class RLVM(Network):
"""Tensorflow (tf) implementation of RLVM class
Attributes:
network (FFNetwork object): autoencoder network
input_size (int): size of input
noise_dist (str): noise distribution used for cost function
activations (list of tf ops): evaluates the layer-wise activations of
the model
cost (tf op): evaluates the cost function of the model
cost_reg (tf op): evaluates the regularization penalty of the model
cost_penalized (tf op): evaluates sum of `cost` and `cost_reg`
train_step (tf op): evaluates one training step using the specified
cost function and learning algorithm
data_in_ph (tf.placeholder): placeholder for input data
data_out_ph (tf.placeholder): placeholder for output data
data_in_var (tf.Variable): Variable for input data fed by data_in_ph
data_out_var (tf.Variable): Variable for output data fed by data_out_ph
data_in_batch (tf.Variable): batched version of data_in_var
data_out_batch (tf.Variable): batched version of data_out_var
indices (tf.placeholder): placeholder for indices into data_in/out_var
to creat data_in/out_batch
learning_alg (str): algorithm used for learning parameters
learning_rate (float): learning rate used by the gradient
descent-based optimizers
num_examples (int): total number of examples in training data
use_batches (bool): all training data is placed on the GPU by
piping data into i/o variables through a feed_dict. If use_batches
is False, all data from these variables are used during training.
If use_batches is True, the a separate index feed_dict is used to
specify which chunks of data from these variables acts as input to
the model.
graph (tf.Graph): dataflow graph for the network
use_gpu (bool): input into sess_config
sess_config (tf.ConfigProto): specifies device for model training
saver (tf.train.Saver): for saving and restoring variables
merge_summaries (tf op): op that merges all summary ops
init (tf op): op that initializes global variables in graph
"""
_allowed_noise_dists = ['gaussian', 'poisson', 'bernoulli']
_allowed_learning_algs = ['adam', 'lbfgs']
def __init__(self,
layer_sizes=None,
num_examples=None,
act_funcs='relu',
noise_dist='poisson',
init_type='trunc_normal',
learning_alg='adam',
learning_rate=1e-3,
use_batches=True,
tf_seed=0,
use_gpu=None):
"""Constructor for RLVM class
Args:
layer_sizes (list of ints): size of each layer, including input
layer
num_examples (int): total number of examples in training data
act_funcs (str or list of strs, optional): activation function for
network layers; replicated if a single element.
['relu'] | 'sigmoid' | 'tanh' | 'identity' | 'softplus' | 'elu'
noise_dist (str, optional): noise distribution used by network
['gaussian'] | 'poisson' | 'bernoulli'
init_type (str or list of strs, optional): initialization
used for network weights; replicated if a single element.
['trunc_normal'] | 'normal' | 'zeros' | 'xavier'
learning_alg (str, optional): algorithm used for learning
parameters.
['adam'] | 'lbfgs'
learning_rate (float, optional): learning rate used by the
gradient descent-based optimizers ('adam'). Default is 1e-3.
use_batches (boolean, optional): determines how data is fed to
model; if False, all data is pinned to variables used
throughout fitting; if True, a data slicer and batcher are
constructed to feed the model shuffled batches throughout
training
tf_seed (scalar, optional): rng seed for tensorflow to facilitate
building reproducible models
use_gpu (bool): use gpu for training model
Raises:
TypeError: If layer_sizes argument is not specified
TypeError: If num_examples argument is not specified
ValueError: If noise_dist argument is not valid string
ValueError: If learning_alg is not a valid string
"""
# call __init__() method of super class
super(RLVM, self).__init__()
# input checking
if layer_sizes is None:
raise TypeError('Must specify layer sizes for network')
if num_examples is None:
raise TypeError('Must specify number of training examples')
if noise_dist not in self._allowed_noise_dists:
raise ValueError('Invalid noise distribution')
if learning_alg not in self._allowed_learning_algs:
raise ValueError('Invalid learning algorithm')
# set model attributes from input
self.input_size = layer_sizes[0]
self.output_size = self.input_size
self.noise_dist = noise_dist
self.learning_alg = learning_alg
self.learning_rate = learning_rate
self.num_examples = num_examples
self.use_batches = use_batches
# for saving and restoring models
self.graph = tf.Graph() # must be initialized before graph creation
# for specifying device
if use_gpu is not None:
self.use_gpu = use_gpu
if use_gpu:
self.sess_config = tf.ConfigProto(device_count={'GPU': 1})
else:
self.sess_config = tf.ConfigProto(device_count={'GPU': 0})
# build model graph
with self.graph.as_default():
tf.set_random_seed(tf_seed)
# define pipeline for feeding data into model
with tf.name_scope('data'):
self._initialize_data_pipeline()
# initialize weights and create model
self.network = FFNetwork(scope='model',
inputs=self.data_in_batch,
layer_sizes=layer_sizes,
activation_funcs=act_funcs,
weights_initializer=init_type,
biases_initializer='zeros',
log_activations=True)
# set l2 penalty on weights to 0.0 to avoid future graph extensions
for layer in range(self.network.num_layers):
self.network.layers[layer].reg.vals['l2'] = 0.0
# define loss function
with tf.name_scope('loss'):
self._define_loss()
# define optimization routine
with tf.name_scope('optimizer'):
self._define_optimizer()
# add additional ops
# for saving and restoring models (initialized after var creation)
self.saver = tf.train.Saver()
# collect all summaries into a single op
self.merge_summaries = tf.summary.merge_all()
# add variable initialization op to graph
self.init = tf.global_variables_initializer()
def _define_loss(self):
"""Loss function that will be used to optimize model parameters"""
data_out = self.data_out_batch
pred = self.network.layers[-1].outputs
# define cost function
if self.noise_dist == 'gaussian':
with tf.name_scope('gaussian_loss'):
self.cost = tf.nn.l2_loss(data_out - pred)
elif self.noise_dist == 'poisson':
with tf.name_scope('poisson_loss'):
cost = -tf.reduce_sum(
tf.multiply(data_out, tf.log(self._log_min + pred)) - pred)
# normalize by number of spikes
self.cost = tf.divide(cost, tf.reduce_sum(data_out))
elif self.noise_dist == 'bernoulli':
with tf.name_scope('bernoulli_loss'):
self.cost = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=data_out,
logits=pred))
# add regularization penalties
with tf.name_scope('regularization'):
self.cost_reg = self.network.define_regularization_loss()
self.cost_penalized = tf.add(self.cost, self.cost_reg)
# save summary of cost
with tf.name_scope('summaries'):
tf.summary.scalar('cost', cost)
def _assign_model_params(self, sess):
"""Functions assigns parameter values to randomly initialized model"""
with self.graph.as_default():
self.network.assign_model_params(sess)
def _assign_reg_vals(self, sess):
"""Loops through all current regularization penalties and updates
prameter values"""
with self.graph.as_default():
self.network.assign_reg_vals(sess)
def set_regularization(self, reg_type, reg_val, layer_target=None):
"""Add or reassign regularization values
Args:
reg_type (str): see allowed_reg_types in regularization.py
reg_val (int): corresponding regularization value
layer_target (int or list of ints): specifies which layers the
current reg_type/reg_val pair is applied to
"""
if layer_target is None:
# set all layers
layer_target = range(self.network.num_layers)
# set regularization at the layer level
rebuild_graph = False
for layer in layer_target:
new_reg_type = self.network.layers[layer].set_regularization(
reg_type, reg_val)
rebuild_graph = rebuild_graph or new_reg_type
if rebuild_graph:
with self.graph.as_default():
# redefine loss function
with tf.name_scope('loss'):
self._define_loss()
# redefine optimization routine
with tf.variable_scope('optimizer'):
self._define_optimizer()
def forward_pass(self, input_data=None, output_data=None, data_indxs=None):
"""Transform a given input into its reconstruction
Args:
input_data (time x input_dim numpy array): input to network
output_data (time x output_dim numpy array): desired output of
network
data_indxs (numpy array, optional): indexes of data to use in
calculating forward pass; if not supplied, all data is used
Returns:
pred (time x num_cells numpy array): predicted model output
"""
if data_indxs is None:
data_indxs = np.arange(self.num_examples)
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
self._restore_params(sess, input_data, output_data)
# calculate model prediction
pred = sess.run(self.network.layers[-1].outputs,
feed_dict={self.indices: data_indxs})
return pred
def get_cost(self, input_data=None, output_data=None, data_indxs=None):
"""Get cost from loss function and regularization terms
Args:
input_data (time x input_dim numpy array): input to model
output_data (time x output_dim numpy array): desired output of
model
data_indxs (numpy array, optional): indexes of data to use in
calculating forward pass; if not supplied, all data is used
Returns:
cost (float): value of model's cost function evaluated on previous
model data or that used as input
reg_pen (float): value of model's regularization penalty
Raises:
ValueError: If data_out is not supplied with data_in
ValueError: If data_in/out time dims don't match
"""
if data_indxs is None:
data_indxs = np.arange(self.num_examples)
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
self._restore_params(sess, input_data, output_data)
cost, cost_reg = sess.run([self.cost, self.cost_reg],
feed_dict={self.indices: data_indxs})
return cost, cost_reg
def get_r2s(self, input_data=None, output_data=None, data_indxs=None):
"""Transform a given input into its reconstruction
Args:
input_data (time x input_dim numpy array): input to network
output_data (time x output_dim numpy array): desired output of
network
data_indxs (numpy array, optional): indexes of data to use in
calculating forward pass; if not supplied, all data is used
Returns:
r2s (1 x num_cells numpy array): pseudo-r2 values for each cell
lls (dict): contains log-likelihoods for fitted model, null model
(prediction is mean), and saturated model (prediction is true
activity) for each cell
Raises:
ValueError: If both input and output data are not provided
ValueError: If input/output time dims don't match
"""
if data_indxs is None:
data_indxs = np.arange(self.num_examples)
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
self._restore_params(sess, input_data, output_data)
data_in = sess.run(self.data_in_batch,
feed_dict={self.indices: data_indxs})
data_out = sess.run(self.data_out_batch,
feed_dict={self.indices: data_indxs})
pred = sess.run(self.network.layers[-1].outputs,
feed_dict={self.indices: data_indxs})
t, num_cells = data_in.shape
mean_act = np.tile(np.mean(data_out, axis=0), (t, 1))
if self.noise_dist == 'gaussian':
ll = np.sum(np.square(data_out - pred), axis=0)
ll_null = np.sum(np.square(data_out - mean_act), axis=0)
ll_sat = 0.0
elif self.noise_dist == 'poisson':
ll = -np.sum(
np.multiply(data_out, np.log(self._log_min + pred)) - pred,
axis=0)
ll_null = -np.sum(np.multiply(
data_out, np.log(self._log_min + mean_act)) - mean_act,
axis=0)
ll_sat = np.multiply(data_out, np.log(self._log_min + data_out))
ll_sat = -np.sum(ll_sat - data_out, axis=0)
elif self.noise_dist == 'bernoulli':
ll_sat = 1.0
ll_null = 0.0
ll = 0.0
r2s = 1.0 - np.divide(ll_sat - ll, ll_sat - ll_null)
r2s[ll_sat == ll_null] = 1.0
lls = {'ll': ll, 'll_null': ll_null, 'll_sat': ll_sat}
return r2s, lls
def get_reg_pen(self):
"""Return reg penalties in a dictionary"""
reg_dict = {}
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
# initialize all parameters randomly
sess.run(self.init)
# overwrite randomly initialized values of model with stored values
self._assign_model_params(sess)
# update regularization parameter values
self._assign_reg_vals(sess)
with tf.name_scope('get_reg_pen'): # to keep the graph clean-ish
for layer in range(self.network.num_layers):
reg_dict['layer%i' % layer] = \
self.network.layers[layer].get_reg_pen(sess)
return reg_dict
class NetworkNIM(Network):
"""Tensorflow (tf) implementation of network-NIM class
Attributes:
network (FFNetwork object): feedforward network
input_dims (int): dimensions of stimulus (up to 3-D)
noise_dist (str): noise distribution used for cost function
activations (list of tf ops): evaluates the layer-wise activations of
the model
cost (tf op): evaluates the cost function of the model
cost_reg (tf op): evaluates the regularization penalty of the model
cost_penalized (tf op): evaluates sum of `cost` and `cost_reg`
train_step (tf op): evaluates one training step using the specified
cost function and learning algorithm
data_in_ph (tf.placeholder): placeholder for input data
data_out_ph (tf.placeholder): placeholder for output data
data_in_var (tf.Variable): Variable for input data fed by data_in_ph
data_out_var (tf.Variable): Variable for output data fed by data_out_ph
data_in_batch (tf.Variable): batched version of data_in_var
data_out_batch (tf.Variable): batched version of data_out_var
indices (tf.placeholder): placeholder for indices into data_in/out_var
to create data_in/out_batch
learning_alg (str): algorithm used for learning parameters
learning_rate (float): learning rate used by the gradient
descent-based optimizers
num_examples (int): total number of examples in training data
use_batches (bool): all training data is placed on the GPU by
piping data into i/o variables through a feed_dict. If use_batches
is False, all data from these variables are used during training.
If use_batches is True, the a separate index feed_dict is used to
specify which chunks of data from these variables acts as input to
the model.
graph (tf.Graph): dataflow graph for the network
use_gpu (bool): input into sess_config
sess_config (tf.ConfigProto): specifies device for model training
saver (tf.train.Saver): for saving and restoring variables
merge_summaries (tf op): op that merges all summary ops
init (tf op): op that initializes global variables in graph
"""
_allowed_noise_dists = ['gaussian', 'poisson', 'bernoulli']
def __init__(
self,
network_params,
noise_dist='poisson',
tf_seed=0 ):
"""Constructor for network-NIM class
Args:
network_params: created using 'createNIMparams', which has at least the following
arguments that get passed to FFnetwork (and possibly more):
-> layer_sizes (list of integers, or 3-d lists of integers)
-> activation_funcs (list of strings)
-> pos_constraints (list of Booleans)
noise_dist (str, optional): noise distribution used by network
['gaussian'] | 'poisson' | 'bernoulli'
tf_seed (scalar): rng seed for tensorflow to facilitate building
reproducible models
use_gpu (bool): use gpu for training model
Raises:
TypeError: If layer_sizes argument is not specified
TypeError: If num_examples argument is not specified
ValueError: If noise_dist argument is not valid string
ValueError: If learning_alg is not a valid string
Thinking about Ninhibitory units and positivity constraints:
-- The last (output layer) is never negative, so should only correspond
to subunits
-- Positivity constraints never applied to weights on input
"""
# input checking
#if num_examples is None:
# raise ValueError('Must specify number of training examples')
# call __init__() method of super class
super(NetworkNIM, self).__init__()
# process stim-dims into 3-d dimensions
stim_dims = network_params['layer_sizes'][0]
if not isinstance(stim_dims, list):
# then just 1-dimension (place in time)
stim_dims = [stim_dims,1,1]
else:
while len(stim_dims) < 3:
stim_dims.append(1)
# make a list out of this
self.input_size = [stim_dims[0]*stim_dims[1]*stim_dims[2]]
# set model attributes from input
self.network_params = network_params # kindof redundant, but currently not a better way...
self.stim_dims = stim_dims
self.output_size = [network_params['layer_sizes'][-1]] # make a list
self.num_layers = len(network_params['layer_sizes'])-1
self.activation_functions = network_params['activation_funcs']
self.noise_dist = noise_dist
self.tf_seed = tf_seed
self._define_network( network_params )
# set parameters for graph (constructed for each train)
self.graph = None
self.saver = None
self.merge_summaries = None
self.init = None
# END networkNIM.__init__
def _define_network( self, network_params ):
self.network = FFNetwork( scope = 'FFNetwork',
params_dict = network_params )
def _build_graph(self, learning_alg='adam', learning_rate=1e-3, use_gpu=False ):
# for saving and restoring models
self.graph = tf.Graph() # must be initialized before graph creation
# for specifying device
if use_gpu:
self.sess_config = tf.ConfigProto(device_count={'GPU': 1})
else:
self.sess_config = tf.ConfigProto(device_count={'GPU': 0})
# build model graph
with self.graph.as_default():
np.random.seed(self.tf_seed)
tf.set_random_seed(self.tf_seed)
# define pipeline for feeding data into model
with tf.variable_scope('data'):
self._initialize_data_pipeline()
# Build network graph
self.network.build_graph( self.data_in_batch[0], self.network_params )
# Define loss function
with tf.variable_scope('loss'):
self._define_loss()
# Define optimization routine
with tf.variable_scope('optimizer'):
self._define_optimizer( learning_alg, learning_rate )
# add additional ops
# for saving and restoring models (initialized after var creation)
self.saver = tf.train.Saver()
# collect all summaries into a single op
self.merge_summaries = tf.summary.merge_all()
# add variable initialization op to graph
self.init = tf.global_variables_initializer()
def _define_loss(self):
"""Loss function that will be used to optimize model parameters"""
data_out = self.data_out_batch[0]
pred = self.network.layers[-1].outputs
# define cost function
cost = 0.0
if self.noise_dist == 'gaussian':
with tf.name_scope('gaussian_loss'):
# should variable 'cost' be defined here too?
cost = tf.nn.l2_loss(data_out - pred)
self.unit_cost = tf.reduce_mean(tf.square(data_out-pred), axis=0)
elif self.noise_dist == 'poisson':
with tf.name_scope('poisson_loss'):
cost_norm = tf.maximum( tf.reduce_sum(data_out, axis=0), 1)
cost = -tf.reduce_sum( tf.divide(
tf.multiply(data_out,tf.log(self._log_min + pred)) - pred,
cost_norm ) )
self.unit_cost = tf.divide( -tf.reduce_sum(
tf.multiply(data_out,tf.log(self._log_min + pred)) - pred, axis=0), cost_norm )
elif self.noise_dist == 'bernoulli':
with tf.name_scope('bernoulli_loss'):
# Check per-cell normalization with cross-entropy
cost_norm = tf.maximum( tf.reduce_sum(data_out, axis=0), 1)
cost = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=data_out,logits=pred) )
self.unit_cost = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(labels=data_out,logits=pred), axis=0 )
#cost = tf.reduce_sum(self.unit_cost)
else:
print('Cost function not supported.')
self.cost = cost
# add regularization penalties
with tf.name_scope('regularization'):
self.cost_reg = self.network.define_regularization_loss()
self.cost_penalized = tf.add(self.cost, self.cost_reg)
# save summary of cost
with tf.variable_scope('summaries'):
tf.summary.scalar('cost', cost)
# END NetworkNIM._define_loss
def _assign_model_params(self, sess):
"""Functions assigns parameter values to randomly initialized model"""
with self.graph.as_default():
self.network.assign_model_params(sess)
def _write_model_params(self, sess):
"""Pass write_model_params down to network"""
self.network.write_model_params(sess)
def _assign_reg_vals(self, sess):
"""Loops through all current regularization penalties and updates
parameter values"""
with self.graph.as_default():
self.network.assign_reg_vals(sess)
def _build_fit_variable_list( self, fit_parameter_list ):
"""Generates variable list to fit if argument is not none. 'fit_parameter_list'
is generated by a """
var_list = None
if fit_parameter_list is not None:
var_list = self.network._build_fit_variable_list( fit_parameter_list[0] )
return var_list
# END NetworkNIM._generate_variable_list
def variables_to_fit(self, layers_to_skip=None, fit_biases=False):
"""Generates a list-of-lists-of-lists of correct format to specify all the
variables to fit, as an argument for network.train
Args:
layers_to_skip: [default=None] This is a list of layers to not fit variables
fit_biases: [default=False] Whether to default not fit biases"""
if layers_to_skip is None:
layers_to_skip = []
else:
if not isinstance(layers_to_skip,list):
layers_to_skip = [layers_to_skip]
fit_list = [{}]*self.network.num_layers
for layer in range(self.network.num_layers):
fit_list[layer]['weights']=True
fit_list[layer]['biases']=fit_biases
if layer in layers_to_skip:
fit_list[layer]['weights'] = False
fit_list[layer]['biases'] = False
return [fit_list]
# END NetworkNIM.set_fit_variables
def set_regularization(self, reg_type, reg_val, layer_target=None):
"""Add or reassign regularization values
Args:
reg_type (str): see allowed_reg_types in regularization.py
reg_val (int): corresponding regularization value
layer_target (int or list of ints): specifies which layers the
current reg_type/reg_val pair is applied to
"""
if layer_target is None:
# set all layers
layer_target = range(self.network.num_layers)
# set regularization at the layer level
for layer in layer_target:
new_reg_type = self.network.layers[layer].set_regularization(reg_type, reg_val)
# No longer need to know this since not building graph until train
# END NetworkNIM.set_regularization
def get_LL(self, input_data, output_data, data_indxs=None):
"""Get cost from loss function and regularization terms
Args:
input_data (time x input_dim numpy array): input to model
output_data (time x output_dim numpy array): desired output of
model
data_indxs (numpy array, optional): indexes of data to use in
calculating forward pass; if not supplied, all data is used
Returns:
cost (float): value of model's cost function evaluated on previous
model data or that used as input
reg_pen (float): value of model's regularization penalty
Raises:
ValueError: If data_in/out time dims don't match
"""
# check input
if data_indxs is None:
data_indxs = np.arange(self.num_examples)
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
self._restore_params(sess, input_data, output_data)
cost = sess.run(self.cost,
feed_dict={self.indices: data_indxs})
return cost
# END get_LL
def eval_models(self, input_data, output_data, data_indxs=None, nulladjusted=False):
"""Get cost for each output neuron without regularization terms
Args:
input_data (time x input_dim numpy array): input to model
output_data (time x output_dim numpy array): desired output of
model
data_indxs (numpy array, optional): indexes of data to use in
calculating forward pass; if not supplied, all data is used
Returns:
cost (float): value of model's cost function evaluated on previous
model data or that used as input
reg_pen (float): value of model's regularization penalty
Raises:
ValueError: If data_in/out time dims don't match
"""
assert self.graph is not None, 'Must fit model first.'
# check input
if data_indxs is None:
data_indxs = np.arange(self.num_examples)
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
self._restore_params(sess, input_data, output_data)
LL_neuron = sess.run(self.unit_cost, feed_dict={self.indices: data_indxs})
if nulladjusted:
# note that LL_neuron is negative of the true LL, but nullLL is not (so + is actually subtraction)
LL_neuron += self.nullLL(output_data[data_indxs, :])
return LL_neuron
# END get_LL_neuron
def nullLL(self, Robs ):
"""Calculates null-model (constant firing rate) likelihood, given Robs (which determines
what firing rate for each cell)"""
if self.noise_dist == 'gaussian':
# In this case, LLnull is just var of data
LLnulls = np.var(Robs, axis=0)
elif self.noise_dist == 'poisson':
rbars = np.mean(Robs, axis=0)
LLnulls = np.log(rbars)-1
# elif self.noise_dist == 'bernoulli':
else:
LLnulls = [0]*Robs.shape[1]
print('Not worked out yet')
return LLnulls
# END NetworkNIM.nullLL
def generate_prediction(self, input_data, data_indxs=None, layer=-1 ):
assert self.graph is not None, 'Must fit model first.'
# check input
if layer >= 0:
assert layer < len(self.network.layers), 'This layer does not exist.'
if data_indxs is None:
data_indxs = np.arange(self.num_examples)
# Generate fake_output data
output_data = np.zeros( [self.num_examples,self.output_size[0]], dtype='float32' )
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
self._restore_params(sess, input_data, output_data)
pred = sess.run(self.network.layers[layer].outputs, feed_dict={self.indices: data_indxs})
return pred
# END NetworkNIM.generate_prediction
def get_reg_pen(self):
"""Return reg penalties in a dictionary"""
reg_dict = {}
with tf.Session(graph=self.graph, config=self.sess_config) as sess:
# initialize all parameters randomly
sess.run(self.init)
# overwrite randomly initialized values of model with stored values
self._assign_model_params(sess)
# update regularization parameter values
self._assign_reg_vals(sess)
with tf.name_scope('get_reg_pen'): # to keep the graph clean-ish
for layer in range(self.network.num_layers):
reg_dict['layer%i' % layer] = \
self.network.layers[layer].get_reg_pen(sess)
return reg_dict
# END get_reg_pen
def copy_model( self, alternate_network_params = None, target = None,
layers_to_transfer = None,
target_layers = None,
init_type='trunc_normal', tf_seed=0 ):
num_layers = len(self.network.layers)
if target is None:
# Make new target
target = self.create_NIM_copy( init_type=init_type, tf_seed=tf_seed,
alternate_network_params=alternate_network_params )
# Figure out mapping from self.layers to target.layers
num_layers_target = len(target.network.layers)
if layers_to_transfer is not None:
assert max(layers_to_transfer) <= num_layers, 'Too many layers to transfer.'
if target_layers is None:
assert len(layers_to_transfer) <= num_layers_target, 'Too many layers to transfer.'
target_layers = range(len(layers_to_transfer))
if target_layers is not None:
assert max(target_layers) <= num_layers_target, 'Too many layers for target.'
if layers_to_transfer is None:
assert len(target_layers) <= num_layers, 'Too many target layers.'
layers_to_transfer = range(len(target_layers))
if num_layers >= num_layers_target:
target_copy = range(num_layers_target)
if target_layers is None:
self_copy = target_copy # then default is copy the top_most layers
else:
self_copy = target_layers
else:
self_copy = range(num_layers)
if target_layers is None:
target_copy = self_copy # then default is copy the top_most layers
else:
target_copy = target_layers
assert len(target_copy) == len(self_copy), 'Number of targets and transfers must match.'
# Copy information from self to new target NIM
for nn in range(len(self_copy)):
self_layer = self.network.layers[self_copy[nn]]
tar = target_copy[nn]
self_num_outputs = self_layer.output_dims[0]*self_layer.output_dims[1]*self_layer.output_dims[2]
tar_num_outputs = target.network.layers[tar].output_dims[0]\
*target.network.layers[tar].output_dims[1]*target.network.layers[tar].output_dims[2]
# Copy remaining layer properties
target.network.layers[tar].ei_mask = self_layer.ei_mask
if self_num_outputs <= tar_num_outputs:
target.network.layers[tar].weights[:,0:self_num_outputs] \
= self_layer.weights
target.network.layers[tar].biases[0:self_num_outputs] \
= self_layer.biases
else:
target.network.layers[tar].weights = \
self_layer.weights[:,0:tar_num_outputs]
target.network.layers[tar].biases = \
self_layer.biases[0:tar_num_outputs]
return target
# END make_copy
def create_NIM_copy( self, init_type=None, tf_seed=None, alternate_network_params=None ):
if alternate_network_params is not None:
network_params = alternate_network_params
else:
network_params = self.network_params
target = NetworkNIM( network_params,
noise_dist = self.noise_dist,
learning_alg = self.learning_alg,
learning_rate = self.learning_rate,
use_batches = self.use_batches,
tf_seed = tf_seed,
use_gpu = self.use_gpu )
return target