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target_lstm.py
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import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
class TARGET_LSTM(object):
def __init__(self, opt, FLAGS, params, pretrain = True):
self.num_emb = opt.vocab_size
self.emb_dim = opt.g_emb_dim
self.hidden_dim = opt.g_hid_dim
self.seq_len = opt.seq_len
self.g_params = []
if pretrain:
self.batch_size = FLAGS.gen_pre_batch_size
else:
self.batch_size = FLAGS.gen_batch_size
self.start_token = tf.constant([opt.start_token] * self.batch_size, dtype=tf.int32)
self.params = params
tf.set_random_seed(66)
with tf.variable_scope('generator'):
self.g_embeddings = tf.Variable(self.params[0])
self.g_params.append(self.g_embeddings)
self.g_recurrent_unit = self.create_recurrent_unit(self.g_params) # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit(self.g_params) # maps h_t to o_t (output token logits)
# placeholder definition
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.seq_len]) # sequence of tokens generated by generator
# processed for batch
with tf.device("/cpu:0"):
self.processed_x = tf.transpose(tf.nn.embedding_lookup(self.g_embeddings, self.x), perm=[1, 0, 2]) # seq_length x batch_size x emb_dim
# initial states
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = tf.stack([self.h0, self.h0])
# generator on initial randomness
gen_o = tensor_array_ops.TensorArray(dtype=tf.float32, size=self.seq_len,
dynamic_size=False, infer_shape=True)
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.seq_len,
dynamic_size=False, infer_shape=True)
def _g_recurrence(i, x_t, h_tm1, gen_o, gen_x):
h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
o_t = self.g_output_unit(h_t) # batch x vocab , logits not prob
log_prob = tf.log(tf.nn.softmax(o_t))
next_token = tf.cast(tf.reshape(tf.multinomial(log_prob, 1), [self.batch_size]), tf.int32)
x_tp1 = tf.nn.embedding_lookup(self.g_embeddings, next_token) # batch x emb_dim
gen_o = gen_o.write(i, tf.reduce_sum(tf.multiply(tf.one_hot(next_token, self.num_emb, 1.0, 0.0),
tf.nn.softmax(o_t)), 1)) # [batch_size] , prob
gen_x = gen_x.write(i, next_token) # indices, batch_size
return i + 1, x_tp1, h_t, gen_o, gen_x
_, _, _, self.gen_o, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4: i < self.seq_len,
body=_g_recurrence,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, gen_o, gen_x)
)
self.gen_x = self.gen_x.stack() # seq_length x batch_size
self.gen_x = tf.transpose(self.gen_x, perm=[1, 0]) # batch_size x seq_length
# supervised pretraining for generator
g_predictions = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.seq_len,
dynamic_size=False, infer_shape=True)
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.seq_len)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
def _pretrain_recurrence(i, x_t, h_tm1, g_predictions):
h_t = self.g_recurrent_unit(x_t, h_tm1)
o_t = self.g_output_unit(h_t)
g_predictions = g_predictions.write(i, tf.nn.softmax(o_t)) # batch x vocab_size
x_tp1 = ta_emb_x.read(i)
return i + 1, x_tp1, h_t, g_predictions
_, _, _, self.g_predictions = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3: i < self.seq_len,
body=_pretrain_recurrence,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token),
self.h0, g_predictions))
self.g_predictions = tf.transpose(
self.g_predictions.stack(), perm=[1, 0, 2]) # batch_size x seq_length x vocab_size
# pretraining loss
self.pretrain_loss = -tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.reshape(self.g_predictions, [-1, self.num_emb]))) / (self.seq_len * self.batch_size)
self.out_loss = tf.reduce_sum(
tf.reshape(
-tf.reduce_sum(
tf.one_hot(tf.to_int32(tf.reshape(self.x, [-1])), self.num_emb, 1.0, 0.0) * tf.log(
tf.reshape(self.g_predictions, [-1, self.num_emb])), 1
), [-1, self.seq_len]
), 1
) # batch_size
def generate(self, session):
# h0 = np.random.normal(size=self.hidden_dim)
outputs = session.run(self.gen_x)
return outputs
def init_matrix(self, shape):
return tf.random_normal(shape, stddev=1.0)
def create_recurrent_unit(self, params):
# Weights and Bias for input and hidden tensor
self.Wi = tf.Variable(self.params[1])
self.Ui = tf.Variable(self.params[2])
self.bi = tf.Variable(self.params[3])
self.Wf = tf.Variable(self.params[4])
self.Uf = tf.Variable(self.params[5])
self.bf = tf.Variable(self.params[6])
self.Wog = tf.Variable(self.params[7])
self.Uog = tf.Variable(self.params[8])
self.bog = tf.Variable(self.params[9])
self.Wc = tf.Variable(self.params[10])
self.Uc = tf.Variable(self.params[11])
self.bc = tf.Variable(self.params[12])
params.extend([
self.Wi, self.Ui, self.bi,
self.Wf, self.Uf, self.bf,
self.Wog, self.Uog, self.bog,
self.Wc, self.Uc, self.bc])
def unit(x, hidden_memory_tm1):
previous_hidden_state, c_prev = tf.unstack(hidden_memory_tm1)
# Input Gate
i = tf.sigmoid(
tf.matmul(x, self.Wi) +
tf.matmul(previous_hidden_state, self.Ui) + self.bi
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf) +
tf.matmul(previous_hidden_state, self.Uf) + self.bf
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog) +
tf.matmul(previous_hidden_state, self.Uog) + self.bog
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc) +
tf.matmul(previous_hidden_state, self.Uc) + self.bc
)
# Final Memory cell
c = f * c_prev + i * c_
# Current Hidden state
current_hidden_state = o * tf.nn.tanh(c)
return tf.stack([current_hidden_state, c])
return unit
def create_output_unit(self, params):
self.Wo = tf.Variable(self.params[13])
self.bo = tf.Variable(self.params[14])
params.extend([self.Wo, self.bo])
def unit(hidden_memory_tuple):
hidden_state, c_prev = tf.unstack(hidden_memory_tuple)
# hidden_state : batch x hidden_dim
logits = tf.matmul(hidden_state, self.Wo) + self.bo
# output = tf.nn.softmax(logits)
return logits
return unit