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CNN.py
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139 lines (117 loc) · 6.83 KB
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import numpy as np
import tensorflow as tf
from utils.scorer import *
from tensorflow.contrib.layers import fully_connected
from preprocess import *
class CNN():
def __init__(self, params, vocab, my_embeddings=None):
self.params = params
self.vocab = vocab
for key in params:
setattr(self, key, params[key])
if self.pretrain:
self.my_embeddings = np.expand_dims(my_embeddings, 2)
def build_embedding(self):
if self.pretrain:
embeddings = tf.Variable(tf.constant(0.0, shape=[len(self.vocab), self.embedding_size]),
trainable=False, name="W")
embedding_placeholder = tf.placeholder(tf.float32, [len(self.vocab), self.embedding_size])
embedding_init = embeddings.assign(embedding_placeholder)
else:
embedding_placeholder = tf.get_variable("embedding",
initializer=tf.random_uniform(
[len(self.vocab), self.embedding_size], -1, 1),
dtype=tf.float32)
embedding_placeholder_expanded = tf.expand_dims(embedding_placeholder, -1)
return embedding_placeholder_expanded
def build(self):
self.embedding_placeholder = self.build_embedding()
self.train_inputs = tf.placeholder(tf.int32, shape=[None, None], name="inputs")
self.embed = tf.nn.embedding_lookup(self.embedding_placeholder, self.train_inputs)
pooled_outputs = list()
self.learning_rate_placeholder = tf.placeholder(tf.float32, [])
self.keep_prob = tf.placeholder(tf.float32)
self.relation = tf.placeholder(tf.int64, [None])
self.direction = tf.placeholder(tf.int64, [None])
self.max_len = tf.placeholder(tf.int32)
for i, filter_size in enumerate(self.filter_sizes):
filter_shape = [filter_size, self.embedding_size, 1, self.num_filters]
b = tf.Variable(tf.constant(0.1, shape=[self.num_filters]))
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name="W")
conv = tf.nn.conv2d(self.embed, W, strides=[1, 1, 1, 1], padding="VALID")
relu = tf.nn.relu(tf.nn.bias_add(conv, b))
#pooled = tf.nn.max_pool(relu, ksize=[1, self.max_len - filter_size + 1, 1, 1], strides=[1, 1, 1, 1], padding='VALID')
pooled = tf.reduce_max(relu, axis=1, keep_dims=True)
pooled_outputs.append(pooled)
num_filters_total = self.num_filters * len(self.filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
drop = tf.nn.dropout(self.h_pool_flat, self.keep_prob)
self.relation_predictions = fully_connected(drop, self.n_outputs, activation_fn=tf.sigmoid)
self.direction_predictions = fully_connected(drop, 2, activation_fn=tf.sigmoid)
self.xentropy_rel = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.relation,
logits=self.relation_predictions)
self.xentropy_dir = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=self.direction,
logits=self.direction_predictions)
self.loss_rel = tf.reduce_mean(self.xentropy_rel)
self.loss_dir = tf.reduce_mean(self.xentropy_dir)
self.loss = (self.loss_dir + 3 * self.loss_rel) / 4
self.predicted_rel = tf.argmax(self.relation_predictions, 1)
self.predicted_dir = tf.argmax(self.direction_predictions, 1)
self.accuracy_rel = tf.reduce_mean(
tf.cast(tf.equal(self.predicted_rel, self.relation), tf.float32))
self.accuracy_dir = tf.reduce_mean(
tf.cast(tf.equal(self.predicted_dir, self.direction), tf.float32))
self.accuracy = (self.accuracy_dir + self.accuracy_rel) / 2
self.training_op = tf.train.AdamOptimizer(learning_rate=self.learning_rate_placeholder).minimize(self.loss)
def run_model(self, batches, test_batches, labels, dic):
init = tf.global_variables_initializer()
with tf.Session() as self.sess:
init.run()
epoch = 1
while True:
## Train
if epoch == 20:
self.learning_rate = self.learning_rate * 0.1
if epoch == 50:
self.learning_rate = self.learning_rate * 0.5
epoch_loss = float(0)
acc_train = 0
epoch += 1
for (X_batch, X_len, y_batch) in batches:
feed_dict = {self.train_inputs: X_batch,
self.keep_prob: self.keep_ratio,
self.relation: y_batch[:, 0],
self.direction: y_batch[:, 1],
self.learning_rate_placeholder: self.learning_rate,
self.max_len: X_batch.shape[1]
}
if self.pretrain:
feed_dict[self.embedding_placeholder] = self.my_embeddings
_, loss_val= self.sess.run([self.training_op, self.loss], feed_dict=feed_dict)
acc_train += self.accuracy.eval(feed_dict=feed_dict)
epoch_loss += loss_val
## Test
acc_test = 0
test_predictions = list()
for (X_batch, X_len, y_batch) in test_batches:
feed_dict = {self.train_inputs: X_batch,
self.keep_prob: 1,
self.relation: y_batch[:, 0],
self.direction: y_batch[:, 1],
self.learning_rate_placeholder: self.learning_rate,
self.max_len: X_batch.shape[1]}
if self.pretrain:
feed_dict[self.embedding_placeholder] = self.my_embeddings
dir = self.predicted_dir.eval(feed_dict=feed_dict)
rel = self.predicted_rel.eval(feed_dict=feed_dict)
test_predictions.extend([true_label(rel[i], dir[i], dic) for i in range(len(dir))])
evaluate(labels, test_predictions)
print(epoch, "Train accuracy:", acc_train / float(len(batches)),
"Loss: ", epoch_loss / float(len(batches)),
"Test accuracy: ", acc_test / float(len(test_batches)))
if epoch == self.epochs:
test_predictions = np.transpose(test_predictions)
break
#save_path = saver.save(self.sess, "/tmp/model.ckpt")
return test_predictions, acc_test / float(len(test_batches))