forked from LantaoYu/SeqGAN
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rollout.py
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rollout.py
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import tensorflow as tf
from tensorflow.python.ops import tensor_array_ops, control_flow_ops
import numpy as np
class ROLLOUT(object):
def __init__(self, lstm, update_rate):
self.lstm = lstm
self.update_rate = update_rate
self.num_emb = self.lstm.num_emb
self.batch_size = self.lstm.batch_size
self.emb_dim = self.lstm.emb_dim
self.hidden_dim = self.lstm.hidden_dim
self.sequence_length = self.lstm.sequence_length
self.start_token = tf.identity(self.lstm.start_token)
self.dropout_keep_prob = self.lstm.dropout_keep_prob
self.num_recurrent_layers = self.lstm.num_recurrent_layers
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = [self.create_recurrent_unit(i) for i in range(self.num_recurrent_layers)] # maps h_tm1 to h_t for generator
self.g_output_unit = self.create_output_unit() # maps h_t to o_t (output token logits)
#####################################################################################################
# placeholder definition
self.x = tf.placeholder(tf.int32, shape=[self.batch_size, self.sequence_length]) # sequence of tokens generated by generator
self.given_num = tf.placeholder(tf.int32)
# 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
ta_emb_x = tensor_array_ops.TensorArray(
dtype=tf.float32, size=self.sequence_length)
ta_emb_x = ta_emb_x.unstack(self.processed_x)
ta_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length)
ta_x = ta_x.unstack(tf.transpose(self.x, perm=[1, 0]))
#####################################################################################################
self.h0 = tf.zeros([self.batch_size, self.hidden_dim])
self.h0 = [tf.stack([self.h0, self.h0])] * self.num_recurrent_layers
gen_x = tensor_array_ops.TensorArray(dtype=tf.int32, size=self.sequence_length,
dynamic_size=False, infer_shape=True)
# When current index i < given_num, use the provided tokens as the input at each time step
def _g_recurrence_1(i, x_t, h_tm1, given_num, gen_x):
# h_t = self.g_recurrent_unit(x_t, h_tm1) # hidden_memory_tuple
h_t = []
curr_x = x_t
for layer_i in range(self.num_recurrent_layers):
h_t.append(self.g_recurrent_unit[layer_i](curr_x, h_tm1[layer_i])) # hidden_memory_tuple
curr_hidden_state, curr_prev = tf.unstack(h_t[-1])
curr_x = curr_hidden_state
x_tp1 = ta_emb_x.read(i)
gen_x = gen_x.write(i, ta_x.read(i))
return i + 1, x_tp1, h_t, given_num, gen_x
# When current index i >= given_num, start roll-out, use the output as time step t as the input at time step t+1
def _g_recurrence_2(i, x_t, h_tm1, given_num, 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
h_t = []
curr_x = x_t
for layer_i in range(self.num_recurrent_layers):
h_t.append(self.g_recurrent_unit[layer_i](curr_x, h_tm1[layer_i])) # hidden_memory_tuple
curr_hidden_state, curr_prev = tf.unstack(h_t[-1])
curr_x = curr_hidden_state
o_t = self.g_output_unit(h_t[-1])
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_x = gen_x.write(i, next_token) # indices, batch_size
return i + 1, x_tp1, h_t, given_num, gen_x
i, x_t, h_tm1, given_num, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, given_num, _4: i < given_num,
body=_g_recurrence_1,
loop_vars=(tf.constant(0, dtype=tf.int32),
tf.nn.embedding_lookup(self.g_embeddings, self.start_token), self.h0, self.given_num, gen_x))
_, _, _, _, self.gen_x = control_flow_ops.while_loop(
cond=lambda i, _1, _2, _3, _4: i < self.sequence_length,
body=_g_recurrence_2,
loop_vars=(i, x_t, h_tm1, given_num, self.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
def get_reward(self, sess, input_x, rollout_num, discriminator):
rewards = []
for i in range(rollout_num):
# given_num between 1 to sequence_length - 1 for a part completed sentence
for given_num in range(1, self.sequence_length ):
feed = {self.x: input_x, self.given_num: given_num}
samples = sess.run(self.gen_x, feed)
feed = {discriminator.input_x: samples, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
rewards[given_num - 1] += ypred
# the last token reward
feed = {discriminator.input_x: input_x, discriminator.dropout_keep_prob: 1.0}
ypred_for_auc = sess.run(discriminator.ypred_for_auc, feed)
ypred = np.array([item[1] for item in ypred_for_auc])
if i == 0:
rewards.append(ypred)
else:
# completed sentence reward
rewards[self.sequence_length - 1] += ypred
rewards = np.transpose(np.array(rewards)) / (1.0 * rollout_num) # batch_size x seq_length
return rewards
def create_recurrent_unit(self, num):
if num == 0:
self.Wi = [None] * self.num_recurrent_layers
self.Ui = [None] * self.num_recurrent_layers
self.bi = [None] * self.num_recurrent_layers
self.Wf = [None] * self.num_recurrent_layers
self.Uf = [None] * self.num_recurrent_layers
self.bf = [None] * self.num_recurrent_layers
self.Wog = [None] * self.num_recurrent_layers
self.Uog = [None] * self.num_recurrent_layers
self.bog = [None] * self.num_recurrent_layers
self.Wc = [None] * self.num_recurrent_layers
self.Uc = [None] * self.num_recurrent_layers
self.bc = [None] * self.num_recurrent_layers
# Weights and Bias for input and hidden tensor
self.Wi[num] = tf.identity(self.lstm.Wi[num])
self.Ui[num] = tf.identity(self.lstm.Ui[num])
self.bi[num] = tf.identity(self.lstm.bi[num])
self.Wf[num] = tf.identity(self.lstm.Wf[num])
self.Uf[num] = tf.identity(self.lstm.Uf[num])
self.bf[num] = tf.identity(self.lstm.bf[num])
self.Wog[num] = tf.identity(self.lstm.Wog[num])
self.Uog[num] = tf.identity(self.lstm.Uog[num])
self.bog[num] = tf.identity(self.lstm.bog[num])
self.Wc[num] = tf.identity(self.lstm.Wc[num])
self.Uc[num] = tf.identity(self.lstm.Uc[num])
self.bc[num] = tf.identity(self.lstm.bc[num])
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[num]) +
tf.matmul(previous_hidden_state, self.Ui[num]) + self.bi[num]
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf[num]) +
tf.matmul(previous_hidden_state, self.Uf[num]) + self.bf[num]
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog[num]) +
tf.matmul(previous_hidden_state, self.Uog[num]) + self.bog[num]
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc[num]) +
tf.matmul(previous_hidden_state, self.Uc[num]) + self.bc[num]
)
# 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 update_recurrent_unit(self,num):
# Weights and Bias for input and hidden tensor
self.Wi[num] = self.update_rate * self.Wi[num] + (1 - self.update_rate) * tf.identity(self.lstm.Wi[num])
self.Ui[num] = self.update_rate * self.Ui[num] + (1 - self.update_rate) * tf.identity(self.lstm.Ui[num])
self.bi[num] = self.update_rate * self.bi[num] + (1 - self.update_rate) * tf.identity(self.lstm.bi[num])
self.Wf[num] = self.update_rate * self.Wf[num] + (1 - self.update_rate) * tf.identity(self.lstm.Wf[num])
self.Uf[num] = self.update_rate * self.Uf[num] + (1 - self.update_rate) * tf.identity(self.lstm.Uf[num])
self.bf[num] = self.update_rate * self.bf[num] + (1 - self.update_rate) * tf.identity(self.lstm.bf[num])
self.Wog[num] = self.update_rate * self.Wog[num] + (1 - self.update_rate) * tf.identity(self.lstm.Wog[num])
self.Uog[num] = self.update_rate * self.Uog[num] + (1 - self.update_rate) * tf.identity(self.lstm.Uog[num])
self.bog[num] = self.update_rate * self.bog[num] + (1 - self.update_rate) * tf.identity(self.lstm.bog[num])
self.Wc[num] = self.update_rate * self.Wc[num] + (1 - self.update_rate) * tf.identity(self.lstm.Wc[num])
self.Uc[num] = self.update_rate * self.Uc[num] + (1 - self.update_rate) * tf.identity(self.lstm.Uc[num])
self.bc[num] = self.update_rate * self.bc[num] + (1 - self.update_rate) * tf.identity(self.lstm.bc[num])
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[num]) +
tf.matmul(previous_hidden_state, self.Ui[num]) + self.bi[num]
)
# Forget Gate
f = tf.sigmoid(
tf.matmul(x, self.Wf[num]) +
tf.matmul(previous_hidden_state, self.Uf[num]) + self.bf[num]
)
# Output Gate
o = tf.sigmoid(
tf.matmul(x, self.Wog[num]) +
tf.matmul(previous_hidden_state, self.Uog[num]) + self.bog[num]
)
# New Memory Cell
c_ = tf.nn.tanh(
tf.matmul(x, self.Wc[num]) +
tf.matmul(previous_hidden_state, self.Uc[num]) + self.bc[num]
)
# 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):
self.Wo = tf.identity(self.lstm.Wo)
self.bo = tf.identity(self.lstm.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
def update_output_unit(self):
self.Wo = self.update_rate * self.Wo + (1 - self.update_rate) * tf.identity(self.lstm.Wo)
self.bo = self.update_rate * self.bo + (1 - self.update_rate) * tf.identity(self.lstm.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
def update_params(self):
self.g_embeddings = tf.identity(self.lstm.g_embeddings)
self.g_recurrent_unit = [self.update_recurrent_unit(i) for i in range(self.num_recurrent_layers)]
self.g_output_unit = self.update_output_unit()