/
seq2seq_nmt.py
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seq2seq_nmt.py
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import re
import sys
import tensorflow as tf
import numpy as np
import time
import os
from model_nmt import Model
from data_util import load_and_preprocess_data, UNK_TOKEN, START_TOKEN, END_TOKEN, PAD_TOKEN
from util import Progbar, minibatches, padded_batch, tokens_to_sentences
from rouge import rouge_n
from tensorflow.python.layers import core as layers_core
import logging
from datetime import datetime, date
logger = logging.getLogger("project.milestone")
logger.setLevel(logging.DEBUG)
logging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)
global UNK_IDX, START_IDX, END_IDX, PAD_IDX
debug = False
class Config:
"""Holds model hyperparams and data information.
The config class is used to store various hyperparameters and dataset
information parameters. Model objects are passed a Config() object at
instantiation.
Things to add: (?)
- global_step
- learning_rate_decay
- change lr to tf.Variable
"""
batch_size = 100
n_epochs = 40
lr = 0.2
max_grad_norm = 5.
clip_gradients = True
encoder_hidden_units = 20
decoder_hidden_units = 20
class SequencePredictor(Model):
def add_placeholders(self):
"""
Generates placeholder variables to represent the input and target tensors
"""
self.encoder_inputs = tf.placeholder(tf.int32, shape = (None, self.config.max_length_x),
name = "encoder_inputs")
self.decoder_targets = tf.placeholder(tf.int32, shape =(None, self.config.max_length_y),
name = "decoder_targets")
self.decoder_inputs = tf.placeholder(tf.int32, shape=(None, self.config.max_length_y),
name = "decoder_inputs")
self.length_encoder_inputs = tf.placeholder(tf.int32, shape = (None), name = "length_encoder_inputs")
self.length_decoder_inputs = tf.placeholder(tf.int32, shape = (None), name = "length_decoder_inputs")
def create_feed_dict(self, inputs_batch, length_encoder_batch, length_decoder_batch = None,
decoder_inputs_batch = None,
targets_batch = None):
"""
Creates the feed_dict for the model.
"""
if targets_batch is not None:
feed_dict = {
self.encoder_inputs: inputs_batch,
self.decoder_inputs: decoder_inputs_batch,
self.decoder_targets: targets_batch,
self.length_encoder_inputs: length_encoder_batch,
self.length_decoder_inputs: length_decoder_batch,
}
else:
feed_dict = {
self.encoder_inputs: inputs_batch,
self.length_encoder_inputs: length_encoder_batch,
}
return feed_dict
def add_embeddings(self):
"""
Adds an embedding layer that maps from input tokens (integers) to vectors and then
concatenates those vectors:
- Creates a tf.Variable and initializes it with self.pretrained_embeddings.
- Uses the encoder_inputs and decoder_inputs to index into the embeddings tensor,
resulting in two tensor of shape (None, embedding_size).
Returns:
encoder_inputs_embedded: tf.Tensor of shape (None, embed_size)
decoder_inputs_embedded: tf.Tensor of shape (None, embed_size)
"""
E = tf.get_variable("E", initializer = self.pretrained_embeddings)
encoder_inputs_embedded = tf.nn.embedding_lookup(E, self.encoder_inputs)
decoder_inputs_embedded = tf.nn.embedding_lookup(E, self.decoder_inputs)
return encoder_inputs_embedded, decoder_inputs_embedded
def add_prediction_op(self):
"""Runs a seq2seq model on the input using TensorFlows
and returns the final state of the decoder as a prediction.
Returns:
train_preds: tf.Tensor of shape #TODO
pred_outputs: tf.Tensor of shape #TODO
"""
# Encoder
encoder_cell = tf.contrib.rnn.BasicLSTMCell(self.config.encoder_hidden_units)
encoder_inputs_embedded, decoder_inputs_embedded = self.add_embeddings()
_, encoder_final_state = tf.nn.dynamic_rnn(encoder_cell,
encoder_inputs_embedded,
sequence_length = self.length_encoder_inputs,
dtype = tf.float32)
# Helpers for train and inference
self.length_decoder_inputs.set_shape([None])
train_helper = tf.contrib.seq2seq.TrainingHelper(decoder_inputs_embedded,
self.length_decoder_inputs)
start_tokens = tf.fill([tf.shape(encoder_inputs_embedded)[0]], self.config.voc[START_TOKEN])
end_token = self.config.voc[END_TOKEN]
pred_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(E, start_tokens, end_token)
# Decoder
def decode(helper, scope, reuse = None):
# Here could add attn_cell, etc. (see https://gist.github.com/ilblackdragon/)
decoder_cell = tf.contrib.rnn.BasicLSTMCell(self.config.decoder_hidden_units)
projection_layer = layers_core.Dense(self.config.voc_size, use_bias = False)
maximum_iterations = self.config.max_length_y
decoder = tf.contrib.seq2seq.BasicDecoder(decoder_cell,
helper,
encoder_final_state,
output_layer = projection_layer)
outputs, _, _ = tf.contrib.seq2seq.dynamic_decode(decoder,
maximum_iterations = maximum_iterations,
impute_finished = True)
return outputs.rnn_output
train_outputs = decode(train_helper, 'decode')
pred_outputs = tf.argmax(decode(pred_helper, 'decode'),2)
return train_outputs, pred_outputs
def add_loss_op(self, train_preds):
"""
Adds ops to compute the stepwise cross-entropy loss function.
Args:
preds: A tensor of shape (batch_size, 1) containing the last
state of the neural network.
Returns:
loss: A 0-d tensor (scalar)
"""
y = self.decoder_targets
cross_ent = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y,
logits = train_preds)
target_weights = tf.sequence_mask(self.length_decoder_inputs,
self.config.max_length_y,
dtype = train_preds.dtype)
loss = tf.reduce_sum(cross_ent*target_weights)/tf.to_float(self.config.batch_size)
return loss
def add_training_op(self, loss):
"""
Sets up the training Ops.
Creates an optimizer and applies the gradients to all trainable variables.
The Op returned by this function is what must be passed to the
`sess.run()` call to cause the model to train.
Args:
loss: Loss tensor.
Returns:
train_op: The Op for training.
"""
optimizer = tf.train.AdamOptimizer(learning_rate = self.config.lr)
gradients, variables = zip(*optimizer.compute_gradients(loss))
if self.config.clip_gradients:
gradients, _ = tf.clip_by_global_norm(gradients, self.config.max_grad_norm)
self.grad_norm = tf.global_norm(gradients)
train_op = optimizer.apply_gradients(zip(gradients,variables))
return train_op
def add_summary_op(self, loss):
with tf.name_scope("summaries"):
tf.summary.scalar("loss", self.loss)
summary_op = tf.summary.merge_all()
return summary_op
def train_on_batch(self, sess, inputs_batch, targets_batch):
"""
Perform one step of gradient descent on the provided batch of data.
This version also returns the norm of gradients.
"""
inputs_batch_padded, _ = padded_batch(inputs_batch, self.config.max_length_x, self.config.voc)
length_inputs_batch = np.asarray([min(config.max_length_x,len(item)) for item in inputs_batch])
if targets_batch is None:
feed = self.create_feed_dict(inputs_batch_padded, length_inputs_batch)
else:
decoder_batch_padded, _ = padded_batch(targets_batch, self.config.max_length_y,
self.config.voc, option = 'decoder_inputs')
targets_batch_padded, _ = padded_batch(targets_batch, self.config.max_length_y,
self.config.voc, option = 'decoder_targets')
# length_targets_batch = np.asarray([min(config.max_length_y, len(item)+1) for item in targets_batch])
length_targets_batch = np.asarray([config.max_length_y for item in targets_batch])
feed = self.create_feed_dict(inputs_batch_padded,
length_inputs_batch,
length_targets_batch,
decoder_batch_padded,
targets_batch_padded)
_, loss, grad_norm, summ = sess.run([self.train_op, self.loss, self.grad_norm, self.summary_op], feed_dict=feed)
return loss, grad_norm, summ
def predict_on_batch(self, sess, inputs_batch):
"""
Make predictions for the provided batch of data
Args:
sess: tf.Session()
input_batch: np.ndarray of shape (n_samples, #TODO)
Returns:
predictions: np.ndarray of shape (n_samples, max_length_y)
"""
inputs_batch_padded, _ = padded_batch(inputs_batch, self.config.max_length_x, self.config.voc)
length_inputs_batch = np.asarray([min(config.max_length_x,len(item)) for item in inputs_batch])
feed = self.create_feed_dict(inputs_batch_padded, length_inputs_batch)
predictions = sess.run(self.infer_pred, feed_dict=feed)
return predictions
def run_epoch(self, sess, saver, train, dev):
prog = Progbar(target= int(len(train) / self.config.batch_size))
losses, grad_norms = [], []
for i, batch in enumerate(minibatches(train, self.config.batch_size)):
loss, grad_norm, summ = self.train_on_batch(sess, *batch)
losses.append(loss)
grad_norms.append(grad_norm)
prog.update(i + 1, [("train loss", loss)])
print("\nEvaluating on dev set...")
predictions = []
references = []
for batch in minibatches(dev, self.config.batch_size):
inputs_batch, targets_batch = batch
prediction = list(self.predict_on_batch(sess, inputs_batch))
predictions += prediction
references += list(targets_batch)
predictions = [tokens_to_sentences(pred, self.config.idx2word) for pred in predictions]
references = [tokens_to_sentences(ref, self.config.idx2word) for ref in references]
f1, _, _ = rouge_n(predictions, references)
print("- dev rouge f1: {}".format(f1))
return losses, grad_norms, summ, predictions, f1
def fit(self, sess, saver, train, dev):
losses, grad_norms, predictions = [], [], []
best_dev_ROUGE = -1.0
for epoch in range(self.config.n_epochs):
logger.info("Epoch %d out of %d", epoch + 1, self.config.n_epochs)
loss, grad_norm, summ, preds, f1 = self.run_epoch(sess, saver, train, dev)
if writer:
print("Saving graph in ./data/graph/loss.summary")
writer.add_summary(summ, global_step = epoch)
if f1 > best_dev_ROUGE:
best_dev_ROUGE = f1
if saver:
print("New best dev ROUGE! Saving model in ./data/weights/model.weights")
saver.save(sess, './data/weights/model.weights')
losses.append(loss)
grad_norms.append(grad_norm)
predictions.append(preds)
return losses, grad_norms, predictions
def __init__(self, config, pretrained_embeddings):
self.pretrained_embeddings = pretrained_embeddings
self.config = config
self.encoder_inputs = None
self.decoder_inputs = None
self.decoder_targets = None
self.length_encoder_inputs = None
self.length_decoder_inputs = None
self.grad_norm = None
self.build()
if __name__ == '__main__':
# Get data and embeddings
start = time.time()
print("Loading data...")
train, dev, test, _, _, _, max_x, max_y, E, voc = load_and_preprocess_data()
# train, dev, test, _, _, _, max_x, max_y, E, voc = load_and_preprocess_data(output = 'tokens_debug.txt', debug = True)
print("Took {} seconds to load data".format(time.time() - start))
# Set up some parameters.
print(80 * "=")
print("INITIALIZING")
print(80 * "=")
config = Config()
config.voc_size = len(voc)
config.embedding_size = E.shape[1]
config.max_length_x = 250
config.max_length_y = 11
config.voc = voc
config.idx2word = dict([[v,k] for k,v in voc.items()])
UNK_IDX = voc[UNK_TOKEN]
START_IDX = voc[START_TOKEN]
END_IDX = voc[END_TOKEN]
PAD_IDX = voc[PAD_TOKEN]
# Create directory for saver
if not os.path.exists('./data/weights/'):
os.makedirs('./data/weights/')
if not os.path.exists('./data/graphs/'):
os.makedirs('./data/graphs/')
if not os.path.exists('./data/predictions/'):
os.makedirs('./data/predictions/')
# Build model
with tf.Graph().as_default() as graph:
start = time.time()
print("Building model...")
model = SequencePredictor(config, E)
init = tf.global_variables_initializer()
saver = tf.train.Saver()
writer = tf.summary.FileWriter('./data/graphs', tf.get_default_graph())
print("Took {} seconds to build model".format(time.time() - start))
graph.finalize()
with tf.Session(graph = graph) as sess:
sess.run(init)
print(80 * "=")
print("TRAINING")
print(80 * "=")
losses, grad_norms, predictions = model.fit(sess, saver, train, dev)
if not debug:
print(80 * "=")
print("TESTING")
print(80 * "=")
print("Restoring the best model weights found on the dev set")
saver.restore(sess, './data/weights/model.weights')
print("Final evaluation on test set")
predictions = []
references = []
for batch in minibatches(test, model.config.batch_size):
inputs_batch, targets_batch = batch
prediction = list(model.predict_on_batch(sess, inputs_batch))
predictions += prediction
references += list(targets_batch)
predictions = [tokens_to_sentences(pred, model.config.idx2word) for pred in predictions]
references = [tokens_to_sentences(ref, model.config.idx2word) for ref in references]
f1, _, _ = rouge_n(predictions, references)
print("- test ROUGE: {}".format(f1))
print("Writing predictions")
fname = 'predictions' + str(date.today()) + '.txt'
with open(fname, 'w') as f:
for pred, ref in zip(predictions, references):
f.write(pred + '\t' + ref)
f.write('\n')
print("Done!")
writer.close()