def __init__(
self, sequence_length, num_classes, vocab_size,
embedding_size, filter_sizes, num_filters, num_blocks, l2_reg_lambda=0.0):
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
self.is_training = tf.placeholder(tf.bool, name="is_training")
self.filter_size = 3
self.num_filters = num_filters
self.use_region_emb = True
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(
tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
name="W")
if self.use_region_emb:
self.region_size = 5
self.region_radius = self.region_size / 2
self.k_matrix_embedding = tf.Variable(tf.random_uniform([vocab_size, self.region_size, embedding_size], -1.0, 1.0), name="k_matrix")
self.embedded_chars = self.region_embedding(self.input_x)
sequence_length = int(self.embedded_chars.shape[1])
else:
self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
# Create a convolution + maxpool layer for each filter size
# 2.two layers of convs
conv = self.dpcnn_two_layers_conv(self.embedded_chars_expanded)
# 2.1 skip connection: add and activation
b = tf.get_variable("b-inference", [self.num_filters])
conv = tf.nn.relu(tf.nn.bias_add(conv, b), "relu-inference")
conv = conv + self.embedded_chars_expanded
# 3.repeat of building blocks
for i in range(num_blocks):
conv = self.dpcnn_pooling_two_conv(conv, i)
# 4.max pooling
seq_length1 = conv.get_shape().as_list()[1]
seq_length2 = conv.get_shape().as_list()[2]
pooling = tf.nn.max_pool(conv, ksize=[1, seq_length1, seq_length2, 1], strides=[1, 1, 1, 1], padding='VALID',name="pool")
fc_hidden_size = pooling.get_shape().as_list()[-1]
self.h_pool_flat = tf.squeeze(pooling)
# Fully Connected Layer
with tf.name_scope("fc"):
W_fc = tf.Variable(tf.truncated_normal(shape=[fc_hidden_size, fc_hidden_size],\
stddev=0.1, dtype=tf.float32), name="W_fc")
self.fc = tf.matmul(self.h_pool_flat, W_fc)
self.fc_bn = tf.layers.batch_normalization(self.fc, training=self.is_training)
self.fc_out = tf.nn.relu(self.fc_bn, name="relu")
# Highway Layer
self.highway = highway(self.fc_out, self.fc_out.get_shape()[1], num_layers=1, bias=-0.5, scope="Highway")
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.highway, self.dropout_keep_prob)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W = tf.get_variable(
"W",
shape=[fc_hidden_size, num_classes],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.Variable(tf.constant(0.1, shape=[num_classes]), name="b")
l2_loss += tf.nn.l2_loss(W)
l2_loss += tf.nn.l2_loss(b)
self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name="scores")
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# Calculate mean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, "float"), name="accuracy")
self.correct_pred_num = tf.reduce_sum(tf.cast(correct_predictions, tf.int32), name="correct_pred_num")
def __init__(self,
sequence_length,
num_classes,
vocab_size,
embedding_size,
filter_sizes,
num_filters,
l2_reg_lambda=0.0):
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length],
name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes],
name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32,
name="dropout_keep_prob")
self.is_training = tf.placeholder(tf.bool, name="is_training")
self.use_region_emb = True
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(tf.random_uniform(
[vocab_size, embedding_size], -1.0, 1.0),
name="W")
if self.use_region_emb:
self.region_size = 5
self.region_radius = self.region_size / 2
self.k_matrix_embedding = tf.Variable(tf.random_uniform(
[vocab_size, self.region_size, embedding_size], -1.0, 1.0),
name="k_matrix")
self.embedded_chars = self.region_embedding(self.input_x)
sequence_length = int(self.embedded_chars.shape[1])
else:
self.embedded_chars = tf.nn.embedding_lookup(
self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(
self.embedded_chars, -1)
# Create a convolution + maxpool layer for each filter size
pooled_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.name_scope("conv-maxpool-%s" % filter_size):
# Convolution Layer
filter_shape = [filter_size, embedding_size, 1, num_filters]
W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1),
name="W")
conv = tf.nn.conv2d(self.embedded_chars_expanded,
W,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv")
conv_bn = tf.layers.batch_normalization(
conv, training=self.is_training)
# Apply nonlinearity
h = tf.nn.relu(conv_bn, name="relu")
# Maxpooling over the outputs
pool_size = sequence_length - filter_size + 1
pooled = self._max_pooling(h, pool_size)
pooled_outputs.append(pooled)
# Combine all the pooled features
num_filters_total = num_filters * len(filter_sizes)
self.h_pool = tf.concat(pooled_outputs, 3)
self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])
# Fully Connected Layer
with tf.name_scope("fc"):
fc_hidden_size = num_filters_total
W_fc = tf.Variable(tf.truncated_normal(shape=[num_filters_total, fc_hidden_size],\
stddev=0.1, dtype=tf.float32), name="W_fc")
self.fc = tf.matmul(self.h_pool_flat, W_fc)
self.fc_bn = tf.layers.batch_normalization(
self.fc, training=self.is_training)
self.fc_out = tf.nn.relu(self.fc_bn, name="relu")
# Highway Layer
self.highway = highway(self.fc_out,
self.fc_out.get_shape()[1],
num_layers=1,
bias=-0.5,
scope="Highway")
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.highway, self.dropout_keep_prob)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W_out = tf.Variable(tf.truncated_normal(shape=[fc_hidden_size, num_classes],\
stddev=0.1, dtype=tf.float32), name="W_out")
b_out = tf.Variable(tf.constant(0.1, shape=[num_classes]),
name="b_out")
l2_loss += tf.nn.l2_loss(W_out)
l2_loss += tf.nn.l2_loss(b_out)
self.scores = tf.nn.xw_plus_b(self.h_drop,
W_out,
b_out,
name="scores")
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# Calculate mean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(
logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions,
tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions,
"float"),
name="accuracy")
self.correct_pred_num = tf.reduce_sum(tf.cast(
correct_predictions, tf.int32),
name="correct_pred_num")
def __init__(self,
sequence_length,
num_classes,
vocab_size,
embedding_size,
filter_sizes=[7, 5],
num_filters=[8, 14],
top_k=6,
k1=12,
l2_reg_lambda=0.0):
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length],
name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes],
name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32,
name="dropout_keep_prob")
self.is_training = tf.placeholder(tf.bool, name="is_training")
self.use_region_emb = False
self.fc_hidden_size = 2048
self.use_dialate_conv = False
# Keeping track of l2 regularization loss (optional)
l2_loss = tf.constant(0.0)
# Embedding layer
with tf.device('/cpu:0'), tf.name_scope("embedding"):
self.W = tf.Variable(tf.random_uniform(
[vocab_size, embedding_size], -1.0, 1.0),
name="W")
if self.use_region_emb:
self.region_size = 5
self.region_radius = self.region_size / 2
self.k_matrix_embedding = tf.Variable(tf.random_uniform(
[vocab_size, self.region_size, embedding_size], -1.0, 1.0),
name="k_matrix")
self.embedded_chars = self.region_embedding(self.input_x)
sequence_length = int(self.embedded_chars.shape[1])
else:
self.embedded_chars = tf.nn.embedding_lookup(
self.W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(
self.embedded_chars, -1)
# Create a dcnn + dynamic k max pooling layer
with tf.name_scope("conv_pooling_layer"):
if self.use_dialate_conv:
#first layer
W1 = tf.Variable(tf.truncated_normal(
[filter_sizes[0], 2, 1, num_filters[0]], stddev=0.1),
name="W1")
b1 = tf.Variable(tf.constant(0.1, shape=[num_filters[0]]),
name="b1")
conv1 = self.dialate_conv_layer(self.embedded_chars_expanded,
W1,
b1,
rate=2,
scope="dialate_conv_1")
conv_bn1 = tf.layers.batch_normalization(
conv1, training=self.is_training)
pooled1 = self.folding_k_max_pooling(conv_bn1, k1)
#second layer
W2 = tf.Variable(tf.truncated_normal(
[filter_sizes[1], 3, num_filters[0], num_filters[1]],
stddev=0.1),
name="W2")
b2 = tf.Variable(tf.constant(0.1, shape=[num_filters[1]]),
name="b2")
conv2 = self.dialate_conv_layer(pooled1,
W2,
b2,
rate=2,
scope="dialate_conv_2")
conv_bn2 = tf.layers.batch_normalization(
conv2, training=self.is_training)
pooled2 = self.folding_k_max_pooling(conv_bn2, top_k)
else:
W1 = tf.Variable(tf.truncated_normal(
[filter_sizes[0], embedding_size, 1, num_filters[0]],
stddev=0.1),
name="W1")
b1 = tf.Variable(tf.constant(
0.1, shape=[num_filters[0], embedding_size]),
name="b1")
conv1 = self.conv1d_layer(self.embedded_chars_expanded,
W1,
b1,
scope="conv1d_1")
conv_bn1 = tf.layers.batch_normalization(
conv1, training=self.is_training)
pooled1 = self.folding_k_max_pooling(conv_bn1, k1)
W2 = tf.Variable(tf.truncated_normal([
filter_sizes[1], embedding_size, num_filters[0],
num_filters[1]
],
stddev=0.1),
name="W2")
b2 = tf.Variable(tf.constant(
0.1, shape=[num_filters[1], embedding_size]),
name="b2")
conv2 = self.conv1d_layer(pooled1, W2, b2, scope="conv1d_2")
conv_bn2 = tf.layers.batch_normalization(
conv2, training=self.is_training)
pooled2 = self.folding_k_max_pooling(conv_bn2, top_k)
# Combine all the pooled features
num_filters_total = int(pooled2.get_shape()[1] *
pooled2.get_shape()[2] *
pooled2.get_shape()[3])
self.h_pool_flat = tf.reshape(pooled2, [-1, num_filters_total])
# Fully Connected Layer
with tf.name_scope("fc"):
W_fc = tf.Variable(tf.truncated_normal(shape=[num_filters_total, self.fc_hidden_size],\
stddev=0.1, dtype=tf.float32), name="W_fc")
self.fc = tf.matmul(self.h_pool_flat, W_fc)
self.fc_bn = tf.layers.batch_normalization(
self.fc, training=self.is_training)
self.fc_out = tf.nn.relu(self.fc_bn, name="relu")
# Highway Layer
self.highway = highway(self.fc_out,
self.fc_out.get_shape()[1],
num_layers=1,
bias=-0.5,
scope="Highway")
# Add dropout
with tf.name_scope("dropout"):
self.h_drop = tf.nn.dropout(self.highway, self.dropout_keep_prob)
# Final (unnormalized) scores and predictions
with tf.name_scope("output"):
W_out = tf.Variable(tf.truncated_normal(shape=[self.fc_hidden_size, num_classes],\
stddev=0.1, dtype=tf.float32), name="W_out")
b_out = tf.Variable(tf.constant(0.1, shape=[num_classes]),
name="b_out")
l2_loss += tf.nn.l2_loss(W_out)
l2_loss += tf.nn.l2_loss(b_out)
self.scores = tf.nn.xw_plus_b(self.h_drop,
W_out,
b_out,
name="scores")
self.predictions = tf.argmax(self.scores, 1, name="predictions")
# Calculate mean cross-entropy loss
with tf.name_scope("loss"):
losses = tf.nn.softmax_cross_entropy_with_logits(
logits=self.scores, labels=self.input_y)
self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss
# Accuracy
with tf.name_scope("accuracy"):
correct_predictions = tf.equal(self.predictions,
tf.argmax(self.input_y, 1))
self.accuracy = tf.reduce_mean(tf.cast(correct_predictions,
tf.float32),
name="accuracy")
self.correct_pred_num = tf.reduce_sum(tf.cast(
correct_predictions, tf.int32),
name="correct_num")