def attention(scale_input, is_training=False):
l2_reg = FLAGS.learning_rate
dropout_ratio = 0
if is_training is True:
dropout_ratio = 0.5
conv1 = Utils.conv(scale_input, filters=512, l2_reg_scale=l2_reg)
conv1 = Utils.dropout(conv1, dropout_ratio, is_training)
conv2 = Utils.conv(conv1, filters=3, kernel_size=[
1, 1], l2_reg_scale=l2_reg)
return conv2
def attention(scale_input, is_training=False):
l2_reg = FLAGS.learning_rate
keep_prob = 1.0
if is_training is True:
keep_prob = 0.5
with tf.variable_scope("attention"):
conv1 = Utils.conv(scale_input, filters=512, l2_reg_scale=l2_reg)
conv1 = Utils.dropout(conv1, keep_prob)
conv2 = Utils.conv(conv1,
filters=3,
kernel_size=[1, 1],
l2_reg_scale=l2_reg)
return conv2
