def _build_generator_given_z(self):
z = Input(shape=(self.z_dim, ), name='z')
z_bits = Subtensor(*self.pos_z_bits, axis=1)(z)
z_labels = Subtensor(*self.pos_z_labels, axis=1)(z)
z_offset = Subtensor(*self.pos_z_offset, axis=1)(z)
bits = ThresholdBits()(z_bits)
nb_labels_without_bits = self.labels_shape[0] - self.nb_bits
generated_labels = get_label_generator(
z_labels,
self.generator_units,
nb_output_units=nb_labels_without_bits)
labels_normed = NormSinCosAngle(0)(generated_labels)
labels = concat([bits, labels_normed], name='labels')
fake = self.generator_given_z_and_labels([z_offset, labels])
self.generator_given_z = Model([z], [fake])
sample_tensors = self.sample_generator_given_z_and_labels(
[z_offset, labels])
sample_tensors = [
name_tensor(t, n) for t, n in zip(
sample_tensors,
self.sample_generator_given_z_and_labels.output_names)
]
self.sample_generator_given_z_output_names = ['labels'] + \
self.sample_generator_given_z_and_labels_output_names
self.sample_generator_given_z = Model([z], [labels] + sample_tensors)
def test_get_mask_driver():
batch_shape = (64, 22)
n = 50
input = Input(shape=batch_shape[1:])
output = get_label_generator(input, nb_units=n, nb_output_units=n)
model = Model(input, output)
model.compile('adam', 'mse')
x = np.random.sample(batch_shape)
y = np.random.sample((64, n))
model.train_on_batch(x, y)
def test_get_mask_driver():
batch_shape = (64, 22)
n = 50
input = Input(shape=batch_shape[1:])
output = get_label_generator(input, nb_units=n, nb_output_units=n)
model = Model(input, output)
model.compile('adam', 'mse')
x = np.random.sample(batch_shape)
y = np.random.sample((64, n))
model.train_on_batch(x, y)
def _build_generator_given_z(self):
z = Input(shape=(self.z_dim,), name='z')
z_bits = Subtensor(*self.pos_z_bits, axis=1)(z)
z_labels = Subtensor(*self.pos_z_labels, axis=1)(z)
z_offset = Subtensor(*self.pos_z_offset, axis=1)(z)
bits = ThresholdBits()(z_bits)
nb_labels_without_bits = self.labels_shape[0] - self.nb_bits
generated_labels = get_label_generator(
z_labels, self.generator_units, nb_output_units=nb_labels_without_bits)
labels_normed = NormSinCosAngle(0)(generated_labels)
labels = concat([bits, labels_normed], name='labels')
fake = self.generator_given_z_and_labels([z_offset, labels])
self.generator_given_z = Model([z], [fake])
sample_tensors = self.sample_generator_given_z_and_labels([z_offset, labels])
sample_tensors = [name_tensor(t, n)
for t, n in zip(sample_tensors,
self.sample_generator_given_z_and_labels.output_names)]
self.sample_generator_given_z_output_names = ['labels'] + \
self.sample_generator_given_z_and_labels_output_names
self.sample_generator_given_z = Model([z], [labels] + sample_tensors)
