def _build_model(self):
# Model PlaceHolder for input
# [batch, sent]
self.in_words = tf.placeholder(tf.int32, [None, self.max_len_sent])
self.in_y = tf.placeholder(tf.int32, [None])
self.in_len = tf.placeholder(tf.int32, [None])
seq_words = tf.nn.embedding_lookup(self.we, self.in_words) # [B, T, D]
with tf.name_scope('rcnn'):
rcnn_out = tf_encoder.rcnn(seq_words, self.in_lens_word,
self.n_rnn)
dim_sent = 2 * self.n_rnn + self.dim_word
with tf.name_scope('mlp'):
# rcnn_out = tf.nn.dropout(rcnn_out, keep_prob=self.keep_prob)
mlp = tf_ops.dense_layer(rcnn_out,
dim_sent,
self.n_mlp,
activation=tf.nn.relu)
with tf.name_scope('logits'):
self.logits = tf_ops.dense_layer(mlp,
self.n_mlp,
self.n_class,
scope='logits')
def _build_model(self):
self.in_chars = tf.placeholder(tf.int32, [None, self.max_len])
self.in_y = tf.placeholder(tf.int32, [None])
seq_chars = tf.nn.embedding_lookup(self.ce, self.in_chars)
x = tf.expand_dims(seq_chars, -1) # [B, T, D, 1]
with tf.name_scope("conv1-max-pooling"):
# [filter_height, filter_width, in_channels, out_channels]
filter_shape = [7, self.dim_char, 1, 256]
w = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.05),
name="w")
b = tf.Variable(tf.constant(0.1, shape=[256]), name="b")
conv = tf.nn.conv2d(x,
w,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv1")
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
x = tf.nn.max_pool(h,
ksize=[1, 3, 1, 1],
strides=[1, 3, 1, 1],
padding="VALID",
name="pool1")
with tf.name_scope("conv2-max-pooling"):
filter_shape = [3, 1, 256, 256]
w = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.05),
name="W")
b = tf.Variable(tf.constant(0.1, shape=[256]), name="b")
conv = tf.nn.conv2d(x,
w,
strides=[1, 1, 1, 1],
padding="VALID",
name="conv3")
h = tf.nn.relu(tf.nn.bias_add(conv, b), name="relu")
x = tf.nn.max_pool(h,
ksize=[1, 3, 1, 1],
strides=[1, 3, 1, 1],
padding="VALID",
name="pool2")
num_features_total = 12 * 256
x = tf.reshape(x, [-1, num_features_total])
with tf.name_scope("dropout-1"):
x = tf.nn.dropout(x, self.keep_prob)
x = tf_ops.dense_layer(x,
num_features_total,
1024,
activation=tf.nn.relu,
scope="fc1")
with tf.name_scope("dropout-1"):
x = tf.nn.dropout(x, self.keep_prob)
x = tf_ops.dense_layer(x,
1024,
256,
activation=tf.nn.relu,
scope="fc2")
self.logits = tf_ops.dense_layer(x, 256, self.n_class, scope="logits")
def _build_model(self):
self.in_x = tf.placeholder(tf.int32, [None, self.max_len_sent])
self.in_y = tf.placeholder(tf.int32, [None])
self.in_len = tf.placeholder(tf.int32, [None])
seq_words = tf.nn.embedding_lookup(self.we, self.in_x) # [B, T, D]
with tf.name_scope("rnn"):
rnn_out = tf_encoder.bi_rnn_std(seq_words, self.in_len, self.n_rnn, return_hs=True)
with tf.name_scope("att"):
sent = tf_ops.self_attention(rnn_out, self.in_len, att_size=self.att_size)
sent_dim = 2*self.n_rnn
with tf.name_scope("mlp"):
mlp = tf_ops.dense_layer(sent, sent_dim, self.n_mlp, activation=tf.nn.relu)
with tf.name_scope("logits"):
self.logits = tf_ops.dense_layer(mlp, self.n_mlp, self.n_class, scope='logits')
def _build_model(self):
self.in_words = tf.placeholder(tf.int32, [None, self.max_len_sent])
self.in_chars = tf.placeholder(tf.int32, [None, self.max_len_sent, self.max_len_word])
self.in_y = tf.placeholder(tf.int32, [None])
self.in_lens_sent = tf.placeholder(tf.int32, [None])
self.in_lens_word = tf.placeholder(
tf.int32, [None, self.max_len_sent], name="in_lens_word")
seqs_words_embed = tf.nn.embedding_lookup(self.we, self.in_words)
# seqs_words_embed = tf.nn.dropout(seqs_words_embed, self.keep_prob)
c_gate_w = tf_encoder.char2word_gate_word(
self.in_chars, self.in_lens_word, self.ce, self.n_rnn_char, seqs_words_embed)
with tf.name_scope("rnn"):
rnn_out = tf_encoder.bi_rnn_std(c_gate_w, self.in_lens_sent, self.n_rnn_word, return_hs=True)
with tf.name_scope("att"):
sent = tf_ops.self_attention(rnn_out, self.in_lens_sent, att_size=self.n_rnn_word)
sent_dim = 2*self.n_rnn_word
# sent = tf_encoder.word2sent(c_gate_w, self.in_lens_sent, self.n_rnn_word)
# dim_sent = 2*self.n_rnn_word
with tf.name_scope("mlp"):
# sent = tf.nn.dropout(sent, self.keep_prob)
mlp = tf_ops.dense_layer(
sent, sent_dim, self.n_mlp, activation=tf.nn.relu, scope="mlp")
with tf.name_scope("logits"):
# self.logits = tf_ops.dense_layer(mlp, self.n_mlp, self.n_class, scope="logits")
self.logits = tf_ops.centroids_logits(mlp, self.emoji_w)
def _build_model(self):
# Model PlaceHolder for input
self.in_words = tf.placeholder(tf.int32, [None, self.max_len_sent])
self.in_y = tf.placeholder(tf.int32, [None])
self.in_lens_word = tf.placeholder(tf.int32, [None])
embedded_seq = tf.nn.embedding_lookup(self.we, self.in_words)
with tf.name_scope('nbow'):
avg_out = tf_ops.avg_pool(embedded_seq, self.in_lens_word)
with tf.name_scope("mlp"):
mlp = tf_ops.dense_layer(avg_out,
self.dim_word,
self.n_mlp,
activation=tf.nn.relu,
scope="mlp")
with tf.name_scope('logits'):
self.logits = tf_ops.dense_layer(mlp,
self.n_mlp,
self.n_class,
scope="logits")
def _build_model(self):
# Model PlaceHolder for input
# [batch, sent]
self.in_words = tf.placeholder(tf.int32, [None, self.max_len_sent])
self.in_lens_sent = tf.placeholder(tf.int32, [None])
self.in_y = tf.placeholder(tf.int32, [None])
# Embedding layer
# [batch, sent, dim_word]
embedded_seq = tf.nn.embedding_lookup(self.we, self.in_words)
# with tf.name_scope("lstm"):
# rnn_h_out = tf_encoder.bi_rnn_std(
# embedded_seq, self.in_lens_word, self.n_rnn, return_hs=True)
# rnn_out = tf_ops.max2d_pooling(rnn_h_out, self.max_len_sent)
#
# with tf.name_scope("mlp"):
# mlp = tf_ops.dense_layer(
# rnn_out, 2*self.n_rnn, self.n_mlp, activation=tf.nn.relu, scope="mlp")
with tf.name_scope("lstm"):
rnn_out = tf_encoder.bi_rnn_std(embedded_seq, self.in_lens_word,
self.n_rnn)
with tf.name_scope("mlp"):
mlp = tf_ops.dense_layer(rnn_out,
2 * self.n_rnn,
self.n_mlp,
activation=tf.nn.relu,
scope="mlp")
with tf.name_scope("logits"):
self.logits = tf_ops.dense_layer(mlp,
self.n_mlp,
self.n_class,
scope="logits")
self.logits = tf.layers.dense(mlp, self.n_class)