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model.py
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model.py
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import tensorflow as tf
from tensorflow.python.ops import rnn_cell
from tensorflow.python.ops import seq2seq
from tensorflow.python.util import nest
from dropgru import DropoutGRUCell, DropoutBasicRNNCell
import numpy as np
class Model():
def __init__(self, args, infer=False):
self.args = args
if infer:
self.batch_size = 1
self.seq_length = 1
else:
self.batch_size = args.batch_size
self.seq_length = args.seq_length
if args.model == 'rnn':
cell_fn = rnn_cell.BasicRNNCell
elif args.model == 'gru':
cell_fn = rnn_cell.GRUCell
elif args.model == 'lstm':
cell_fn = rnn_cell.BasicLSTMCell
elif args.model == 'dropgru' or args.model == 'droprnn':
pass
else:
raise Exception("model type not supported: {}".format(args.model))
if args.model.startswith('drop'):
cells = []
dt1 = DropoutBasicRNNCell
dt2 = DropoutGRUCell
if args.model != 'dropgru':
print("additional layers will be basic RNN")
dt2 = DropoutBasicRNNCell
for ii in range(args.num_layers):
if False and args.learn_input_embedding:
# context-dependent embedding learned as a small RNN before the large GRUs
args.learn_input_embedding = False
if ii == 0:
nc = dt1(args.vocab_size, input_size=args.vocab_size, probofdrop_st=args.dropout, probofdrop_in=0.0)
elif ii == 1:
nc = dt2(args.rnn_size, input_size=args.vocab_size, probofdrop_st=args.dropout, probofdrop_in=args.dropout)
else:
nc = dt2(args.rnn_size, input_size=args.rnn_size, probofdrop_st=args.dropout, probofdrop_in=args.dropout)
else:
# embedding is fixed, context-independent; like word vectors
firstdroprate = 0.0
if args.learn_input_embedding:
firstdroprate = args.dropout
if ii == 0:
nc = dt2(args.rnn_size, input_size=args.vocab_size, probofdrop_st=args.dropout, probofdrop_in=firstdroprate)
else:
nc = dt2(args.rnn_size, input_size=args.rnn_size, probofdrop_st=args.dropout, probofdrop_in=args.dropout)
cells.append(nc)
self.cell = rnn_cell.MultiRNNCell(cells)
self.cellusesdropout = True
else:
print("building basic non-dropout model")
c1 = cell_fn(args.rnn_size)
self.cell = rnn_cell.MultiRNNCell([c1] * args.num_layers)
self.cellusesdropout = False
self.input_data = tf.placeholder(tf.int32, [self.batch_size, self.seq_length], name="x_input_data")
self.targets = tf.placeholder(tf.int32, [self.batch_size, self.seq_length], name="y_targets")
self.initial_state = self.cell.zero_state(self.batch_size, tf.float32)
if args.learn_input_embedding:
self.embedding = tf.get_variable("embedding", [args.vocab_size, args.vocab_size])
else:
self.embedding = tf.placeholder(tf.float32, [args.vocab_size, args.vocab_size], name="embedding")
if self.cellusesdropout:
self._dropMaskOutput = tf.placeholder(dtype=tf.float32, shape=[self.batch_size*self.seq_length, args.rnn_size], name="dropout_output_mask")
self._latest_mask_output = None
with tf.variable_scope('rnnlm'):
softmax_w = tf.get_variable("top_softmax_w", [args.rnn_size, args.vocab_size])
softmax_b = tf.get_variable("top_softmax_b", [args.vocab_size])
inputs = tf.split(1, self.seq_length, tf.nn.embedding_lookup(self.embedding, self.input_data))
inputs = [tf.squeeze(input_, [1]) for input_ in inputs]
def loop(prev, _):
if self.cellusesdropout:
assert(prev.get_shape() == self._dropMaskOutput.get_shape())
prev = tf.matmul(tf.mul(prev, self._dropMaskOutput), softmax_w) + softmax_b
else:
prev = tf.matmul(prev, softmax_w) + softmax_b
prev_symbol = tf.stop_gradient(tf.argmax(prev, 1))
return tf.nn.embedding_lookup(self.embedding, prev_symbol)
self.temperature = tf.placeholder(tf.float32, 1, name="temperature")
# if loop_function is not None, it is used to generate the next input
# otherwise, if it is None, the next input will be from the "inputs" sequence
outputs, last_state = seq2seq.rnn_decoder(inputs, self.initial_state, self.cell, loop_function=loop if infer else None, scope='rnnlm')
output = tf.reshape(tf.concat(1, outputs), [self.batch_size*self.seq_length, args.rnn_size])
if self.cellusesdropout:
assert(output.get_shape() == self._dropMaskOutput.get_shape())
self.logits = tf.matmul(tf.mul(output, self._dropMaskOutput), softmax_w) + softmax_b
else:
self.logits = tf.matmul(output, softmax_w) + softmax_b
self.probs = tf.nn.softmax(self.logits)
self.probswithtemp = tf.nn.softmax(self.logits / self.temperature)
# 1.44... term converts cost from units of "nats" to units of "bits"
self.cost = seq2seq.sequence_loss([self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([self.batch_size * self.seq_length])]) * 1.44269504088896340736
self.pred_entropy = tf.reduce_sum(tf.mul(self.probs, tf.log(self.probs + 1e-12)), 1) * (-1.44269504088896340736)
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable=False, name="learningrate")
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
args.grad_clip)
optimizer = tf.train.AdamOptimizer(self.lr)
zipgradvars = zip(grads, tvars)
self.train_op = optimizer.apply_gradients(zipgradvars)
# for tensorboard
tb_cost = tf.scalar_summary('cost_train', self.cost)
tb_predent = tf.scalar_summary('prediction_entropy_train', tf.reduce_mean(self.pred_entropy))
mergethese = [tb_cost, tb_predent]
for grad,var in zipgradvars:
mergethese.append(tf.histogram_summary(var.name+'_value', var))
mergethese.append(tf.histogram_summary(var.name+'_grad', grad))
self.tbsummary = tf.merge_summary(mergethese)
def extrafeed(self, feed):
if self.args.learn_input_embedding == False:
feed[self.embedding] = np.identity(self.args.vocab_size, dtype=np.float32)
if self.cellusesdropout:
for cell in self.cell._cells:
feed.update(cell.get_mask_feed())
assert(self._latest_mask_output is not None)
feed[self._dropMaskOutput] = self._latest_mask_output
return feed
def resetstate(self):
if nest.is_sequence(self.initial_state):
if nest.is_sequence(self.initial_state[0]):
state = tuple(tuple(is2.eval() for is2 in ist) for ist in self.initial_state)
else:
state = tuple(ist.eval() for ist in self.initial_state)
else:
state = self.initial_state.eval()
return state
def resetweights(self, expectationdropout=False):
if self.cellusesdropout:
for cell in self.cell._cells:
if expectationdropout:
cell.expectation_drop_mask(self.batch_size)
else:
cell.random_drop_mask(self.batch_size)
outdropsize = (self.batch_size*self.seq_length, self.args.rnn_size)
if self.cellusesdropout:
if expectationdropout:
self._latest_mask_output = np.ones(outdropsize) * (1.0 - float(self.args.dropout))
else:
self._latest_mask_output = np.random.binomial(1, (1.0 - float(self.args.dropout)), size=outdropsize)
def sample(self, sess, chars, vocab, num=200, prime=' ', sampling_type=1, temperature=1.0):
self.resetweights(expectationdropout=True)
state = self.resetstate()
for char in prime[:-1]:
x = np.zeros((1, 1))
x[0, 0] = vocab[char]
feed = {self.input_data: x, self.initial_state: state}
feed = self.extrafeed(feed)
[state] = sess.run([self.final_state], feed)
def weighted_pick(weights):
t = np.cumsum(weights)
s = np.sum(weights)
return(int(np.searchsorted(t, np.random.rand(1)*s)))
ret = prime
char = prime[-1]
for n in range(num):
x = np.zeros((1, 1))
x[0, 0] = vocab[char]
feed = {self.input_data: x, self.initial_state: state, self.temperature: (float(temperature),)}
feed = self.extrafeed(feed)
[probs, state] = sess.run([self.probswithtemp, self.final_state], feed)
p = probs[0]
if sampling_type == 0:
sample = np.argmax(p)
elif sampling_type == 2:
if char == ' ':
sample = weighted_pick(p)
else:
sample = np.argmax(p)
else: # sampling_type == 1 default:
sample = weighted_pick(p)
pred = chars[sample]
ret += pred
char = pred
return ret