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(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
sample_weights = np.ones((10), np.float32)
dataset = dataset_ops.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 DISABLED_test_function_model_feature_layer_input(self):
col_a = fc.numeric_column('a')
col_b = fc.numeric_column('b')
feature_layer = fc.DenseFeatures([col_a, col_b], name='fc')
dense = keras.layers.Dense(4)
# This seems problematic.... We probably need something for DenseFeatures
# the way Input is for InputLayer.
output = dense(feature_layer)
model = keras.models.Model([feature_layer], [output])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(optimizer,
loss,
metrics=[metrics_module.CategoricalAccuracy(), 'mae'],
loss_weights=loss_weights)
data = ({'a': np.arange(10), 'b': np.arange(10)}, np.arange(10, 20))
print(model.fit(*data, epochs=1))
def test_model_methods_with_eager_tensors_single_io(self):
if not context.executing_eagerly():
# Only test V2 Function and V2 Eager modes, as V1 Graph mode with
# symbolic tensors has different requirements.
return
model = testing_utils.get_small_mlp(10, 4, 3)
optimizer = rmsprop.RMSprop(learning_rate=0.001)
loss = 'mse'
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer,
loss,
metrics=metrics,
run_eagerly=testing_utils.should_run_eagerly(),
run_distributed=testing_utils.should_run_distributed())
inputs = array_ops.zeros(shape=(10, 3))
targets = array_ops.zeros(shape=(10, 4))
model.fit(inputs, targets, epochs=1, batch_size=2, verbose=0)
model.fit(inputs,
targets,
epochs=1,
batch_size=3,
verbose=0,
shuffle=False)
model.fit(inputs,
targets,
epochs=1,
batch_size=4,
verbose=0,
validation_data=(inputs, targets))
model.evaluate(inputs, targets, batch_size=2, verbose=0)
model.predict(inputs, batch_size=2)
model.train_on_batch(inputs, targets)
model.test_on_batch(inputs, targets)
def test_predict_generator_method(self):
model = testing_utils.get_small_mlp(num_hidden=3,
num_classes=4,
input_dim=2)
model.compile(loss='mse',
optimizer='sgd',
metrics=['mae',
metrics_module.CategoricalAccuracy()],
run_eagerly=testing_utils.should_run_eagerly())
model.predict_generator(custom_generator(),
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(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_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(),
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_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()])
self._sleep_at_end = True
model.fit_generator(custom_generator(),
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_model_methods_with_eager_tensors_multi_io(self):
if not context.executing_eagerly():
# Only test V2 Function and V2 Eager modes, as V1 Graph mode with
# symbolic tensors has different requirements.
return
input_a = keras.layers.Input(shape=(3, ), name='input_a')
input_b = keras.layers.Input(shape=(3, ), name='input_b')
dense = keras.layers.Dense(4, name='dense')
dropout = keras.layers.Dropout(0.5, name='dropout')
model = testing_utils.get_multi_io_model([input_a, dense],
[input_b, dense, dropout])
optimizer = rmsprop.RMSprop(learning_rate=0.001)
loss = 'mse'
loss_weights = [1., 0.5]
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer,
loss,
metrics=metrics,
loss_weights=loss_weights,
run_eagerly=testing_utils.should_run_eagerly(),
experimental_run_tf_function=testing_utils.
should_run_tf_function(),
sample_weight_mode=None)
input_a = array_ops.zeros(shape=(10, 3))
input_b = array_ops.zeros(shape=(10, 3))
target_a = array_ops.zeros(shape=(10, 4))
target_b = array_ops.zeros(shape=(10, 4))
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0)
# Test: no shuffle.
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
# Test: validation data.
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=2,
verbose=0,
validation_data=([input_a, input_b], [target_a, target_b]))
model.train_on_batch([input_a, input_b], [target_a, target_b])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_a, target_b],
batch_size=2,
verbose=0)
model.test_on_batch([input_a, input_b], [target_a, target_b])
# Test: mix np and tensors.
input_b = np.zeros(shape=(10, 3)).astype('float32')
target_b = np.zeros(shape=(10, 4)).astype('float32')
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0)
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=2,
verbose=0,
validation_data=([input_a, input_b], [target_a, target_b]))
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
model.train_on_batch([input_a, input_b], [target_a, target_b])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_a, target_b],
batch_size=2,
verbose=0)
model.test_on_batch([input_a, input_b], [target_a, target_b])
def test_training_and_eval_methods_on_iterators_single_io(self, model):
if model == 'functional':
model = testing_utils.get_small_functional_mlp(1, 4, input_dim=3)
elif model == 'subclass':
model = testing_utils.get_small_sequential_mlp(1, 4)
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer, loss, metrics=metrics)
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
iterator = dataset_ops.make_one_shot_iterator(dataset)
model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1)
model.evaluate(iterator, steps=2, verbose=1)
model.predict(iterator, steps=2)
# Test with validation data
model.fit(iterator,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_data=iterator,
validation_steps=2)
# Test with validation split
with self.assertRaisesRegexp(
ValueError, '`validation_split` argument is not supported '
'when input `x` is a dataset or a dataset iterator'):
model.fit(iterator,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_split=0.5,
validation_steps=2)
# Test with sample weight.
sample_weight = np.random.random((10, ))
with self.assertRaisesRegexp(
ValueError, '`sample_weight` argument is not supported '
'when input `x` is a dataset or a dataset iterator'):
model.fit(iterator,
epochs=1,
steps_per_epoch=2,
verbose=0,
sample_weight=sample_weight)
# Test invalid usage
with self.assertRaisesRegexp(ValueError,
'you should not specify a target'):
model.fit(iterator,
iterator,
epochs=1,
steps_per_epoch=2,
verbose=0)
with self.assertRaisesRegexp(
ValueError,
'you should specify the `steps_per_epoch` argument'):
model.fit(iterator, epochs=1, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'you should specify the `steps` argument'):
model.evaluate(iterator, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'you should specify the `steps` argument'):
model.predict(iterator, verbose=0)
def DISABLED_test_function_model_multiple_feature_layer_inputs(self):
col_a = fc.numeric_column('a')
col_b = fc.numeric_column('b')
col_c = fc.numeric_column('c')
fc1 = df.DenseFeatures([col_a, col_b], name='fc1')
fc2 = df.DenseFeatures([col_b, col_c], name='fc2')
dense = keras.layers.Dense(4)
# This seems problematic.... We probably need something for DenseFeatures
# the way Input is for InputLayer.
output = dense(fc1) + dense(fc2)
model = keras.models.Model([fc1, fc2], [output])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(optimizer,
loss,
metrics=[metrics_module.CategoricalAccuracy(), 'mae'],
loss_weights=loss_weights)
data_list = ([{
'a': np.arange(10),
'b': np.arange(10)
}, {
'b': np.arange(10),
'c': np.arange(10)
}], np.arange(10, 100))
model.fit(*data_list, epochs=1)
data_bloated_list = ([{
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
}, {
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
}], np.arange(10, 100))
model.fit(*data_bloated_list, epochs=1)
data_dict = ({
'fc1': {
'a': np.arange(10),
'b': np.arange(10)
},
'fc2': {
'b': np.arange(10),
'c': np.arange(10)
}
}, np.arange(10, 100))
model.fit(*data_dict, epochs=1)
data_bloated_dict = ({
'fc1': {
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
},
'fc2': {
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
}
}, np.arange(10, 100))
model.fit(*data_bloated_dict, epochs=1)
def test_training_and_eval_methods_on_dataset(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(),
experimental_run_tf_function=testing_utils.
should_run_tf_function())
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat() # Infinite dataset.
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)
# Test with validation data
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_data=dataset,
validation_steps=2)
# Test with validation split
with self.assertRaisesRegexp(
ValueError,
'`validation_split` argument is not supported when '):
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_split=0.5,
validation_steps=2)
# Test with sample weight.
sample_weight = np.random.random((10, ))
with self.assertRaisesRegexp(
ValueError,
r'`sample_weight` argument is not supported .+dataset'):
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
sample_weight=sample_weight)
# Test invalid usage
with self.assertRaisesRegexp(
ValueError, 'The `batch_size` argument must not be specified'):
model.fit(dataset,
batch_size=10,
epochs=1,
steps_per_epoch=2,
verbose=0)
with self.assertRaisesRegexp(
ValueError, 'The `batch_size` argument must not be specified'):
model.predict(dataset, batch_size=10, steps=2, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'The `batch_size` argument must not be specified'):
model.evaluate(dataset, batch_size=10, steps=2, verbose=0)
with self.assertRaisesRegexp(
ValueError, '(you should not specify a target)|'
'(`y` argument is not supported when using dataset as input.)'
):
model.fit(dataset, dataset, epochs=1, steps_per_epoch=2, verbose=0)
# With an infinite dataset, `steps_per_epoch`/`steps` argument is required.
with self.assertRaisesRegexp(ValueError,
'the `steps_per_epoch` argument'):
model.fit(dataset, epochs=1, verbose=0)
with self.assertRaisesRegexp(ValueError, 'the `steps` argument'):
model.evaluate(dataset, verbose=0)
with self.assertRaisesRegexp(ValueError, 'the `steps` argument'):
model.predict(dataset, verbose=0)
def test_training_and_eval_methods_on_dataset(self):
model = testing_utils.get_small_mlp(1, 4, input_dim=3)
optimizer = RMSPropOptimizer(learning_rate=0.001)
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)
dataset = dataset_ops.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)
model.evaluate(dataset, steps=2, verbose=1)
model.predict(dataset, steps=2)
# Test with validation data
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_data=dataset,
validation_steps=2)
# Test with validation split
with self.assertRaisesRegexp(
ValueError, '`validation_split` argument is not supported '
'when input `x` is a dataset or a dataset iterator'):
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
validation_split=0.5,
validation_steps=2)
# Test with sample weight.
sample_weight = np.random.random((10, ))
with self.assertRaisesRegexp(
ValueError, '`sample_weight` argument is not supported '
'when input `x` is a dataset or a dataset iterator'):
model.fit(dataset,
epochs=1,
steps_per_epoch=2,
verbose=0,
sample_weight=sample_weight)
# Test invalid usage
with self.assertRaisesRegexp(
ValueError, 'The `batch_size` argument'
' must not be specified when using dataset'
' as an input.'):
model.fit(dataset,
batch_size=10,
epochs=1,
steps_per_epoch=2,
verbose=0)
with self.assertRaisesRegexp(
ValueError, 'The `batch_size` argument'
' must not be specified when using dataset'
' as an input.'):
model.predict(dataset, batch_size=10, steps=2, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'The `batch_size` argument'
' must not be specified when using dataset'
' as an input.'):
model.evaluate(dataset, batch_size=10, steps=2, verbose=0)
with self.assertRaisesRegexp(ValueError,
'you should not specify a target'):
model.fit(dataset, dataset, epochs=1, steps_per_epoch=2, verbose=0)
with self.assertRaisesRegexp(ValueError,
'the `steps_per_epoch` argument'):
model.fit(dataset, epochs=1, verbose=0)
with self.assertRaisesRegexp(ValueError, 'the `steps` argument'):
model.evaluate(dataset, verbose=0)
with self.assertRaisesRegexp(ValueError, 'the `steps` argument'):
model.predict(dataset, verbose=0)
def test_model_methods_with_eager_tensors_multi_io(self):
a = keras.layers.Input(shape=(3, ), name='input_a')
b = keras.layers.Input(shape=(3, ), name='input_b')
dense = keras.layers.Dense(4, name='dense')
c = dense(a)
d = dense(b)
e = keras.layers.Dropout(0.5, name='dropout')(c)
model = keras.models.Model([a, b], [d, e])
optimizer = RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
loss_weights = [1., 0.5]
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer,
loss,
metrics=metrics,
loss_weights=loss_weights,
sample_weight_mode=None)
input_a = keras.backend.zeros(shape=(10, 3))
input_b = keras.backend.zeros(shape=(10, 3))
target_d = keras.backend.zeros(shape=(10, 4))
target_e = keras.backend.zeros(shape=(10, 4))
model.fit([input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0)
# Test: no shuffle.
model.fit([input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
# Test: validation data.
model.fit([input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=2,
verbose=0,
validation_data=([input_a, input_b], [target_d, target_e]))
model.train_on_batch([input_a, input_b], [target_d, target_e])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_d, target_e],
batch_size=2,
verbose=0)
model.test_on_batch([input_a, input_b], [target_d, target_e])
# Test: mix np and tensors.
input_b = np.zeros(shape=(10, 3)).astype('float32')
target_e = np.zeros(shape=(10, 4)).astype('float32')
model.fit([input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0)
model.fit([input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=2,
verbose=0,
validation_data=([input_a, input_b], [target_d, target_e]))
model.fit([input_a, input_b], [target_d, target_e],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
model.train_on_batch([input_a, input_b], [target_d, target_e])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_d, target_e],
batch_size=2,
verbose=0)
model.test_on_batch([input_a, input_b], [target_d, target_e])
encoded_text =converttoInt(text,vocab)
encoded_texts.append(encoded_text)
print("encoded_text example: ",encoded_texts[0])
print("decoded_text example: ",[int_to_vocab[char] for char in encoded_texts[0]])
#decoded_text = [int_to_vocab[char] for char in encoded_texts[0]]
#loss_object = tf.keras.losses.categorical_crossentropy()
optimizer = tf.keras.optimizers.Adam(lr=0.001)
# track the evolution
# Loss
train_loss = metrics.Mean(name='train_loss')
valid_loss = metrics.Mean(name='valid_loss')
# Accuracy
train_accuracy = metrics.CategoricalAccuracy(name='train_accuracy')
valid_accuracy = metrics.CategoricalAccuracy(name='valid_accuracy')
model = createModel()
model.summary()
epochs = 15
batch_size = 32
actual_batch = 0
model.reset_states()
for epoch in range(epochs):
print("\n epoch :", epoch)
model.reset_states()
actual_batch = 0
for songs, targets in get_batches(mfccs, encoded_texts,batch_size):
def test_generator_methods(self):
arr_data = np.random.random((50, 2))
arr_labels = np.random.random((50, ))
def custom_generator():
batch_size = 10
num_samples = 50
while True:
batch_index = np.random.randint(0, num_samples - batch_size)
start = batch_index
end = start + batch_size
x = arr_data[start:end]
y = arr_labels[start:end]
yield x, y
with self.cached_session():
x = keras.Input((2, ))
y = keras.layers.Dense(1)(x)
fn_model = keras.models.Model(x, y)
fn_model.compile(
loss='mse',
optimizer='sgd',
metrics=['mae', metrics_module.CategoricalAccuracy()])
seq_model = keras.models.Sequential()
seq_model.add(keras.layers.Dense(1, input_shape=(2, )))
seq_model.compile(loss='mse', optimizer='sgd')
for model in [fn_model, seq_model]:
model.fit_generator(custom_generator(),
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)
model.predict_generator(custom_generator(),
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)
model.evaluate_generator(custom_generator(),
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_training_and_eval_methods_on_iterators_single_io(self):
if testing_utils.should_run_distributed():
self.skipTest('b/137397816')
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(),
run_distributed=testing_utils.should_run_distributed())
inputs = np.zeros((10, 3), np.float32)
targets = np.zeros((10, 4), np.float32)
dataset = dataset_ops.Dataset.from_tensor_slices((inputs, targets))
dataset = dataset.repeat(100)
dataset = dataset.batch(10)
iterator = dataset_ops.make_one_shot_iterator(dataset)
model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1)
model.evaluate(iterator, steps=2, verbose=1)
model.predict(iterator, steps=2)
# Test with validation data
model.fit(iterator,
epochs=1, steps_per_epoch=2, verbose=0,
validation_data=iterator, validation_steps=2)
# Test with validation split
with self.assertRaisesRegexp(
ValueError, '`validation_split` argument is not supported '
'when input `x` is a dataset or a dataset iterator'):
model.fit(iterator,
epochs=1, steps_per_epoch=2, verbose=0,
validation_split=0.5, validation_steps=2)
# Test with sample weight.
sample_weight = np.random.random((10,))
with self.assertRaisesRegexp(
ValueError, '`sample_weight` argument is not supported '
'when input `x` is a dataset or a dataset iterator'):
model.fit(
iterator,
epochs=1,
steps_per_epoch=2,
verbose=0,
sample_weight=sample_weight)
# Test invalid usage
with self.assertRaisesRegexp(ValueError,
'you should not specify a target'):
model.fit(iterator, iterator,
epochs=1, steps_per_epoch=2, verbose=0)
with self.assertRaisesRegexp(
ValueError, 'the `steps_per_epoch` argument'):
model.fit(iterator, epochs=1, verbose=0)
with self.assertRaisesRegexp(ValueError,
'the `steps` argument'):
model.evaluate(iterator, verbose=0)
with self.assertRaisesRegexp(ValueError,
'the `steps` argument'):
model.predict(iterator, verbose=0)