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
# 'core_type_ph': [1, 1, 0,..],
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_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
