def __init__(self,
name,
ndim_x,
ndim_y,
n_centers=10,
hidden_sizes=(16, 16),
hidden_nonlinearity=tf.nn.tanh,
n_training_epochs=1000,
x_noise_std=None,
y_noise_std=None,
adaptive_noise_fn=None,
entropy_reg_coef=0.0,
weight_decay=0.0,
weight_normalization=True,
data_normalization=True,
dropout=0.0,
l2_reg=0.0,
l1_reg=0.0,
random_seed=None):
Serializable.quick_init(self, locals())
self._check_uniqueness_of_scope(name)
self.name = name
self.ndim_x = ndim_x
self.ndim_y = ndim_y
self.random_seed = random_seed
self.random_state = np.random.RandomState(seed=random_seed)
tf.set_random_seed(random_seed)
self.n_centers = n_centers
self.hidden_sizes = hidden_sizes
self.hidden_nonlinearity = hidden_nonlinearity
self.n_training_epochs = n_training_epochs
# regularization parameters
self.x_noise_std = x_noise_std
self.y_noise_std = y_noise_std
self.entropy_reg_coef = entropy_reg_coef
self.adaptive_noise_fn = adaptive_noise_fn
self.weight_decay = weight_decay
self.l2_reg = l2_reg
self.l1_reg = l1_reg
self.weight_normalization = weight_normalization
self.data_normalization = data_normalization
self.dropout = dropout
self.can_sample = True
self.has_pdf = True
self.has_cdf = True
self.fitted = False
# build tensorflow model
self._build_model()
def __init__(self, name, ndim_x, ndim_y, flows_type=None, n_flows=10, hidden_sizes=(16, 16),
hidden_nonlinearity=tf.tanh, n_training_epochs=1000, x_noise_std=None, y_noise_std=None, adaptive_noise_fn=None,
weight_decay=0.0, weight_normalization=True, data_normalization=True, dropout=0.0, l2_reg=0.0, l1_reg=0.0,
random_seed=None):
Serializable.quick_init(self, locals())
self._check_uniqueness_of_scope(name)
self.name = name
self.ndim_x = ndim_x
self.ndim_y = ndim_y
self.random_seed = random_seed
self.random_state = np.random.RandomState(seed=random_seed)
tf.set_random_seed(random_seed)
# charateristics of the flows to be used
if flows_type is None:
flows_type = ['affine'] + ['radial' for _ in range(n_flows)]
assert all([f in FLOWS.keys() for f in flows_type])
self.flows_type = flows_type
# specification of the network
self.hidden_sizes = hidden_sizes
self.hidden_nonlinearity = hidden_nonlinearity
self.n_training_epochs = n_training_epochs
# regularization parameters
self.x_noise_std = x_noise_std
self.y_noise_std = y_noise_std
self.adaptive_noise_fn = adaptive_noise_fn
# decoupled weight decay
self.weight_decay = weight_decay
# l1 / l2 regularization
self.l2_reg = l2_reg
self.l1_reg = l1_reg
# normalizing the network weights
self.weight_normalization = weight_normalization
# whether to normalize the data to zero mean, and uniform variance
self.data_normalization = data_normalization
# the prob of dropping a node
self.dropout = dropout
# gradients for planar flows tend to explode -> clip them by global norm
self.gradient_clipping = True if 'planar' in flows_type else False
# as we'll be using reversed flows, sampling is too slow to be useful
self.can_sample = False
self.has_pdf = True
# tf has a cdf implementation only for 1-D Normal Distribution
self.has_cdf = True if self.ndim_y == 1 else False
self.fitted = False
# build tensorflow model
self._build_model()
def __init__(self,
name,
output_dim,
hidden_sizes,
hidden_nonlinearity,
output_nonlinearity,
hidden_W_init=L.XavierUniformInitializer(),
hidden_b_init=tf.zeros_initializer(),
output_W_init=L.XavierUniformInitializer(),
output_b_init=tf.zeros_initializer(),
input_var=None,
input_layer=None,
input_shape=None,
batch_normalization=False,
weight_normalization=False,
dropout_ph=None):
"""
:param dropout_ph: None if no dropout should be used. Else a scalar placeholder that determines the prob of dropping a node.
Remember to set placeholder to Zero during test / eval
"""
Serializable.quick_init(self, locals())
with tf.variable_scope(name):
if input_layer is None:
l_in = L.InputLayer(shape=(None, ) + input_shape,
input_var=input_var,
name="input")
else:
l_in = input_layer
self._layers = [l_in]
l_hid = l_in
if batch_normalization:
l_hid = L.batch_norm(l_hid)
for idx, hidden_size in enumerate(hidden_sizes):
l_hid = L.DenseLayer(l_hid,
num_units=hidden_size,
nonlinearity=hidden_nonlinearity,
name="hidden_%d" % idx,
W=hidden_W_init,
b=hidden_b_init,
weight_normalization=weight_normalization)
if dropout_ph is not None:
l_hid = L.DropoutLayer(l_hid, dropout_ph, rescale=False)
if batch_normalization:
l_hid = L.batch_norm(l_hid)
self._layers.append(l_hid)
l_out = L.DenseLayer(l_hid,
num_units=output_dim,
nonlinearity=output_nonlinearity,
name="output",
W=output_W_init,
b=output_b_init,
weight_normalization=weight_normalization)
if batch_normalization:
l_out = L.batch_norm(l_out)
self._layers.append(l_out)
self._l_in = l_in
self._l_out = l_out
# self._input_var = l_in.input_var
self._output = L.get_output(l_out)
LayersPowered.__init__(self, l_out)
def __init__(self,
name,
ndim_x,
ndim_y,
center_sampling_method='k_means',
n_centers=50,
keep_edges=True,
init_scales='default',
hidden_sizes=(16, 16),
hidden_nonlinearity=tf.nn.tanh,
train_scales=True,
n_training_epochs=1000,
x_noise_std=None,
y_noise_std=None,
adaptive_noise_fn=None,
entropy_reg_coef=0.0,
weight_decay=0.0,
weight_normalization=True,
data_normalization=True,
dropout=0.0,
l2_reg=0.0,
l1_reg=0.0,
random_seed=None):
Serializable.quick_init(self, locals())
self._check_uniqueness_of_scope(name)
self.name = name
self.ndim_x = ndim_x
self.ndim_y = ndim_y
self.random_seed = random_seed
self.random_state = np.random.RandomState(seed=random_seed)
tf.set_random_seed(random_seed)
self.n_centers = n_centers
self.hidden_sizes = hidden_sizes
self.hidden_nonlinearity = hidden_nonlinearity
self.n_training_epochs = n_training_epochs
# center sampling parameters
self.center_sampling_method = center_sampling_method
self.keep_edges = keep_edges
# regularization parameters
self.x_noise_std = x_noise_std
self.y_noise_std = y_noise_std
self.adaptive_noise_fn = adaptive_noise_fn
self.entropy_reg_coef = entropy_reg_coef
self.weight_decay = weight_decay
self.l2_reg = l2_reg
self.l1_reg = l1_reg
self.weight_normalization = weight_normalization
self.data_normalization = data_normalization
self.dropout = dropout
if init_scales == 'default':
init_scales = np.array([0.7, 0.3])
self.n_scales = len(init_scales)
self.train_scales = train_scales
self.init_scales = init_scales
# Transform scales so that the softplus will result in passed init_scales
self.init_scales_softplus = [
np.log(np.exp(s) - 1) for s in init_scales
]
self.can_sample = True
self.has_pdf = True
self.has_cdf = True
self.fitted = False
# build tensorflow model
self._build_model()