def create_cond_critic_proj(cls, xinput, input_clusters, var_scope,
critic_layers, clusters_no, reuse=None):
"""
Class method that instantiates a Critic and creates a conditional
critic with the original projection conditioning method.
Parameters
----------
xinput : Tensor
Tensor containing the input cells.
input_clusters : Tensor
Tensor containing the corresponding cluster indexes of the input cells.
var_scope : str
Variable scope used for the created tensors.
critic_layers : list
List of integers corresponding to the number of neurons of each
layer of the critic.
clusters_no : int
Number of clusters.
reuse : Boolean
Whether to reuse the already existing Tensors.
Default is None.
Returns
-------
A Creator object with the defined architecture.
"""
with tf.variable_scope(var_scope, reuse=reuse):
for i_lay, output_size in enumerate(critic_layers):
with tf.variable_scope("layers_" + str(i_lay + 1)):
xinput = layers.relu(
xinput,
output_size,
weights_initializer=layers.variance_scaling_initializer(mode="FAN_AVG"),
biases_initializer=tf.zeros_initializer())
with tf.variable_scope("layers_" + 'proj'):
proj_weights_m = tf.get_variable(
"proj_weights_m",
[clusters_no, critic_layers[-1], 1],
dtype=tf.float32, initializer=layers.xavier_initializer())
proj_weights = tf.nn.embedding_lookup(proj_weights_m,
input_clusters)
output_proj = tf.einsum('ij,ijk->ik', xinput, proj_weights)
with tf.variable_scope("layers_" + 'output'):
output = layers.linear(
xinput, 1,
weights_initializer=layers.xavier_initializer(),
biases_initializer=tf.zeros_initializer())
dist = tf.add(output_proj, output)
return cls(xinput, dist, var_scope, critic_layers,
input_clusters=input_clusters,
clusters_no=clusters_no, reuse=reuse)
def create_critic(cls, xinput, var_scope, critic_layers, reuse=None):
"""
Class method that instantiates a Critic and creates a
non-conditional critic.
Parameters
----------
xinput : Tensor
Tensor containing the input cells.
var_scope : str
Variable scope used for the created tensors.
critic_layers : list
List of integers corresponding to the number of neurons of each
layer of the critic.
reuse : Boolean
Whether to reuse the already existing Tensors.
Default is None.
Returns
-------
A Creator object with the defined architecture.
"""
with tf.variable_scope(var_scope, reuse=reuse):
for i_lay, output_size in enumerate(critic_layers):
with tf.variable_scope("layers_" + str(i_lay + 1)):
xinput = layers.relu(
xinput,
output_size,
weights_initializer=layers.
variance_scaling_initializer(mode="FAN_AVG"),
biases_initializer=tf.zeros_initializer())
with tf.variable_scope("layers_" + 'output'):
output = layers.linear(
xinput,
1,
weights_initializer=layers.xavier_initializer(),
biases_initializer=tf.zeros_initializer())
return cls(xinput, output, var_scope, critic_layers, reuse=reuse)
def create_cond_generator(cls, z_input, batch_size, latent_dim,
output_cells_dim, var_scope, gen_layers,
output_lsn, gen_cond_type, clusters_ratios,
is_training, clusters_no=None,
input_clusters=None, reuse=None):
"""
Class method that instantiates a Generator and creates a
conditional generator.
Parameters
----------
z_input : Tensor
Tensor containing the noise used as input by the generator.
batch_size : int
Batch size used during the training.
latent_dim : int
Dimension of the latent space used from which the input noise
of the generator is sampled.
output_cells_dim : int
Dimension of the output cells (i.e. the number of genes).
var_scope : str
Variable scope used for the created tensors.
gen_layers : list
List of integers corresponding to the number of neurons of
each layer of the generator.
output_lsn : int, None
Parameter of the LSN layer at the output of the critic
(i.e. total number of counts per generated cell).
gen_cond_type : str
conditional normalization layers used in the generator, can be
either "batchnorm" or "layernorm". If anything else, it won't be
added in the model (which means no conditional generation).
clusters_ratios : Tensor
Placeholder containing the list of cluster ratios of the input data.
is_training : Tensor
Boolean placeholder encoding for whether we're in training or
inference mode (for the batch normalization).
clusters_no : int
Number of clusters.
Default is None.
input_clusters : Tensor
Placeholders for the cluster indexes that should be used for
conditional generation.
Default is None.
reuse : Boolean
Whether to reuse the already existing Tensors.
Default is None.
Returns
-------
A conditional Generator object with the defined architecture.
"""
with tf.variable_scope(var_scope, reuse=reuse):
for i_lay, size in enumerate(gen_layers):
with tf.variable_scope("generator_layers_" + str(i_lay + 1)):
z_input = layers.linear(
z_input,
size,
weights_initializer=layers.xavier_initializer(),
biases_initializer=None)
if i_lay != -1:
if gen_cond_type == "batchnorm":
z_input = batchnorm(
[0], z_input,
is_training=is_training,
labels=input_clusters,
n_labels=clusters_no)
elif gen_cond_type == "layernorm":
z_input = layernorm([1],
z_input,
labels=input_clusters,
n_labels=clusters_no)
z_input = tf.nn.relu(z_input)
with tf.variable_scope("generator_layers_" + 'output'):
fake_outputs = layers.relu(
z_input, output_cells_dim,
weights_initializer=layers.variance_scaling_initializer(mode="FAN_AVG"),
biases_initializer=tf.zeros_initializer())
if output_lsn is not None:
gammas_output = tf.Variable(
np.ones(z_input.shape.as_list()[0]) * output_lsn,
trainable=False)
sigmas = tf.reduce_sum(fake_outputs, axis=1)
scale_ls = tf.cast(gammas_output, dtype=tf.float32) / \
(sigmas + sys.float_info.epsilon)
fake_outputs = tf.transpose(tf.transpose(fake_outputs) *
scale_ls)
return cls(fake_outputs, batch_size, latent_dim, output_cells_dim,
var_scope, gen_layers, output_lsn,
gen_cond_type=gen_cond_type, is_training=is_training,
clusters_ratios=clusters_ratios, clusters_no=clusters_no,
input_clusters=input_clusters, reuse=reuse)