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bnlstm.py
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import math
import numpy as np
import tensorflow as tf
from tensorflow.python.ops.rnn_cell import RNNCell
class LSTMCell(RNNCell):
'''Vanilla LSTM implemented with same initializations as BN-LSTM'''
def __init__(self, num_units):
self.num_units = num_units
@property
def state_size(self):
return (self.num_units, self.num_units)
@property
def output_size(self):
return self.num_units
def __call__(self, x, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
c, h = state
# Keep W_xh and W_hh separate here as well to reuse initialization methods
x_size = x.get_shape().as_list()[1]
W_xh = tf.get_variable('W_xh',
[x_size, 4 * self.num_units],
initializer=orthogonal_initializer())
W_hh = tf.get_variable('W_hh',
[self.num_units, 4 * self.num_units],
initializer=bn_lstm_identity_initializer(0.95))
bias = tf.get_variable('bias', [4 * self.num_units])
# hidden = tf.matmul(x, W_xh) + tf.matmul(h, W_hh) + bias
# improve speed by concat.
concat = tf.concat(1, [x, h])
W_both = tf.concat(0, [W_xh, W_hh])
hidden = tf.matmul(concat, W_both) + bias
i, j, f, o = tf.split(1, 4, hidden)
new_c = c * tf.sigmoid(f) + tf.sigmoid(i) * tf.tanh(j)
new_h = tf.tanh(new_c) * tf.sigmoid(o)
return new_h, (new_c, new_h)
class BNLSTMCell(RNNCell):
'''Batch normalized LSTM as described in arxiv.org/abs/1603.09025'''
def __init__(self, num_units, training):
self.num_units = num_units
self.training = training
@property
def state_size(self):
return (self.num_units, self.num_units)
@property
def output_size(self):
return self.num_units
def __call__(self, x, state, scope=None):
with tf.variable_scope(scope or type(self).__name__):
c, h = state
x_size = x.get_shape().as_list()[1]
W_xh = tf.get_variable('W_xh',
[x_size, 4 * self.num_units],
initializer=orthogonal_initializer())
W_hh = tf.get_variable('W_hh',
[self.num_units, 4 * self.num_units],
initializer=bn_lstm_identity_initializer(0.95))
bias = tf.get_variable('bias', [4 * self.num_units])
xh = tf.matmul(x, W_xh)
hh = tf.matmul(h, W_hh)
bn_xh = batch_norm(xh, 'xh', self.training)
bn_hh = batch_norm(hh, 'hh', self.training)
hidden = bn_xh + bn_hh + bias
i, j, f, o = tf.split(1, 4, hidden)
new_c = c * tf.sigmoid(f) + tf.sigmoid(i) * tf.tanh(j)
bn_new_c = batch_norm(new_c, 'c', self.training)
new_h = tf.tanh(bn_new_c) * tf.sigmoid(o)
return new_h, (new_c, new_h)
def orthogonal(shape):
flat_shape = (shape[0], np.prod(shape[1:]))
a = np.random.normal(0.0, 1.0, flat_shape)
u, _, v = np.linalg.svd(a, full_matrices=False)
q = u if u.shape == flat_shape else v
return q.reshape(shape)
def bn_lstm_identity_initializer(scale):
def _initializer(shape, dtype=tf.float32, partition_info=None):
'''Ugly cause LSTM params calculated in one matrix multiply'''
size = shape[0]
# gate (j) is identity
t = np.zeros(shape)
t[:, size:size * 2] = np.identity(size) * scale
t[:, :size] = orthogonal([size, size])
t[:, size * 2:size * 3] = orthogonal([size, size])
t[:, size * 3:] = orthogonal([size, size])
return tf.constant(t, dtype)
return _initializer
def orthogonal_initializer():
def _initializer(shape, dtype=tf.float32, partition_info=None):
return tf.constant(orthogonal(shape), dtype)
return _initializer
def batch_norm(x, name_scope, training, epsilon=1e-3, decay=0.999):
'''Assume 2d [batch, values] tensor'''
with tf.variable_scope(name_scope):
size = x.get_shape().as_list()[1]
scale = tf.get_variable('scale', [size], initializer=tf.constant_initializer(0.1))
offset = tf.get_variable('offset', [size])
pop_mean = tf.get_variable('pop_mean', [size], initializer=tf.constant_initializer(value=0.), trainable=False)
pop_var = tf.get_variable('pop_var', [size], initializer=tf.constant_initializer(value=0.), trainable=False)
batch_mean, batch_var = tf.nn.moments(x, [0])
train_mean_op = tf.assign(pop_mean, pop_mean * decay + batch_mean * (1 - decay))
train_var_op = tf.assign(pop_var, pop_var * decay + batch_var * (1 - decay))
def batch_statistics():
with tf.control_dependencies([train_mean_op, train_var_op]):
return tf.nn.batch_normalization(x, batch_mean, batch_var, offset, scale, epsilon)
def population_statistics():
return tf.nn.batch_normalization(x, pop_mean, pop_var, offset, scale, epsilon)
return tf.cond(training, batch_statistics, population_statistics)