deep learning - How to minimize the Absolute Difference loss in tensorflow? -


i have tried reproduce flownet 1.0 in tensorflow days.the reconstruction not hard， absolute difference loss displayed on tensorboard seems fell loop. enter image description here

the main code may wanna know shown below.

#iamges1_shape = iamges2_shape = [batch, 461, 589, 6] def inference(iamges1, images2, flownet_groud_truth):     tf.device('/gpu:0'):         iamges1 = iamges1 * 0.00392156862745         images2 = images2 * 0.00392156862745         inputs = tf.concat([iamges1, images2], axis = 3)         conv1 = tf.contrib.layers.conv2d(inputs, 64, 5, [2, 2] )         conv2 = tf.contrib.layers.conv2d(conv1, 128, 5, stride=[ 2, 2] )          blablabla....          flowloss = tf.losses.absolute_difference(regroud_truth, predict_flow )             final_flow = 20*final_flow           return final_flow, flowloss  lr = tf.train.exponential_decay(0.0001,                                 global_step,                                 10000,                                 0.99,                                 staircase=true) opt = tf.train.adamoptimizer(learning_rate=lr) train_op = opt.minimize(loss, global_step=global_step) sess = tf.session(config=tf.configproto(         allow_soft_placement=true,         log_device_placement=false)) sumwriter = tf.summary.filewriter('/tmp/flow', graph=sess.graph)  threads = tf.train.start_queue_runners(sess ) sess.run(tf.global_variables_initializer())  step in range(100000):     gdt = flowio.next_groudtruthflow_batch(gd_truth_name_list, 4)     _ , total_loss, summary = sess.run([train_op, loss, merged ], feed_dict={gd_truth:gdt })     print('---------', 'step %d' % step)     print(' loss %f ' % total_loss )     if step % 200 == 0:         sumwriter.add_summary(summary, step)

i tried other learning_rate,like 0.1,0.001,etc,even optimizer.however,the loop still here，just different shape.

considering many urgly code may hurt mood，i not post of it.if more information help,you it.

any suggestion appreciate. lot!


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