adjusted code

@@ -50,12 +50,12 @@ if __name__ == "__main__":
       "read 3D batches VS 4D batches.")
 
   # Other flags.
-  flags.DEFINE_integer("batch_size", 1024,
+  flags.DEFINE_integer("batch_size", 1,
                        "How many examples to process per batch.")
   flags.DEFINE_integer("num_readers", 8,
                        "How many threads to use for reading input files.")
   flags.DEFINE_boolean("run_once", False, "Whether to run eval only once.")
-  flags.DEFINE_integer("top_k", 20, "How many predictions to output per video.")
+  flags.DEFINE_integer("top_k", 5, "How many predictions to output per video.")
 
 
 def find_class_by_name(name, modules):
@@ -69,18 +69,15 @@ def get_input_evaluation_tensors(reader,
                                  batch_size=1024,
                                  num_readers=1):
   """Creates the section of the graph which reads the evaluation data.
-
   Args:
     reader: A class which parses the training data.
     data_pattern: A 'glob' style path to the data files.
     batch_size: How many examples to process at a time.
     num_readers: How many I/O threads to use.
-
   Returns:
     A tuple containing the features tensor, labels tensor, and optionally a
     tensor containing the number of frames per video. The exact dimensions
     depend on the reader being used.
-
   Raises:
     IOError: If no files matching the given pattern were found.
   """
@@ -110,7 +107,6 @@ def build_graph(reader,
                 batch_size=1024,
                 num_readers=1):
   """Creates the Tensorflow graph for evaluation.
-
   Args:
     reader: The data file reader. It should inherit from BaseReader.
     model: The core model (e.g. logistic or neural net). It should inherit from
@@ -169,14 +165,12 @@ def build_graph(reader,
 def evaluation_loop(fetches, saver, summary_writer, evl_metrics,
                     last_global_step_val):
   """Run the evaluation loop once.
-
   Args:
     fetches: a dict of tensors to be run within Session.
     saver: a tensorflow saver to restore the model.
     summary_writer: a tensorflow summary_writer
     evl_metrics: an EvaluationMetrics object.
     last_global_step_val: the global step used in the previous evaluation.
-
   Returns:
     The global_step used in the latest model.
   """
@@ -192,17 +186,20 @@ def evaluation_loop(fetches, saver, summary_writer, evl_metrics,
       # Assuming model_checkpoint_path looks something like:
       # /my-favorite-path/yt8m_train/model.ckpt-0, extract global_step from it.
       global_step_val = os.path.basename(latest_checkpoint).split("-")[-1]
-
+      print("COMES HERERERERERER 55555555555555555 : ")
       # Save model
       if FLAGS.segment_labels:
+        print("COMES HERERERERERER 666666666666666666 : 1111")
         inference_model_name = "segment_inference_model"
       else:
+        print("COMES HERERERERERER 666666666666666666 : 22222")
         inference_model_name = "inference_model"
       saver.save(
           sess,
           os.path.join(FLAGS.train_dir, "inference_model",
                        inference_model_name))
     else:
+      print("COMES HERERERERERER 666666666666666666 : 3333")
       logging.info("No checkpoint file found.")
       return global_step_val
 
@@ -213,6 +210,7 @@ def evaluation_loop(fetches, saver, summary_writer, evl_metrics,
       return global_step_val
 
     sess.run([tf.local_variables_initializer()])
+    print("COMES HERERERERERER 777777777777 : ")
 
     # Start the queue runners.
     coord = tf.train.Coordinator()
@@ -227,15 +225,25 @@ def evaluation_loop(fetches, saver, summary_writer, evl_metrics,
       evl_metrics.clear()
 
       examples_processed = 0
+      index = 0
       while not coord.should_stop():
+        index = index + 1
+        print("proceeeeeeeeeeDDDDD!!! : " + str(index))
         batch_start_time = time.time()
+        print("step  1")
         output_data_dict = sess.run(fetches)
+        print("step  2")
         seconds_per_batch = time.time() - batch_start_time
+        print("step  3")
         labels_val = output_data_dict["labels"]
+        print("step  4")
         summary_val = output_data_dict["summary"]
+        print("step  5")
         example_per_second = labels_val.shape[0] / seconds_per_batch
+        print("step  6")
         examples_processed += labels_val.shape[0]
-
+        print("step  7")
+      
         predictions = output_data_dict["predictions"]
         if FLAGS.segment_labels:
           # This is a workaround to ignore the unrated labels.
@@ -354,4 +362,4 @@ def main(unused_argv):
 
 
 if __name__ == "__main__":
-  tf.compat.v1.app.run()
+  tf.compat.v1.app.run()
\ No newline at end of file

@@ -37,7 +37,7 @@ FLAGS = flags.FLAGS
 
 if __name__ == "__main__":
   # Dataset flags.
-  flags.DEFINE_string("train_dir", "/tmp/yt8m_model/",
+  flags.DEFINE_string("train_dir", "F:/yt8mDataset/savedModel",
                       "The directory to save the model files in.")
   flags.DEFINE_string(
       "train_data_pattern", "",
@@ -75,7 +75,7 @@ if __name__ == "__main__":
   flags.DEFINE_integer(
       "num_gpu", 1, "The maximum number of GPU devices to use for training. "
       "Flag only applies if GPUs are installed")
-  flags.DEFINE_integer("batch_size", 1024,
+  flags.DEFINE_integer("batch_size", 256,
                        "How many examples to process per batch for training.")
   flags.DEFINE_string("label_loss", "CrossEntropyLoss",
                       "Which loss function to use for training the model.")
@@ -94,13 +94,13 @@ if __name__ == "__main__":
       "Multiply current learning rate by learning_rate_decay "
       "every learning_rate_decay_examples.")
   flags.DEFINE_integer(
-      "num_epochs", 1000, "How many passes to make over the dataset before "
+      "num_epochs", 100, "How many passes to make over the dataset before "
       "halting training.")
   flags.DEFINE_integer(
       "max_steps", None,
       "The maximum number of iterations of the training loop.")
   flags.DEFINE_integer(
-      "export_model_steps", 1000,
+      "export_model_steps", 1,
       "The period, in number of steps, with which the model "
       "is exported for batch prediction.")
 
@@ -404,6 +404,10 @@ class Trainer(object):
     Returns:
       A tuple of the training Hit@1 and the training PERR.
     """
+
+    print("=========================")
+    print("start now!!!!!")
+
     if self.is_master and start_new_model:
       self.remove_training_directory(self.train_dir)
 
@@ -461,6 +465,7 @@ class Trainer(object):
                              save_summaries_secs=120,
                              saver=saver)
 
+    
     logging.info("%s: Starting managed session.", task_as_string(self.task))
     with sv.managed_session(target, config=self.config) as sess:
       try:
@@ -470,8 +475,8 @@ class Trainer(object):
           _, global_step_val, loss_val, predictions_val, labels_val = sess.run(
               [train_op, global_step, loss, predictions, labels])
           seconds_per_batch = time.time() - batch_start_time
-          examples_per_second = labels_val.shape[0] / seconds_per_batch
-
+          examples_per_second = labels_val.shape[0] / seconds_per_batch          
+          print("CURRENT STEP IS " + str(global_step_val))
           if self.max_steps and self.max_steps <= global_step_val:
             self.max_steps_reached = True
 
@@ -640,7 +645,7 @@ class ParameterServer(object):
 
   def run(self):
     """Starts the parameter server."""
-
+    print("start now=================")
     logging.info("%s: Starting parameter server within cluster %s.",
                  task_as_string(self.task), self.cluster.as_dict())
     server = start_server(self.cluster, self.task)