Modyfying costs, feeding datasets to placeholders instead of constants

nn_ds_inmem_tmp.py

@@ -30,7 +30,7 @@ FILES_PER_SCENE =    5 # number of random offset files for the scene to select f
 #MIN_BATCH_CHOICES = 10 # minimal number of tiles in a file for each bin to select from 
 #MAX_BATCH_FILES =   10 #maximal number of files to use in a batch
 MAX_EPOCH =        500
-LR =               1e-3 # learning rate
+LR =               1e-4 # learning rate
 USE_CONFIDENCE =     False
 ABSOLUTE_DISPARITY = False
 DEBUG_PLT_LOSS =     True
@@ -167,16 +167,21 @@ checkpoint_dir = './attic/result_inmem/'
 save_freq = 500
 
 def lrelu(x):
-    return tf.maximum(x*0.2,x)
+    return tf.maximum(x*0.5,x)
 #    return tf.nn.relu(x)
 
 def network(input):
 
-#  fc1  = slim.fully_connected(input, 512, activation_fn=lrelu,scope='g_fc1')
-#  fc2  = slim.fully_connected(fc1,   512, activation_fn=lrelu,scope='g_fc2')
-  fc3  =     slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3')
-  fc4  =     slim.fully_connected(fc3,   128, activation_fn=lrelu,scope='g_fc4')
-  fc5  =     slim.fully_connected(fc4,    64, activation_fn=lrelu,scope='g_fc5')
+  fc1  = slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc1')
+  fc2  = slim.fully_connected(fc1,   128, activation_fn=lrelu,scope='g_fc2')
+##  fc3  =     slim.fully_connected(input, 256, activation_fn=lrelu,scope='g_fc3')
+##  fc4  =     slim.fully_connected(fc3,   128, activation_fn=lrelu,scope='g_fc4')
+##  fc5  =     slim.fully_connected(fc4,    64, activation_fn=lrelu,scope='g_fc5')
+  
+  fc3  =     slim.fully_connected(fc2,    64, activation_fn=lrelu,scope='g_fc3')
+  fc4  =     slim.fully_connected(fc3,    20, activation_fn=lrelu,scope='g_fc4')
+  fc5  =     slim.fully_connected(fc4,    16, activation_fn=lrelu,scope='g_fc5')
+  
   if USE_CONFIDENCE:
       fc6  = slim.fully_connected(fc5,     2, activation_fn=lrelu,scope='g_fc6')
   else:     
@@ -318,7 +323,7 @@ with tf.Session()  as sess:
     
     for epoch in range(EPOCHS_TO_RUN):
         
-        if SHUFFLE_EPOCH:
+ #       if SHUFFLE_EPOCH:
         dataset_train = dataset_train.shuffle(buffer_size=10000)
         sess.run(iterator_train.initializer)
         
@@ -331,6 +336,8 @@ with tf.Session()  as sess:
             # Train run
             if i<START_TEST:
                 
+                if (epoch <50) or (epoch > 100) :
+                    
                     try:
         #                _, G_current,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
                         train_summary,_, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out  = sess.run(
@@ -347,6 +354,33 @@ with tf.Session()  as sess:
                                 _cost1,
                                 corr2d325,
         #                     target_disparity,
+        #                     gt_ds
+                            ],
+                            feed_dict={lr:LR})
+                        
+                        # save all for now as a test
+                        #train_writer.add_summary(summary, i)
+                        #train_writer.add_summary(train_summary, i)
+                        
+                    except tf.errors.OutOfRangeError:
+                        break
+                else:                
+                    try:
+        #                _, G_current,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out, target_disparity_out, gt_ds_out = sess.run(
+                        train_summary, G_loss_trained,  output, disp_slice, d_gt_slice, out_diff, out_diff2, w_norm, out_wdiff2, out_cost1, corr2d325_out  = sess.run(
+                            [   merged,
+#                                G_opt,
+                                G_loss,
+                                out,
+                                _disp_slice,
+                                _d_gt_slice,
+                                _out_diff,
+                                _out_diff2,
+                                _w_norm,
+                                _out_wdiff2,
+                                _cost1,
+                                corr2d325,
+        #                     target_disparity,
         #                     gt_ds
                             ],
                             feed_dict={lr:LR})