ckpt_path)
# Ensure that the checkpoint is compatible with the original model.
model.load_weights(ckpt_path)
self.assertAllClose(predict, model.predict(input_arr))
def test_training_with_sequences(self):
class DummySequence(data_utils.Sequence):
def __getitem__(self, idx):
return np.zeros([10, 2]), np.ones([10, 4])
def __len__(self):
return 10
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse', optimizer=rmsprop.RMSprop(1e-3))
model.fit_generator(DummySequence(),
steps_per_epoch=10,
validation_data=custom_generator(),
validation_steps=1,
max_queue_size=10,
workers=0,
use_multiprocessing=True)
model.fit_generator(DummySequence(),
steps_per_epoch=10,
validation_data=custom_generator(),
validation_steps=1,
max_queue_size=10,
workers=0,
use_multiprocessing=False)
def test_generator_methods_with_sample_weights(self):
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer=rmsprop.RMSprop(1e-3),
metrics=['mae',
metrics_module.CategoricalAccuracy()],
run_eagerly=testing_utils.should_run_eagerly())
model.fit_generator(custom_generator(mode=3),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False)
model.fit_generator(custom_generator(mode=3),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False,
validation_data=custom_generator(mode=3),
validation_steps=10)
model.predict_generator(custom_generator(mode=3),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
model.evaluate_generator(custom_generator(mode=3),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
def test_model_with_fixed_input_dim(self):
"""Ensure that the batch_dim is removed when saving.
When serving or retraining, it is important to reset the batch dim.
This can be an issue inside of tf.function. See b/132783590 for context.
"""
model = testing_utils.get_small_mlp(10, 3, 5)
loss_object = keras.losses.MeanSquaredError()
optimizer = gradient_descent.SGD()
@tf.function
def train_step(data, labels):
with tf.GradientTape() as tape:
predictions = model(data)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
x = np.random.random((8, 5))
y = np.random.random((8, 3))
train_step(x, y)
fn = saving_utils.trace_model_call(model)
self.assertEqual(fn.input_signature[0].shape.as_list(),
tf.TensorShape([None, 5]).as_list())
def test_evaluate_generator_method(self):
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer=rmsprop.RMSprop(1e-3),
metrics=['mae',
metrics_module.CategoricalAccuracy()],
run_eagerly=testing_utils.should_run_eagerly())
model.evaluate_generator(custom_generator_threads(),
steps=5,
max_queue_size=10,
workers=2,
verbose=1,
use_multiprocessing=True)
model.evaluate_generator(custom_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
model.evaluate_generator(custom_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False,
workers=0)
def test_predict_generator_method(self):
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.run_eagerly = testing_utils.should_run_eagerly()
model.predict_generator(custom_generator_threads(),
steps=5,
max_queue_size=10,
workers=2,
use_multiprocessing=True)
model.predict_generator(custom_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
model.predict_generator(custom_generator(),
steps=5,
max_queue_size=10,
workers=0)
# Test generator with just inputs (no targets)
model.predict_generator(custom_generator_threads(mode=1),
steps=5,
max_queue_size=10,
workers=2,
use_multiprocessing=True)
model.predict_generator(custom_generator(mode=1),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
model.predict_generator(custom_generator(mode=1),
steps=5,
max_queue_size=10,
workers=0)
def test_finite_dataset_unknown_cardinality_no_steps_arg(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.compile('rmsprop',
'mse',
run_eagerly=testing_utils.should_run_eagerly())
inputs = np.zeros((100, 3), dtype=np.float32)
targets = np.random.randint(0, 4, size=100, dtype=np.int32)
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.filter(lambda x, y: True).batch(10)
self.assertEqual(
keras.backend.get_value(tf.data.experimental.cardinality(dataset)),
tf.data.experimental.UNKNOWN_CARDINALITY)
batch_counter = BatchCounterCallback()
history = model.fit(dataset,
epochs=2,
verbose=1,
callbacks=[batch_counter])
self.assertLen(history.history['loss'], 2)
self.assertEqual(batch_counter.batch_end_count, 20)
model.evaluate(dataset)
out = model.predict(dataset)
self.assertEqual(out.shape[0], 100)
def test_calling_model_on_same_dataset(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
optimizer = 'rmsprop'
loss = 'mse'
metrics = ['mae']
model.compile(optimizer,
loss,
metrics=metrics,
run_eagerly=testing_utils.should_run_eagerly())
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
# Call fit with validation data
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_data=dataset,
validation_steps=2)
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_data=dataset,
validation_steps=2)
def test_built_models(self, serializer):
"""Built models should be copyable and picklable for all model types."""
if not tf.__internal__.tf2.enabled():
self.skipTest(
'pickle model only available in v2 when tf format is used.')
model = testing_utils.get_small_mlp(num_hidden=1,
num_classes=2,
input_dim=3)
model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy')
# train
x = np.random.random(size=(1000, 3))
y = np.random.randint(low=0, high=2, size=(1000, ))
model.fit(x, y) # builds model
y1 = model.predict(x)
# roundtrip with training
model = serializer(model)
y2 = model.predict(x)
# check that the predictions are the same
self.assertAllClose(y1, y2)
# and that we can continue training
model.fit(x, y)
y3 = model.predict(x)
# check that the predictions are the same
self.assertNotAllClose(y2, y3)
def test_custom_metric_model(self):
# TODO(b/134519980): Issue with `model.fit` if the model call function uses
# a `tf.function` in graph mode.
if not tf.executing_eagerly():
return
x = np.random.random((1, 3))
y = np.random.random((1, 4))
class CustomMetric(keras.metrics.MeanSquaredError):
pass
def zero_metric(y_true, y_pred):
del y_true, y_pred
return 0
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.compile(loss='mse', optimizer='SGD',
metrics=[CustomMetric(), zero_metric])
model.fit(x, y)
saved_model_dir = self._save_model_dir()
model.save(saved_model_dir, save_format='tf')
with self.assertRaisesRegex(ValueError, 'custom_objects'):
keras_load.load(saved_model_dir)
with generic_utils.CustomObjectScope(
{'CustomMetric': CustomMetric, 'zero_metric': zero_metric}):
loaded = keras_load.load(saved_model_dir)
self.evaluate([v.initializer for v in loaded.variables])
loaded.fit(x, y)
def _get_model(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.layers[-1].activity_regularizer = regularizers.get('l2')
model.activity_regularizer = regularizers.get('l2')
model.compile(
loss='mse',
optimizer='rmsprop')
def callable_loss():
return tf.reduce_sum(model.weights[0])
model.add_loss(callable_loss)
return model
def test_load_weights_from_saved_model(self):
save_path = self._save_model_dir()
save_format = testing_utils.get_save_format()
if save_format == 'h5' and testing_utils.get_model_type() == 'subclass':
# TODO(b/173646281): HDF5 format currently does not allow saving
# subclassed models.
return
with self.cached_session():
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
data = np.random.random((1, 3))
labels = np.random.random((1, 4))
model.compile(loss='mse', optimizer='rmsprop')
model.fit(data, labels)
model.save(save_path, save_format=save_format)
new_model = testing_utils.get_small_mlp(1, 4, input_dim=3)
if testing_utils.get_model_type() == 'subclass':
# Call on test data to build the model.
new_model.predict(data)
new_model.load_weights(save_path)
self.assertAllClose(model.weights, new_model.weights)
def test_unbuilt_models(self, serializer):
"""Unbuilt models should be copyable & deepcopyable for all model types."""
if not tf.__internal__.tf2.enabled():
self.skipTest(
'pickle model only available in v2 when tf format is used.')
original_model = testing_utils.get_small_mlp(num_hidden=1,
num_classes=2,
input_dim=3)
# roundtrip without compiling or training
model = serializer(original_model)
# compile
model.compile(optimizer='sgd', loss='sparse_categorical_crossentropy')
# roundtrip compiled but not trained
model = serializer(model)
def test_dataset_with_sparse_labels(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
optimizer = 'rmsprop'
model.compile(optimizer,
loss='sparse_categorical_crossentropy',
run_eagerly=testing_utils.should_run_eagerly())
inputs = np.zeros((10, 3), dtype=np.float32)
targets = np.random.randint(0, 4, size=10, dtype=np.int32)
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1)
def main(_) -> None:
with testing_utils.model_type_scope('functional'):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.layers[-1].activity_regularizer = regularizers.get('l2')
model.activity_regularizer = regularizers.get('l2')
model.compile(loss='mse', optimizer='rmsprop')
def callable_loss():
return tf.reduce_sum(model.weights[0])
model.add_loss(callable_loss)
print(f'_____Writing saved model to: {FLAGS.output_path}')
model.save(FLAGS.output_path)
def test_custom_metric_model(self):
class CustomMetric(keras.metrics.MeanSquaredError):
pass
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.compile(loss='mse', optimizer='rmsprop', metrics=[CustomMetric()])
saved_model_dir = self._save_model_dir()
tf.saved_model.save(model, saved_model_dir)
with self.assertRaisesRegex(ValueError, 'custom_objects'):
keras_load.load(saved_model_dir)
keras_load.load(saved_model_dir, compile=False)
def _test_save_and_load(self, use_dataset=False):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.layers[-1].activity_regularizer = regularizers.get('l2')
model.activity_regularizer = regularizers.get('l2')
model.compile(
loss='mse',
optimizer='rmsprop')
def callable_loss():
return tf.reduce_sum(model.weights[0])
model.add_loss(callable_loss)
x = np.random.random((1, 3))
y = np.random.random((1, 4))
if not tf.__internal__.tf2.enabled():
# The layer autocast behavior only runs when autocast is enabled, so
# in V1, the numpy inputs still need to be cast to float32.
x = x.astype(np.float32)
y = y.astype(np.float32)
if use_dataset:
dataset = tf.data.Dataset.from_tensor_slices((x, y)).batch(1)
model.fit(dataset)
else:
model.train_on_batch(x, y)
saved_model_dir = self._save_model_dir()
tf.saved_model.save(model, saved_model_dir)
loaded = keras_load.load(saved_model_dir)
self.evaluate(tf.compat.v1.variables_initializer(loaded.variables))
self.assertAllClose(self.evaluate(model.weights),
self.evaluate(loaded.weights))
input_arr = tf.constant(
np.random.random((1, 3)).astype(np.float32))
self.assertAllClose(self.evaluate(model(input_arr)),
self.evaluate(loaded(input_arr)))
# Validate losses. The order of conditional losses may change between the
# model and loaded model, so sort the losses first.
if tf.executing_eagerly():
self.assertAllClose(sorted(self.evaluate(model.losses)),
sorted(self.evaluate(loaded.losses)))
else:
self.assertAllClose(self.evaluate(model.get_losses_for(None)),
self.evaluate(loaded.get_losses_for(None)))
self.assertAllClose(
sorted(self.evaluate(model.get_losses_for(input_arr))),
sorted(self.evaluate(loaded.get_losses_for(input_arr))))
def test_custom_metric_model(self):
class CustomMetric(keras.metrics.MeanSquaredError):
pass
with self.cached_session():
metric = CustomMetric()
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.compile(loss='mse', optimizer='rmsprop', metrics=[metric])
self.evaluate(tf.compat.v1.global_variables_initializer())
self.evaluate([v.initializer for v in metric.variables])
saved_model_dir = self._save_model_dir()
tf.saved_model.save(model, saved_model_dir)
with self.assertRaisesRegex(ValueError, 'custom_objects'):
keras_load.load(saved_model_dir)
keras_load.load(saved_model_dir, compile=False)
def test_finite_dataset_unknown_cardinality_no_step_with_train_and_val(
self):
class CaptureStdout:
def __enter__(self):
self._stdout = sys.stdout
string_io = io.StringIO()
sys.stdout = string_io
self._stringio = string_io
return self
def __exit__(self, *args):
self.output = self._stringio.getvalue()
sys.stdout = self._stdout
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
model.compile('rmsprop',
'mse',
run_eagerly=testing_utils.should_run_eagerly())
inputs = np.zeros((100, 3), dtype=np.float32)
targets = np.random.randint(0, 4, size=100, dtype=np.int32)
dataset = tf.data.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.filter(lambda x, y: True).batch(10)
self.assertEqual(
keras.backend.get_value(tf.data.experimental.cardinality(dataset)),
tf.data.experimental.UNKNOWN_CARDINALITY)
batch_counter = BatchCounterCallback()
io_utils.enable_interactive_logging()
with CaptureStdout() as capture:
history = model.fit(dataset,
epochs=2,
callbacks=[batch_counter],
validation_data=dataset.take(3))
lines = capture.output.splitlines()
self.assertIn('10/10', lines[-1])
self.assertLen(history.history['loss'], 2)
self.assertEqual(batch_counter.batch_begin_count, 21)
self.assertEqual(batch_counter.batch_end_count, 20)
model.evaluate(dataset)
out = model.predict(dataset)
self.assertEqual(out.shape[0], 100)
def test_trace_model_outputs(self):
input_dim = 5 if testing_utils.get_model_type() == 'functional' else None
model = testing_utils.get_small_mlp(10, 3, input_dim)
inputs = tf.ones((8, 5))
if input_dim is None:
with self.assertRaisesRegex(ValueError, 'input shapes have not been set'):
saving_utils.trace_model_call(model)
model._set_inputs(inputs)
fn = saving_utils.trace_model_call(model)
signature_outputs = fn(inputs)
if model.output_names:
expected_outputs = {model.output_names[0]: model(inputs)}
else:
expected_outputs = {'output_1': model(inputs)}
self._assert_all_close(expected_outputs, signature_outputs)
def test_sequence_input_to_fit_eval_predict(self):
val_data = np.ones([10, 10], np.float32), np.ones([10, 1], np.float32)
class CustomSequence(data_utils.Sequence):
def __getitem__(self, idx):
return np.ones([10, 10], np.float32), np.ones([10, 1],
np.float32)
def __len__(self):
return 2
class CustomSequenceChangingBatchSize(data_utils.Sequence):
def __getitem__(self, idx):
batch_size = 10 - idx
return (np.ones([batch_size, 10], np.float32),
np.ones([batch_size, 1], np.float32))
def __len__(self):
return 2
model = testing_utils.get_small_mlp(num_hidden=10,
num_classes=1,
input_dim=10)
model.compile(rmsprop.RMSprop(0.001), 'binary_crossentropy')
model.fit(CustomSequence(), validation_data=val_data, epochs=2)
model.evaluate(CustomSequence())
model.predict(CustomSequence())
with self.assertRaisesRegex(ValueError,
'`y` argument is not supported'):
model.fit(CustomSequence(), y=np.ones([10, 1]))
with self.assertRaisesRegex(
ValueError, '`sample_weight` argument is not supported'):
model.fit(CustomSequence(), sample_weight=np.ones([10, 1]))
model.compile(rmsprop.RMSprop(0.001), 'binary_crossentropy')
model.fit(CustomSequenceChangingBatchSize(),
validation_data=val_data,
epochs=2)
model.evaluate(CustomSequenceChangingBatchSize())
model.predict(CustomSequenceChangingBatchSize())
def test_trace_model_outputs_after_fitting(self):
input_dim = 5 if testing_utils.get_model_type(
) == 'functional' else None
model = testing_utils.get_small_mlp(10, 3, input_dim)
model.compile(optimizer='sgd',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(x=np.random.random((8, 5)).astype(np.float32),
y=np.random.random((8, 3)).astype(np.float32),
epochs=2)
inputs = tf.ones((8, 5))
fn = saving_utils.trace_model_call(model)
signature_outputs = fn(inputs)
if model.output_names:
expected_outputs = {model.output_names[0]: model(inputs)}
else:
expected_outputs = {'output_1': model(inputs)}
self._assert_all_close(expected_outputs, signature_outputs)
def test_dataset_with_sample_weights(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
optimizer = 'rmsprop'
loss = 'mse'
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer,
loss,
metrics=metrics,
run_eagerly=testing_utils.should_run_eagerly())
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
sample_weights = np.ones((10), np.float32)
dataset = tf.data.Dataset.from_tensor_slices(
(inputs, targets, sample_weights))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=1)
model.evaluate(dataset, steps=2, verbose=1)
model.predict(dataset, steps=2)
def test_model_save(self):
input_dim = 5
model = testing_utils.get_small_mlp(10, 3, input_dim)
inputs = tf.ones((8, 5))
if testing_utils.get_model_type() == 'subclass':
model._set_inputs(inputs)
save_dir = os.path.join(self.get_temp_dir(), 'saved_model')
tf.saved_model.save(model, save_dir)
if model.output_names:
output_name = model.output_names[0]
input_name = model.input_names[0]
else:
output_name = 'output_1'
input_name = 'input_1'
self.assertAllClose({output_name: model.predict_on_batch(inputs)},
_import_and_infer(save_dir,
{input_name: np.ones((8, 5))}))
def test_generator_methods_invalid_use_case(self):
def invalid_generator():
while 1:
yield (0, 0, 0, 0)
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer=rmsprop.RMSprop(1e-3),
run_eagerly=testing_utils.should_run_eagerly())
with self.assertRaises(ValueError):
model.fit_generator(invalid_generator(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False)
with self.assertRaises(ValueError):
model.fit_generator(custom_generator(),
steps_per_epoch=5,
epochs=1,
verbose=1,
max_queue_size=10,
use_multiprocessing=False,
validation_data=invalid_generator(),
validation_steps=10)
with self.assertRaises(ValueError):
model.predict_generator(invalid_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
with self.assertRaises(ValueError):
model.evaluate_generator(invalid_generator(),
steps=5,
max_queue_size=10,
use_multiprocessing=False)
