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