def _inner_function(self,
inputs,
past_hidden_state,
activation=tf.nn.tanh):
"""second order function as described equation 11 in delta rnn paper
The main goal is to produce z_t of this function
"""
V_x_d = linear(past_hidden_state, self._num_units, True)
# We make this a private variable to be reused in the _outer_function
self._W_x_inputs = linear(inputs, self._num_units, True)
alpha = tf.get_variable("alpha", [self._num_units],
dtype=tf.float32,
initializer=tf.ones_initializer)
beta_one = tf.get_variable("beta_one", [self._num_units],
dtype=tf.float32,
initializer=tf.ones_initializer)
beta_two = tf.get_variable("beta_two", [self._num_units],
dtype=tf.float32,
initializer=tf.ones_initializer)
z_t_bias = tf.get_variable("z_t_bias", [self._num_units],
dtype=tf.float32,
initializer=tf.zeros_initializer)
# Second Order Cell Calculations
d_1_t = alpha * V_x_d * self._W_x_inputs
d_2_t = beta_one * V_x_d + beta_two * self._W_x_inputs
z_t = activation(d_1_t + d_2_t + z_t_bias)
return z_t
def __call__(self, inputs, state, timestep=0, scope=None):
with tf.variable_scope(scope or type(self).__name__):
with tf.variable_scope(
"Gates"
): # Forget Gate bias starts as 1.0 -- TODO: double check if this is correct
if self.use_multiplicative_integration:
gated_factor = multiplicative_integration(
[inputs, state], self._num_units,
self.forget_bias_initialization)
else:
gated_factor = linear([inputs, state], self._num_units,
True,
self.forget_bias_initialization)
gated_factor = tf.sigmoid(gated_factor)
with tf.variable_scope("Candidate"):
c = tf.tanh(linear([inputs], self._num_units, True, 0.0))
if self.use_recurrent_dropout and self.is_training:
input_contribution = tf.nn.dropout(
c, self.recurrent_dropout_factor)
else:
input_contribution = c
new_h = (1 -
gated_factor) * state + gated_factor * input_contribution
return new_h, new_h
def multiplicative_integration(list_of_inputs,
output_size,
initial_bias_value=0.0,
weights_already_calculated=False,
use_highway_gate=False,
use_l2_loss=False,
scope=None,
timestep=0):
'''expects len(2) for list of inputs and will perform integrative multiplication
weights_already_calculated will treat the list of inputs as Wx and Uz and is useful for batch normed inputs
'''
with tf.variable_scope(scope or 'double_inputs_multiple_integration'):
if len(list_of_inputs) != 2:
raise ValueError('list of inputs must be 2, you have:',
len(list_of_inputs))
if weights_already_calculated: #if you already have weights you want to insert from batch norm
Wx = list_of_inputs[0]
Uz = list_of_inputs[1]
else:
with tf.variable_scope('Calculate_Wx_mulint'):
Wx = linear.linear(list_of_inputs[0],
output_size,
False,
use_l2_loss=use_l2_loss,
timestep=timestep)
with tf.variable_scope("Calculate_Uz_mulint"):
Uz = linear.linear(list_of_inputs[1],
output_size,
False,
use_l2_loss=use_l2_loss,
timestep=timestep)
with tf.variable_scope("multiplicative_integration"):
alpha = tf.get_variable(
'mulint_alpha', [output_size],
initializer=tf.truncated_normal_initializer(mean=1.0,
stddev=0.1))
beta1, beta2 = tf.split(
0, 2,
tf.get_variable('mulint_params_betas', [output_size * 2],
initializer=tf.truncated_normal_initializer(
mean=0.5, stddev=0.1)))
original_bias = tf.get_variable(
'mulint_original_bias', [output_size],
initializer=tf.truncated_normal_initializer(
mean=initial_bias_value, stddev=0.1))
final_output = alpha * Wx * Uz + beta1 * Uz + beta2 * Wx + original_bias
if use_highway_gate:
final_output = highway_network.apply_highway_gate(
final_output, list_of_inputs[0])
return final_output
def __call__(self, inputs, state, timestep=0, scope=None):
with tf.variable_scope(scope
or type(self).__name__): # "BasicLSTMCell"
# Parameters of gates are concatenated into one multiply for efficiency.
hidden_state_plus_c_list = tf.split(1, self.num_memory_arrays + 1,
state)
h = hidden_state_plus_c_list[0]
c_list = hidden_state_plus_c_list[1:]
'''very large matrix multiplication to speed up procedure -- will split variables out later'''
if self.use_multiplicative_integration:
concat = multiplicative_integration(
[inputs, h], self._num_units * 4 * self.num_memory_arrays,
0.0)
else:
concat = linear([inputs, h],
self._num_units * 4 * self.num_memory_arrays,
True)
if self.use_layer_normalization:
concat = layer_norm(concat,
num_variables_in_tensor=4 *
self.num_memory_arrays)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate -- comes in sets of fours
all_vars_list = tf.split(1, 4 * self.num_memory_arrays, concat)
'''memory array loop'''
new_c_list, new_h_list = [], []
for array_counter in xrange(self.num_memory_arrays):
i = all_vars_list[0 + array_counter * 4]
j = all_vars_list[1 + array_counter * 4]
f = all_vars_list[2 + array_counter * 4]
o = all_vars_list[3 + array_counter * 4]
if self.use_recurrent_dropout and self.is_training:
input_contribution = tf.nn.dropout(
tf.tanh(j), self.recurrent_dropout_factor)
else:
input_contribution = tf.tanh(j)
new_c_list.append(c_list[array_counter] *
tf.sigmoid(f + self._forget_bias) +
tf.sigmoid(i) * input_contribution)
if self.use_layer_normalization:
new_c = layer_norm(new_c_list[-1])
else:
new_c = new_c_list[-1]
new_h_list.append(tf.tanh(new_c) * tf.sigmoid(o))
'''sum all new_h components -- could instead do a mean -- but investigate that later'''
new_h = tf.add_n(new_h_list)
return new_h, tf.concat([new_h] + new_c_list, 1) #purposely reversed
def __call__(self, inputs, state, timestep=0, scope=None):
current_state = state
for highway_layer in xrange(self.num_highway_layers):
with tf.variable_scope('highway_factor_' + str(highway_layer)):
if self.use_inputs_on_each_layer or highway_layer == 0:
highway_factor = tf.tanh(
linear([inputs, current_state],
self._num_units,
True,
use_kronecker_reparameterization=self.
_use_kronecker_reparameterization))
else:
highway_factor = tf.tanh(
linear([current_state],
self._num_units,
True,
use_kronecker_reparameterization=self.
_use_kronecker_reparameterization))
with tf.variable_scope('gate_for_highway_factor_' +
str(highway_layer)):
if self.use_inputs_on_each_layer or highway_layer == 0:
gate_for_highway_factor = tf.sigmoid(
linear([inputs, current_state],
self._num_units,
True,
-3.0,
use_kronecker_reparameterization=self.
_use_kronecker_reparameterization))
else:
gate_for_highway_factor = tf.sigmoid(
linear([current_state],
self._num_units,
True,
-3.0,
use_kronecker_reparameterization=self.
_use_kronecker_reparameterization))
gate_for_hidden_factor = 1.0 - gate_for_highway_factor
current_state = highway_factor * gate_for_highway_factor + current_state * gate_for_hidden_factor
return current_state, current_state
def __call__(self, inputs, state, scope=None):
with tf.device("/gpu:" + str(self._gpu_for_layer)):
"""JZS2, mutant 2 with n units cells."""
with tf.variable_scope(scope or type(self).__name__): # "JZS1Cell"
with tf.variable_scope(
"Zinput"): # Reset gate and update gate.
'''equation 1'''
z = tf.sigmoid(
linear([inputs, state],
self._num_units,
True,
1.0,
weight_initializer=self._weight_initializer,
orthogonal_scale_factor=self.
_orthogonal_scale_factor))
'''equation 2 '''
with tf.variable_scope("Rinput"):
r = tf.sigmoid(inputs + (linear(
[state],
self._num_units,
True,
1.0,
weight_initializer=self._weight_initializer,
orthogonal_scale_factor=self._orthogonal_scale_factor))
)
'''equation 3'''
with tf.variable_scope("Candidate"):
component_0 = linear([state * r, inputs], self._num_units,
True)
component_2 = (tf.tanh(component_0)) * z
component_3 = state * (1 - z)
h_t = component_2 + component_3
return h_t, h_t #there is only one hidden state output to keep track of.
def __call__(self, inputs, state, scope=None):
with tf.device("/gpu:" + str(self._gpu_for_layer)):
"""JZS1, mutant 1 with n units cells."""
with tf.variable_scope(scope or type(self).__name__): # "JZS1Cell"
with tf.variable_scope(
"Zinput"): # Reset gate and update gate.
# We start with bias of 1.0 to not reset and not update.
'''equation 1 z = sigm(WxzXt+Bz), x_t is inputs'''
z = tf.sigmoid(
linear([inputs],
self._num_units,
True,
1.0,
weight_initializer=self._weight_initializer,
orthogonal_scale_factor=self.
_orthogonal_scale_factor))
with tf.variable_scope("Rinput"):
'''equation 2 r = sigm(WxrXt+Whrht+Br), h_t is the previous state'''
r = tf.sigmoid(
linear([inputs, state],
self._num_units,
True,
1.0,
weight_initializer=self._weight_initializer,
orthogonal_scale_factor=self.
_orthogonal_scale_factor))
'''equation 3'''
with tf.variable_scope("Candidate"):
component_0 = linear([r * state], self._num_units, True)
component_1 = tf.tanh(tf.tanh(inputs) + component_0)
component_2 = component_1 * z
component_3 = state * (1 - z)
h_t = component_2 + component_3
return h_t, h_t #there is only one hidden state output to keep track of.