def input_fn(is_training, data_dir, batch_size, *args, **kwargs): # pylint: disable=unused-argument
return model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape(
[batch_size, height, width, num_channels]),
input_dtype=tf.float32,
label_shape=tf.TensorShape([batch_size]),
label_dtype=tf.int32)
def _generate_synthetic_data(params):
"""Create synthetic data based on the parameter batch size."""
batch = length = int(math.sqrt(params["batch_size"]))
return model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([batch, length]),
input_value=1,
input_dtype=tf.int32,
label_shape=tf.TensorShape([batch, length]),
label_value=1,
label_dtype=tf.int32,
)
def _generate_synthetic_data(params):
"""Create synthetic data based on the parameter batch size."""
batch = length = int(math.sqrt(params["batch_size"]))
return model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([batch, length]),
input_value=1,
input_dtype=tf.int32,
label_shape=tf.TensorShape([batch, length]),
label_value=1,
label_dtype=tf.int32,
)
def generate_synthetic_input_dataset(model, batch_size):
"""Generate synthetic dataset."""
image_size = _get_default_image_size(model)
image_shape = (batch_size,) + image_size + (_NUM_CHANNELS,)
label_shape = (batch_size, _NUM_CLASSES)
dataset = model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape(image_shape),
label_shape=tf.TensorShape(label_shape),
)
return dataset
def _generate_synthetic_data(params):
"""Create synthetic data based on the parameter batch size."""
batch = length = int(math.sqrt(params["batch_size"]))
dataset = model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([length]),
input_value=1,
input_dtype=tf.int64,
label_shape=tf.TensorShape([length]),
label_value=1,
label_dtype=tf.int64,
)
return dataset.batch(batch, drop_remainder=True)
def test_generate_only_input_data(self):
d = model_helpers.generate_synthetic_data(input_shape=tf.TensorShape(
[4]),
input_value=43.5,
input_dtype=tf.float32)
element = tf.compat.v1.data.make_one_shot_iterator(d).get_next()
self.assertFalse(isinstance(element, tuple))
with self.session() as sess:
inp = sess.run(element)
self.assertAllClose(inp, [43.5, 43.5, 43.5, 43.5])
def test_generate_synethetic_data(self):
input_element, label_element = model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([5]),
input_value=123,
input_dtype=tf.float32,
label_shape=tf.TensorShape([]),
label_value=456,
label_dtype=tf.int32).make_one_shot_iterator().get_next()
with self.test_session() as sess:
for n in range(5):
inp, lab = sess.run((input_element, label_element))
self.assertAllClose(inp, [123., 123., 123., 123., 123.])
self.assertEquals(lab, 456)
def _generate_synthetic_data(params):
"""Create synthetic data based on the parameter batch size."""
batch_size = int(params["batch_size"] // params["max_length"])
length = params["max_length"]
dataset = model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([length]),
input_value=1,
input_dtype=tf.int64,
label_shape=tf.TensorShape([length]),
label_value=1,
label_dtype=tf.int64,
)
if params["static_batch"]:
dataset = dataset.batch(batch_size, drop_remainder=True)
else:
dataset = dataset.padded_batch(batch_size, ([None], [None]))
return dataset
def test_generate_nested_data(self):
d = model_helpers.generate_synthetic_data(
input_shape={'a': tf.TensorShape([2]),
'b': {'c': tf.TensorShape([3]), 'd': tf.TensorShape([])}},
input_value=1.1)
element = tf.compat.v1.data.make_one_shot_iterator(d).get_next()
self.assertIn('a', element)
self.assertIn('b', element)
self.assertEquals(len(element['b']), 2)
self.assertIn('c', element['b'])
self.assertIn('d', element['b'])
self.assertNotIn('c', element)
with self.test_session() as sess:
inp = sess.run(element)
self.assertAllClose(inp['a'], [1.1, 1.1])
self.assertAllClose(inp['b']['c'], [1.1, 1.1, 1.1])
self.assertAllClose(inp['b']['d'], 1.1)
def input_fn(is_training, data_dir, batch_size, *args, **kwargs): # pylint: disable=unused-argument
return model_helpers.generate_synthetic_data(
input_shape=tf.TensorShape([batch_size, height, width, num_channels]),
input_dtype=tf.float32,
label_shape=tf.TensorShape([batch_size]),
label_dtype=tf.int32)
