class RNN_Model():
def load_data(self):
"""Loads train/dev/test data and builds vocabulary."""
self.train_data, self.dev_data, self.test_data = tr.simplified_data(
300, 70, 100)
# build vocab from training data
self.vocab = Vocab()
train_sents = [t.get_words() for t in self.train_data]
self.vocab.construct(list(itertools.chain.from_iterable(train_sents)))
self.w2v_vocab, w2v_embd, embedding_dict = self.load_w2v()
self.embedding_dim = len(w2v_embd[0])
self.w2v_vocab_size = len(self.w2v_vocab)
self.vocab_size = len(self.vocab)
embeddings_tmp = []
for i in range(self.vocab_size):
item = self.vocab.decode(i)
if item in self.w2v_vocab:
embeddings_tmp.append(embedding_dict[item])
# print("Found word {}".format(item))
else:
# print("Couldn't find {}.".format(item))
rand_num = np.random.uniform(low=-0.2,
high=0.2,
size=self.embedding_dim)
embeddings_tmp.append(rand_num)
self.embed = np.asarray(embeddings_tmp)
def inference(self, tree, predict_only_root=True):
"""For a given tree build the RNN models computation graph up to where it
may be used for inference.
Args:
tree: a Tree object on which to build the computation graph for the RNN
Returns:
softmax_linear: Output tensor with the computed logits.
"""
node_tensors = self.add_model(tree.root)
if predict_only_root:
node_tensors = node_tensors[tree.root]
else:
node_tensors = [
tensor for node, tensor in node_tensors.items()
if node.label != 2
]
node_tensors = tf.concat(node_tensors, 0)
return self.add_projections(node_tensors)
def add_model_vars(self):
'''
You model contains the following parameters:
embedding: tensor(vocab_size, embed_size)
W1: tensor(2* embed_size, embed_size)
b1: tensor(1, embed_size)
U: tensor(embed_size, output_size)
bs: tensor(1, output_size)
Hint: Add the tensorflow variables to the graph here and *reuse* them while building
the compution graphs for composition and projection for each tree
Hint: Use a variable_scope "Composition" for the composition layer, and
"Projection") for the linear transformations preceding the softmax.
Hint: Look up tf.get_variable
'''
with tf.variable_scope('Composition'):
### YOUR CODE HERE
# embedding = tf.get_variable(
# "embedding", (len(self.vocab), self.config.embed_size))
embedding = tf.get_variable(
"embedding",
shape=[self.vocab_size, self.embedding_dim],
initializer=tf.constant_initializer(self.embed),
trainable=False)
# embedding = tf.Variable(
# tf.constant(0.0, shape=[self.vocab_size, self.embedding_dim]),
# trainable=False,
# name="embedding")
# self.embedding_placeholder = tf.placeholder(
# tf.float32, [self.vocab_size, self.embedding_dim])
# self.embedding_init = embedding.assign(self.embedding_placeholder)
# embedding = tf.get_variable("embedding", shape=[self.w2v_vocab_size, self.config.embed_size],
# initializaer=tf.constant_initializer(self.embed), trainable=False)
W1 = tf.get_variable("W1",
(self.embedding_dim, self.embedding_dim))
b1 = tf.get_variable("b1", (1, self.embedding_dim))
### END YOUR CODE
with tf.variable_scope('Projection'):
### YOUR CODE HERE
U = tf.get_variable("U",
(self.embedding_dim, self.config.label_size))
bs = tf.get_variable("bs", (1, self.config.label_size))
### END YOUR CODE
def add_model(self, node):
"""Recursively build the model to compute the phrase embeddings in the tree
Hint: Refer to tree.py and vocab.py before you start. Refer to
the model's vocab with self.vocab
Hint: Reuse the "Composition" variable_scope here
Hint: Store a node's vector representation in node.tensor so it can be
used by its parent
Hint: If node is a leaf node, it's vector representation is just that of the
word vector (see tf.gather()).
Args:
node: a Node object
Returns:
node_tensors: Dict: key = Node, value = tensor(1, embed_size)
"""
with tf.variable_scope('Composition', reuse=True):
### YOUR CODE HERE
embedding = tf.get_variable("embedding")
W1 = tf.get_variable("W1")
b1 = tf.get_variable("b1")
# the variables are already stored in self?
## END YOUR CODE
node_tensors = dict()
curr_node_tensor = None
if node.isLeaf:
### YOUR CODE HERE
# word_id = self.vocab.encode(node.word)
# embedded_chars = tf.nn.embedding_lookup(embedding, word_id)
# curr_node_tensor = tf.unstack(embedded_chars, 1, 1)
word_id = self.vocab.encode(node.word)
curr_node_tensor = tf.expand_dims(tf.gather(embedding, word_id), 0)
### END YOUR CODE
else:
node_input = tf.zeros((1, self.embedding_dim))
for child in node.children:
node_tensors.update(self.add_model(child))
node_input = tf.add(node_input, node_tensors[child])
### YOUR CODE HERE
curr_node_tensor = tf.nn.relu(tf.matmul(node_input, W1) + b1)
### END YOUR CODE
node_tensors[node] = curr_node_tensor
return node_tensors
def add_projections(self, node_tensors):
"""Add projections to the composition vectors to compute the raw sentiment scores
Hint: Reuse the "Projection" variable_scope here
Args:
node_tensors: tensor(?, embed_size)
Returns:
output: tensor(?, label_size)
"""
logits = None
### YOUR CODE HERE
with tf.variable_scope('Projection', reuse=True):
U = tf.get_variable("U")
bs = tf.get_variable("bs")
logits = tf.matmul(node_tensors, U) + bs
### END YOUR CODE
return logits
def loss(self, logits, labels):
"""Adds loss ops to the computational graph.
Hint: Use sparse_softmax_cross_entropy_with_logits
Hint: Remember to add l2_loss (see tf.nn.l2_loss)
Args:
logits: tensor(num_nodes, output_size)
labels: python list, len = num_nodes
Returns:
loss: tensor 0-D
"""
loss = None
# YOUR CODE HERE
with tf.variable_scope('Composition', reuse=True):
W1 = tf.get_variable("W1")
with tf.variable_scope('Projection', reuse=True):
U = tf.get_variable("U")
loss = tf.reduce_sum(
tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels,
logits=logits)) + self.config.l2 * tf.nn.l2_loss(
W1) + self.config.l2 * tf.nn.l2_loss(U)
# END YOUR CODE
return loss
def training(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. See
https://www.tensorflow.org/versions/r0.7/api_docs/python/train.html#Optimizer
for more information.
Hint: Use tf.train.GradientDescentOptimizer for this model.
Calling optimizer.minimize() will return a train_op object.
Args:
loss: tensor 0-D
Returns:
train_op: tensorflow op for training.
"""
train_op = None
# YOUR CODE HERE
train_op = tf.train.GradientDescentOptimizer(
self.config.lr).minimize(loss)
# END YOUR CODE
return train_op
def predictions(self, y):
"""Returns predictions from sparse scores
Args:
y: tensor(?, label_size)
Returns:
predictions: tensor(?,1)
"""
predictions = None
# YOUR CODE HERE
predictions = tf.argmax(y, axis=1)
# END YOUR CODE
return predictions
def __init__(self, config):
self.config = config
self.load_data()
def predict(self, trees, weights_path, get_loss=False):
"""Make predictions from the provided model."""
results = []
losses = []
for i in range(int(math.ceil(len(trees) / float(RESET_AFTER)))):
with tf.Graph().as_default(), tf.Session() as sess:
self.add_model_vars()
# sess.run(
# self.embedding_init,
# feed_dict={self.embedding_placeholder: self.embed})
saver = tf.train.Saver()
saver.restore(sess, weights_path)
for tree in trees[i * RESET_AFTER:(i + 1) * RESET_AFTER]:
logits = self.inference(tree, True)
predictions = self.predictions(logits)
root_prediction = sess.run(predictions)[0]
if root_prediction == 1:
root_prediction = 4
if get_loss:
root_label = tree.root.label
loss = sess.run(self.loss(logits, [root_label]))
losses.append(loss)
results.append(root_prediction)
return results, losses
def run_epoch(self, new_model=False, verbose=True):
step = 0
loss_history = []
while step < len(self.train_data):
with tf.Graph().as_default(), tf.Session() as sess:
self.add_model_vars()
# sess.run(
# self.embedding_init,
# feed_dict={self.embedding_placeholder: self.embed})
if new_model:
init = tf.global_variables_initializer()
sess.run(init)
else:
saver = tf.train.Saver()
saver.restore(sess,
'./weights/%s.temp' % self.config.model_name)
for _ in range(RESET_AFTER):
if step >= len(self.train_data):
break
tree = self.train_data[step]
logits = self.inference(tree)
# print(sess.run(logits))
labels = [l for l in tree.labels if l != 2]
if labels[0] == 4:
labels = [1]
# print(labels)
loss = self.loss(logits, labels)
train_op = self.training(loss)
loss, _ = sess.run([loss, train_op])
loss_history.append(loss)
if verbose:
sys.stdout.write('\r{} / {} : loss = {}'.format(
step, len(self.train_data), np.mean(loss_history)))
sys.stdout.flush()
step += 1
saver = tf.train.Saver()
if not os.path.exists("./weights"):
os.makedirs("./weights")
saver.save(sess, './weights/%s.temp' % self.config.model_name)
train_preds, _ = self.predict(
self.train_data, './weights/%s.temp' % self.config.model_name)
val_preds, val_losses = self.predict(self.dev_data,
'./weights/%s.temp1' %
self.config.model_name,
get_loss=True)
train_labels = [t.root.label for t in self.train_data]
val_labels = [t.root.label for t in self.dev_data]
train_acc = np.equal(train_preds, train_labels).mean()
val_acc = np.equal(val_preds, val_labels).mean()
print()
print('Training acc (only root node): {}'.format(train_acc))
print('Validation acc (only root node): {}'.format(val_acc))
print('Confusion matrix:')
print(self.make_conf(train_labels, train_preds))
print(self.make_conf(val_labels, val_preds))
return train_acc, val_acc, loss_history, np.mean(val_losses)
def train(self, verbose=True):
complete_loss_history = []
train_acc_history = []
val_acc_history = []
prev_epoch_loss = float('inf')
best_val_loss = float('inf')
best_val_epoch = 0
stopped = -1
for epoch in range(self.config.max_epochs):
print('epoch %d' % epoch)
if epoch == 0:
train_acc, val_acc, loss_history, val_loss = self.run_epoch(
new_model=True)
else:
train_acc, val_acc, loss_history, val_loss = self.run_epoch()
complete_loss_history.extend(loss_history)
train_acc_history.append(train_acc)
val_acc_history.append(val_acc)
#lr annealing
epoch_loss = np.mean(loss_history)
if epoch_loss > prev_epoch_loss * self.config.anneal_threshold:
self.config.lr /= self.config.anneal_by
print('annealed lr to %f' % self.config.lr)
prev_epoch_loss = epoch_loss
# save if model has improved on val
if val_loss < best_val_loss:
best_val_loss = val_loss
best_val_epoch = epoch
# if model has not improved for a while stop
if epoch - best_val_epoch > self.config.early_stopping:
stopped = epoch
# break
if verbose:
sys.stdout.write('\r')
sys.stdout.flush()
print('\n\nstopped at %d\n' % stopped)
return {
'loss_history': complete_loss_history,
'train_acc_history': train_acc_history,
'val_acc_history': val_acc_history,
}
def make_conf(self, labels, predictions):
confmat = np.zeros([2, 2])
labels = [l if l != 4 else 1 for l in labels]
predictions = [p if p != 4 else 1 for p in predictions]
for l, p in zip(labels, predictions):
confmat[l, p] += 1
return confmat
def load_w2v(self):
vocab = []
embd = []
e_dict = {}
# change 100d to 50d for smaller-dimension GloVe embedding
file = open("./glove.6B.100d.txt", 'r', encoding='UTF-8')
for line in file.readlines():
row = line.strip().split(' ')
vocab.append(row[0])
embd.append(row[1:])
e_dict[row[0]] = [float(i) for i in row[1:]]
print("Loaded word2vec!")
file.close()
return vocab, embd, e_dict
print('-' * 20)
print('epoch: {}'.format(epoch + 1))
for (x, t) in train_dataloader:
train_step(x, t, depth_t)
for (x, t) in val_dataloader:
val_step(x, t, depth_t)
print('loss: {:.3f}, val_loss: {:.3}'.format(train_loss.result(),
val_loss.result()))
for idx, (x, t) in enumerate(test_dataloader):
preds = test_step(x)
source = x.numpy().reshape(-1)
target = t.numpy().reshape(-1)
out = tf.argmax(preds, axis=-1).numpy().reshape(-1)
source = ' '.join(en_vocab.decode(source))
target = ' '.join(ja_vocab.decode(target))
out = ' '.join(ja_vocab.decode(out))
print('>', source)
print('=', target)
print('<', out)
print()
if idx >= 9:
break
class StarTrekCharGenerationDataset(Dataset):
def __init__(self, hparams: HyperParams, data_split: str, sep_line="\n"):
assert Splits.check_split(data_split)
self.hparams = hparams
self.root = Path(self.hparams.root)
self.data_split = data_split
self.sep_line = sep_line
self.path_data = self.download()
self.lines = self.preprocess_data()
self.vocab = Vocab(list(self.sep_line.join(self.lines)))
self.text = self.train_val_test_split()
self.tensor = self.get_sequences()
if self.hparams.verbose:
self.show_samples()
print(dict(vocab_size=len(self.vocab)))
def download(self) -> List[str]:
url = "https://github.com/chiayewken/sutd-materials/releases/download/v0.1.0/star_trek_transcripts_all_episodes.csv"
path = self.root / Path(url).name
if not path.exists():
download_url(url, str(self.root), filename=path.name)
assert path.exists()
return path
def preprocess_data(self) -> List[str]:
with open(str(self.path_data)) as f:
return [
line.strip().strip(",") for line in f
if "NEXTEPISODE" not in line
]
def train_val_test_split(self, fractions=(0.8, 0.1, 0.1)) -> str:
indices_all = list(range(len(self.lines)))
indices_split = shuffle_multi_split(indices_all, fractions)
indices = indices_split[[Splits.train, Splits.val,
Splits.test].index(self.data_split)]
lines = [self.lines[i] for i in indices]
text = self.sep_line.join(lines)
if self.hparams.verbose:
print(dict(lines=len(lines), text=len(text)))
return text
def get_sequences(self) -> torch.Tensor:
path_cache = self.root / f"cache_tensor_{self.data_split}.pt"
token_start = self.vocab.stoi[self.vocab.start]
if not path_cache.exists():
encoded = self.vocab.encode(list(self.text))
sequences = []
for i in tqdm(range(len(encoded) - self.hparams.seq_len)):
sequences.append([token_start] +
encoded[i:i + self.hparams.seq_len])
tensor = torch.from_numpy(np.array(sequences)).type(torch.long)
torch.save(tensor, str(path_cache))
tensor = torch.load(str(path_cache))
if self.hparams.verbose:
print(dict(tensor=tensor.shape))
return tensor
def __len__(self):
return self.tensor.shape[0]
def __getitem__(self, i):
sequence = self.tensor[i, :]
return sequence[:-1], sequence[1:]
def sequence_to_text(self, sequence: torch.Tensor):
assert sequence.ndim == 1
return "".join(self.vocab.decode(sequence.numpy()))
def show_samples(self, num=3):
print(self.__class__.__name__, dict(show_samples=num))
indices = np.random.choice(len(self), size=num, replace=False)
for i in indices:
sequence = self.tensor[i, :]
print(dict(text=self.sequence_to_text(sequence), raw=sequence))
def extract_quotes(self, lines: List[str]) -> List[str]:
def check_speaker_start(s: str) -> bool:
for char in s:
if char.isupper():
pass
elif char == ":":
return True
else:
return False
return False
def handle_newlines(_lines: List[str]) -> List[str]:
out = []
for line in _lines:
out.extend(line.split(self.sep_line))
return out
def check_line_finish(s: str) -> bool:
return s[-1] in "!.?"
lines = handle_newlines(lines)
quotes = []
for row in csv.reader(lines, delimiter=",", quotechar='"'):
for part in row:
if not part:
continue
if not check_line_finish(part):
continue
if not check_speaker_start(part):
continue
quotes.append(part)
if self.hparams.verbose:
print(dict(extract_quotes=len(quotes)))
return quotes
