def __init__(self, args, is_training=True):
self.args = args
if not is_training:
args.batch_size = 1
args.seq_length = 1
if args.model == 'rnn':
self.cell = rnn_cell.BasicRNNCell(args.rnn_size)
elif args.model == 'gru':
self.cell = rnn_cell.GRUCell(args.rnn_size)
elif args.model == 'lstm':
self.cell = rnn_cell.BasicLSTMCell(args.rnn_size)
else:
raise Exception('model type not supported: {}'.format(args.model))
self.cell = rnn_cell.MultiRNNCell([self.cell] * args.num_layers)
self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length])
self.targets = tf.placeholder(tf.int32, [args.batch_size, args.seq_length]) # Target replication
self.initial_state = self.cell.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnn'):
softmax_w = tf.get_variable('softmax_w', [args.rnn_size, 2])
softmax_b = tf.get_variable('softmax_b', [2])
with tf.device('/cpu:0'):
embedding = tf.get_variable('embedding', [args.vocab_size, args.rnn_size])
inputs = tf.split(1, args.seq_length, tf.nn.embedding_lookup(embedding, self.input_data))
inputs = [tf.squeeze(i, [1]) for i in inputs]
outputs, last_state = seq2seq.rnn_decoder(inputs, self.initial_state,
self.cell, loop_function=None)
output_tf = tf.reshape(tf.concat(1, outputs), [-1, args.rnn_size])
self.logits = tf.nn.xw_plus_b(output_tf, softmax_w, softmax_b)
self.probs = tf.nn.softmax(self.logits)
loss = seq2seq.sequence_loss_by_example(
[self.logits],
[tf.reshape(self.targets, [-1])],
[tf.ones([args.batch_size * args.seq_length])])
self.cost = tf.reduce_sum(loss) / args.batch_size / args.seq_length
self.final_state = last_state
self.lr = tf.Variable(0.0, trainable = False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars, aggregation_method=2), args.grad_clip)
optimizer = tf.train.AdamOptimizer(self.lr)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
def __init__(self, config, is_training=False):
self.config = config
self.batch_size = batch_size = config.batch_size
self.num_steps = num_steps = config.num_steps
self.hidden_size = hidden_size = config.hidden_size
self.num_layers = 1
vocab_size = config.vocab_size
self.max_grad_norm = config.max_grad_norm
self.use_lstm = config.use_lstm
# Placeholders for inputs.
self.input_data = tf.placeholder(tf.int32, [batch_size, num_steps])
self.targets = tf.placeholder(tf.int32, [batch_size, num_steps])
self.initial_state = array_ops.zeros(
array_ops.pack([self.batch_size, self.num_steps]),
dtype=tf.float32).set_shape([None, self.num_steps])
embedding = tf.get_variable(
'embedding', [self.config.vocab_size, self.config.hidden_size])
# Set up ACT cell and inner rnn-type cell for use inside the ACT cell.
with tf.variable_scope("rnn"):
if self.use_lstm:
inner_cell = rnn_cell.BasicLSTMCell(self.config.hidden_size)
else:
inner_cell = rnn_cell.GRUCell(self.config.hidden_size)
with tf.variable_scope("ACT"):
act = ACTCell(self.config.hidden_size,
inner_cell,
config.epsilon,
max_computation=config.max_computation,
batch_size=self.batch_size)
inputs = tf.nn.embedding_lookup(embedding, self.input_data)
inputs = [
tf.squeeze(single_input, [1])
for single_input in tf.split(1, self.config.num_steps, inputs)
]
self.outputs, final_state = rnn(act, inputs, dtype=tf.float32)
# Softmax to get probability distribution over vocab.
output = tf.reshape(tf.concat(1, self.outputs), [-1, hidden_size])
softmax_w = tf.get_variable("softmax_w", [hidden_size, vocab_size])
softmax_b = tf.get_variable("softmax_b", [vocab_size])
self.logits = tf.matmul(
output,
softmax_w) + softmax_b # dim (numsteps*batchsize, vocabsize)
loss = seq2seq.sequence_loss_by_example(
[self.logits], [tf.reshape(self.targets, [-1])],
[tf.ones([batch_size * num_steps])], vocab_size)
# Add up loss and retrieve batch-normalised ponder cost: sum N + sum Remainder.
ponder_cost = act.calculate_ponder_cost(
time_penalty=self.config.ponder_time_penalty)
self.cost = (tf.reduce_sum(loss) / batch_size) + ponder_cost
self.final_state = self.outputs[-1]
if is_training:
self.lr = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradients(self.cost, tvars),
self.max_grad_norm)
optimizer = tf.train.AdamOptimizer(self.config.learning_rate)
self.train_op = optimizer.apply_gradients(zip(grads, tvars))
def __init__(self, args, is_training=True):
if not is_training:
seq_length = 1
else:
seq_length = args.seq_length
if args.model == 'rnn':
cell_gen = rnn_cell.BasicRNNCell(args.rnn_size)
cell_dis = rnn_cell.BasicRNNCell(args.rnn_size)
elif args.model == 'gru':
cell_gen = rnn_cell.GRUCell(args.rnn_size)
cell_dis = rnn_cell.GRUCell(args.rnn_size)
elif args.model == 'lstm':
cell_gen = rnn_cell.BasicLSTMCell(args.rnn_size)
cell_dis = rnn_cell.BasicLSTMCell(args.rnn_size)
else:
raise Exception('model type not supported: {}'.format(args.model))
# Pass the generated sequences and targets (1)
with tf.name_scope('input'):
with tf.name_scope('data'):
self.input_data = tf.placeholder(tf.int32, [args.batch_size, seq_length])
with tf.name_scope('targets'):
self.targets = tf.placeholder(tf.int32, [args.batch_size, seq_length])
############
# Generator
############
with tf.variable_scope('generator'):
self.cell_gen = rnn_cell.MultiRNNCell([cell_gen] * args.num_layers)
self.initial_state_gen = self.cell_gen.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnn'):
softmax_w = tf.get_variable('softmax_w', [args.rnn_size, args.vocab_size])
softmax_b = tf.get_variable('softmax_b', [args.vocab_size])
with tf.device('/cpu:0'):
embedding = tf.get_variable('embedding', [args.vocab_size, args.rnn_size])
inputs_gen = tf.split(1, seq_length, tf.nn.embedding_lookup(
embedding, self.input_data))
inputs_gen = [tf.squeeze(i, [1]) for i in inputs_gen]
outputs_gen, last_state_gen = seq2seq.rnn_decoder(inputs_gen, self.initial_state_gen,
self.cell_gen, loop_function=None)
self.logits_sequence = []
for output_gen in outputs_gen:
logits_gen = tf.nn.xw_plus_b(output_gen, softmax_w, softmax_b)
self.logits_sequence.append(logits_gen)
self.final_state_gen = last_state_gen
################
# Discriminator
################
with tf.variable_scope('discriminator'):
self.cell_dis = rnn_cell.MultiRNNCell([cell_dis] * args.num_layers)
self.initial_state_dis = self.cell_dis.zero_state(args.batch_size, tf.float32)
with tf.variable_scope('rnn'):
softmax_w = tf.get_variable('softmax_w', [args.rnn_size, 2])
softmax_b = tf.get_variable('softmax_b', [2])
inputs_dis = []
embedding = tf.get_variable('embedding', [args.vocab_size, args.rnn_size])
for logit in self.logits_sequence:
inputs_dis.append(tf.matmul(logit, embedding))
# inputs_dis.append(tf.matmul(tf.nn.softmax(logit), embedding))
outputs_dis, last_state_dis = seq2seq.rnn_decoder(inputs_dis,
self.initial_state_dis, self.cell_dis, loop_function=None)
probs, logits = [], []
for output_dis in outputs_dis:
logit = tf.nn.xw_plus_b(output_dis, softmax_w, softmax_b)
prob = tf.nn.softmax(logit)
logits.append(logit)
probs.append(prob)
with tf.name_scope('summary'):
probs = tf.pack(probs)
probs_real = tf.slice(probs, [0,0,1], [args.seq_length, args.batch_size, 1])
variable_summaries(probs_real, 'probability of real')
self.final_state_dis = last_state_dis
#########
# Train
#########
with tf.name_scope('train'):
gen_loss = seq2seq.sequence_loss_by_example(
logits,
tf.unpack(tf.transpose(self.targets)),
tf.unpack(tf.transpose(tf.ones_like(self.targets, dtype=tf.float32))))
self.gen_cost = tf.reduce_sum(gen_loss) / args.batch_size
tf.scalar_summary('training loss', self.gen_cost)
self.lr_gen = tf.Variable(0.0, trainable = False)
self.tvars = tf.trainable_variables()
gen_vars = [v for v in self.tvars if not v.name.startswith("discriminator/")]
if is_training:
gen_grads = tf.gradients(self.gen_cost, gen_vars)
self.all_grads = tf.gradients(self.gen_cost, self.tvars)
gen_grads_clipped, _ = tf.clip_by_global_norm(gen_grads, args.grad_clip)
gen_optimizer = tf.train.AdamOptimizer(self.lr_gen)
self.gen_train_op = gen_optimizer.apply_gradients(
zip(gen_grads_clipped, gen_vars))
with tf.name_scope('summary'):
with tf.name_scope('weight_summary'):
for v in self.tvars:
variable_summaries(v, v.op.name)
if is_training:
with tf.name_scope('grad_summary'):
for var, grad in zip(self.tvars, self.all_grads):
variable_summaries(grad, 'grad/' + var.op.name)
self.merged = tf.merge_all_summaries()
def __init__(self, sess, config, data_feed, log_dir):
vocab_size = len(data_feed.vocab)
self.data_feed = data_feed
with tf.name_scope("io"):
self.inputs = tf.placeholder(dtype=tf.int32,
shape=(None, None),
name="input_seq")
self.input_lens = tf.placeholder(dtype=tf.int32,
shape=(None, ),
name="seq_len")
self.da_labels = tf.placeholder(dtype=tf.int32,
shape=(None, ),
name="dialog_acts")
self.senti_labels = tf.placeholder(
dtype=tf.float32,
shape=(None, data_feed.feature_size[data_feed.SENTI_ID]),
name="sentiments")
self.learning_rate = tf.Variable(float(config.init_lr),
trainable=False)
self.learning_rate_decay_op = self.learning_rate.assign(
self.learning_rate * config.lr_decay)
max_sent_len = array_ops.shape(self.inputs)[1]
batch_size = array_ops.shape(self.inputs)[0]
with variable_scope.variable_scope("word-embedding"):
embedding = tf.get_variable("embedding",
[vocab_size, config.embed_size],
dtype=tf.float32)
input_embedding = embedding_ops.embedding_lookup(
embedding,
tf.squeeze(tf.reshape(self.inputs, [-1, 1]), squeeze_dims=[1]))
input_embedding = tf.reshape(input_embedding,
[-1, max_sent_len, config.embed_size])
with variable_scope.variable_scope("rnn"):
if config.cell_type == "gru":
cell = rnn_cell.GRUCell(config.cell_size)
elif config.cell_type == "lstm":
cell = rnn_cell.LSTMCell(config.cell_size,
use_peepholes=False,
forget_bias=1.0)
elif config.cell_type == "rnn":
cell = rnn_cell.BasicRNNCell(config.cell_size)
else:
raise ValueError("unknown RNN type")
if config.keep_prob < 1.0:
cell = rnn_cell.DropoutWrapper(
cell,
output_keep_prob=config.keep_prob,
input_keep_prob=config.keep_prob)
if config.num_layer > 1:
cell = rnn_cell.MultiRNNCell([cell] * config.num_layer,
state_is_tuple=True)
# and enc_last_state will be same as the true last state
outputs, _ = tf.nn.dynamic_rnn(
cell,
input_embedding,
dtype=tf.float32,
sequence_length=self.input_lens,
)
# get the TRUE last outputs
last_outputs = tf.reduce_sum(
tf.mul(
outputs,
tf.expand_dims(
tf.one_hot(self.input_lens - 1, max_sent_len), -1)), 1)
self.dialog_acts = self.fnn(
last_outputs, data_feed.feature_size[data_feed.DA_ID], [100],
"dialog_act_fnn")
self.sentiments = self.fnn(
last_outputs, data_feed.feature_size[data_feed.SENTI_ID],
[100], "setiment_fnn")
self.loss = tf.reduce_sum(nn_ops.sparse_softmax_cross_entropy_with_logits(self.dialog_acts, self.da_labels)) \
+ tf.reduce_sum(nn_ops.softmax_cross_entropy_with_logits(self.sentiments, self.senti_labels))
self.loss /= tf.to_float(batch_size)
tf.scalar_summary("entropy_loss", self.loss)
self.summary_op = tf.merge_all_summaries()
# weight decay
tvars = tf.trainable_variables()
for v in tvars:
print("Trainable %s" % v.name)
# optimization
if config.op == "adam":
print("Use Adam")
optimizer = tf.train.AdamOptimizer(self.learning_rate)
elif config.op == "rmsprop":
print("Use RMSProp")
optimizer = tf.train.RMSPropOptimizer(self.learning_rate)
else:
print("Use SGD")
optimizer = tf.train.GradientDescentOptimizer(self.learning_rate)
grads, _ = tf.clip_by_global_norm(tf.gradients(self.loss, tvars),
config.grad_clip)
self.train_ops = optimizer.apply_gradients(zip(grads, tvars))
self.saver = tf.train.Saver(tf.all_variables(),
write_version=tf.train.SaverDef.V2)
if log_dir is not None:
train_log_dir = os.path.join(log_dir, "train")
print("Save summary to %s" % log_dir)
self.train_summary_writer = tf.train.SummaryWriter(
train_log_dir, sess.graph)