def get_mask_zero_embedded(var_em, var_ph):
mask = tf.equal(var_ph, 0)
mask2 = tf.concat(
[tf.expand_dims(~mask, -1) for i in range(EMBEDDING_DIM)], -1)
rst = tf.where(
mask2, tf.nn.embedding_lookup(var_em, var_ph),
tf.zeros([tf.shape(var_ph)[0],
tf.shape(var_ph)[1], EMBEDDING_DIM]))
return rst
def build_fcn_net(self, inp, use_dice=False):
with self.graph.as_default():
self.saver = tf.train.Saver(max_to_keep=1)
with tf.name_scope("Out"):
bn1 = tf.layers.batch_normalization(inputs=inp, name='bn1')
dnn1 = tf.layers.dense(bn1, 200, activation=None, name='f1')
if use_dice:
dnn1 = dice(dnn1, name='dice_1')
else:
dnn1 = prelu(dnn1, 'prelu1')
dnn2 = tf.layers.dense(dnn1, 80, activation=None, name='f2')
if use_dice:
dnn2 = dice(dnn2, name='dice_2')
else:
dnn2 = prelu(dnn2, 'prelu2')
dnn3 = tf.layers.dense(dnn2, 2, activation=None, name='f3')
self.y_hat = tf.nn.softmax(dnn3) + 0.00000001
with tf.name_scope('Metrics'):
# Cross-entropy loss and optimizer initialization
coe = tf.constant([1.2, 1.2])
coe_mask = tf.equal(self.core_type_ph, 1)
coe_mask2 = tf.concat(
[tf.expand_dims(coe_mask, -1) for i in range(2)], -1)
self.target_ph_coe = tf.where(coe_mask2, self.target_ph * coe,
self.target_ph)
ctr_loss = -tf.reduce_mean(tf.log(self.y_hat) * self.target_ph)
self.loss = ctr_loss
# tf.summary.scalar('loss', self.loss)
self.optimizer = tf.train.AdamOptimizer(
learning_rate=self.lr_ph).minimize(self.loss)
# self.optimizer = tf.train.GradientDescentOptimizer(learning_rate=self.lr_ph).minimize(self.loss)
# Accuracy metric
self.accuracy = tf.reduce_mean(
tf.cast(tf.equal(tf.round(self.y_hat), self.target_ph),
tf.float32))
# tf.summary.scalar('accuracy', self.accuracy)
self.merged = tf.summary.merge_all()
def get_self_or_expand_dims(aim):
return tf.cast(tf.expand_dims(aim, -1), tf.float32)
def get_history_din_embedded(self):
# TODO: add mask info for this operation
his_days = [
'one', 'two', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine',
'ten', 'eleven', 'twelve', 'thirteen', 'fourteen'
]
for fir in his_days:
key = "history_" + fir + "_chap_ph"
embed_key = "history_" + fir + "_chap_embedded"
setattr(
self, embed_key,
get_mask_zero_embedded(self.chapters_embeddings_var,
getattr(self, key)))
for fir in his_days:
key = "history_" + fir + "_sec_ph"
embed_key = "history_" + fir + "_sec_embedded"
setattr(
self, embed_key,
get_mask_zero_embedded(self.sections_embeddings_var,
getattr(self, key)))
history_cha_embedded = []
history_sec_embedded = []
for fir in his_days[::-1]:
key_c = "history_" + fir + "_chap_embedded"
key_s = "history_" + fir + "_sec_embedded"
history_cha_embedded.append(get_rnn_sum(getattr(self, key_c),
"rnncha"))
history_sec_embedded.append(get_rnn_sum(getattr(self, key_s),
"rnnsec"))
#self.history_all_embedded = tf.reshape(, [None,len(history_all_embedded),EMBEDDING_DIM])
# T*B*N -。 B*T*N
history_cha_emb = tf.transpose(history_cha_embedded, [1, 0, 2])
history_sec_emb = tf.transpose(history_sec_embedded, [1, 0, 2])
#dien
with tf.name_scope('rnn_1'):
rnn_outputs, _ = dynamic_rnn(GRUCell(HIDDEN_SIZE * 2),
inputs=tf.concat(
[history_cha_emb, history_sec_emb],
axis=-1),
sequence_length=self.history_len_ph,
dtype=tf.float32,
scope="gru1")
with tf.name_scope('Attention_layer_1'):
att_outputs, alphas = din_fcn_attention(tf.concat([
get_rnn_sum(self.today_chapters_embedded, "rnncha"),
get_rnn_sum(self.today_sections_embedded, "rnnsec")
],
axis=-1),
rnn_outputs,
ATTENTION_SIZE,
self.history_mask_cha_ph,
scope="1_1",
softmax_stag=1,
stag='1_1',
mode='LIST',
return_alphas=True)
with tf.name_scope('rnn_2'):
rnn_outputs2, final_state2 = dynamic_rnn(
VecAttGRUCell(HIDDEN_SIZE * 2),
inputs=rnn_outputs,
att_scores=tf.expand_dims(alphas, -1),
sequence_length=self.history_len_ph,
dtype=tf.float32,
scope="gru2")
return final_state2
