def test_normal_output_without_signals(self):
num_samples = 4
batch_size = 2
params = {'batch_size': batch_size}
input_fn, (a, b) = make_input_fn(num_samples=num_samples)
with ops.Graph().as_default():
dataset = input_fn(params)
features = dataset_lib.make_one_shot_iterator(dataset).get_next()
# With tf.data.Dataset.batch, the batch is None, i.e., dynamic shape.
self.assertIsNone(features['a'].shape.as_list()[0])
with session.Session() as sess:
result = sess.run(features)
self.assertAllEqual(a[:batch_size], result['a'])
self.assertAllEqual(b[:batch_size], result['b'])
# This run should work as num_samples / batch_size = 2.
result = sess.run(features)
self.assertAllEqual(a[batch_size:num_samples], result['a'])
self.assertAllEqual(b[batch_size:num_samples], result['b'])
with self.assertRaises(errors.OutOfRangeError):
# Given num_samples and batch_size, this run should fail.
sess.run(features)
def test_normal_output_without_signals(self):
num_samples = 4
batch_size = 2
params = {'batch_size': batch_size}
input_fn, (a, b) = make_input_fn(num_samples=num_samples)
with ops.Graph().as_default():
dataset = input_fn(params)
features = dataset_lib.make_one_shot_iterator(dataset).get_next()
# With tf.data.Dataset.batch, the batch is None, i.e., dynamic shape.
self.assertIsNone(features['a'].shape.as_list()[0])
with session.Session() as sess:
result = sess.run(features)
self.assertAllEqual(a[:batch_size], result['a'])
self.assertAllEqual(b[:batch_size], result['b'])
# This run should work as num_samples / batch_size = 2.
result = sess.run(features)
self.assertAllEqual(a[batch_size:num_samples], result['a'])
self.assertAllEqual(b[batch_size:num_samples], result['b'])
with self.assertRaises(errors.OutOfRangeError):
# Given num_samples and batch_size, this run should fail.
sess.run(features)
def _EvalInputFn():
mnist_x, mnist_y = test_data
dataset = data.Dataset.from_tensor_slices((mnist_x, mnist_y))
dataset = dataset.apply(
data.experimental.map_and_batch(map_func=_PreprocessFn,
batch_size=batch_size,
num_parallel_calls=8))
dataset = dataset.repeat(count=1)
iterator = data.make_one_shot_iterator(dataset)
features, labels = iterator.get_next()
return features, labels
def _EvalInputFn():
mnist_x, mnist_y = test_data
dataset = data.Dataset.from_tensor_slices((mnist_x, mnist_y))
dataset = dataset.apply(
data.experimental.map_and_batch(
map_func=_PreprocessFn,
batch_size=batch_size,
num_parallel_calls=8))
dataset = dataset.repeat(count=1)
iterator = data.make_one_shot_iterator(dataset)
features, labels = iterator.get_next()
return features, labels
def test_nested_model_with_tensor_input(self):
gpus = 2
input_dim = 10
shape = (input_dim, )
num_samples = 16
num_classes = 10
if not check_if_compatible_devices(gpus=gpus):
return
with self.cached_session():
input_shape = (num_samples, ) + shape
x_train = np.random.randint(0, 255, input_shape)
y_train = np.random.randint(0, num_classes, (input_shape[0], ))
keras.backend.set_learning_phase(True)
y_train = keras.utils.to_categorical(y_train, num_classes)
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
dataset = data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.repeat()
dataset = dataset.batch(4)
iterator = data.make_one_shot_iterator(dataset)
inputs, targets = iterator.get_next()
input_tensor = keras.layers.Input(tensor=inputs)
model = keras.models.Sequential()
model.add(keras.layers.Dense(3, input_shape=(input_dim, )))
model.add(keras.layers.Dense(num_classes))
output = model(input_tensor)
outer_model = keras.Model(input_tensor, output)
parallel_model = keras.utils.multi_gpu_model(outer_model,
gpus=gpus)
parallel_model.compile(loss='categorical_crossentropy',
optimizer=keras.optimizers.RMSprop(
lr=0.0001, decay=1e-6),
metrics=['accuracy'],
target_tensors=[targets])
parallel_model.fit(epochs=1, steps_per_epoch=3)
def test_nested_model_with_tensor_input(self):
gpus = 2
input_dim = 10
shape = (input_dim,)
num_samples = 16
num_classes = 10
if not check_if_compatible_devices(gpus=gpus):
return
with self.cached_session():
input_shape = (num_samples,) + shape
x_train = np.random.randint(0, 255, input_shape)
y_train = np.random.randint(0, num_classes, (input_shape[0],))
keras.backend.set_learning_phase(True)
y_train = keras.utils.to_categorical(y_train, num_classes)
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
dataset = data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.repeat()
dataset = dataset.batch(4)
iterator = data.make_one_shot_iterator(dataset)
inputs, targets = iterator.get_next()
input_tensor = keras.layers.Input(tensor=inputs)
model = keras.models.Sequential()
model.add(keras.layers.Dense(3,
input_shape=(input_dim,)))
model.add(keras.layers.Dense(num_classes))
output = model(input_tensor)
outer_model = keras.Model(input_tensor, output)
parallel_model = keras.utils.multi_gpu_model(outer_model, gpus=gpus)
parallel_model.compile(
loss='categorical_crossentropy',
optimizer=keras.optimizers.RMSprop(lr=0.0001, decay=1e-6),
metrics=['accuracy'],
target_tensors=[targets])
parallel_model.fit(epochs=1, steps_per_epoch=3)