Python tensorflow.contrib.slim.create_global_step() Examples

The following are 2 code examples of tensorflow.contrib.slim.create_global_step(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.contrib.slim , or try the search function .
Example #1
Source File: slim_train_test.py    From SSD_tensorflow_VOC with Apache License 2.0 4 votes vote down vote up 
def __setup_training(self,images, labels):
        tf.logging.set_verbosity(tf.logging.INFO)
        logits, end_points = self.network_fn(images)

        #############################
        # Specify the loss function #
        #############################
        loss_1 = None
        if 'AuxLogits' in end_points:
            loss_1 = tf.losses.softmax_cross_entropy(
                    logits=end_points['AuxLogits'], onehot_labels=labels,
                    label_smoothing=self.label_smoothing, weights=0.4, scope='aux_loss')
        total_loss = tf.losses.softmax_cross_entropy(
                logits=logits, onehot_labels=labels,
                label_smoothing=self.label_smoothing, weights=1.0)
        
        if loss_1 is not None:
            total_loss = total_loss + loss_1 
        
        
        
        global_step = slim.create_global_step()
        
        # Variables to train.
        variables_to_train = self.__get_variables_to_train()
        
        learning_rate = self.__configure_learning_rate(self.dataset.num_samples, global_step)
        optimizer = self.__configure_optimizer(learning_rate)
        
        
        train_op = slim.learning.create_train_op(total_loss, optimizer, variables_to_train=variables_to_train)
        
        self.__add_summaries(end_points, learning_rate, total_loss)
        
        ###########################
        # Kicks off the training. #
        ###########################
       
        slim.learning.train(
                train_op,
                logdir=self.train_dir,
                init_fn=self.__get_init_fn(),
                number_of_steps=self.max_number_of_steps,
                log_every_n_steps=self.log_every_n_steps,
                save_summaries_secs=self.save_summaries_secs,
                save_interval_secs=self.save_interval_secs)
        
        
        return
Example #2
Source File: train_model.py    From SSD_tensorflow_VOC with Apache License 2.0 4 votes vote down vote up 
def __start_training(self):
        tf.logging.set_verbosity(tf.logging.INFO)
        
        #get batched training training data 
        image, filename,glabels,gbboxes,gdifficults,gclasses, localizations, gscores = self.get_voc_2007_2012_train_data()
        
        #get model outputs
        predictions, localisations, logits, end_points = g_ssd_model.get_model(image, weight_decay=self.weight_decay, is_training=True)
        
        #get model training losss
        total_loss = g_ssd_model.get_losses(logits, localisations, gclasses, localizations, gscores)

        
        
        global_step = slim.create_global_step()
        
        # Variables to train.
        variables_to_train = self.__get_variables_to_train()
        
        learning_rate = self.__configure_learning_rate(self.dataset.num_samples, global_step)
        optimizer = self.__configure_optimizer(learning_rate)
        
        
        train_op = slim.learning.create_train_op(total_loss, optimizer, variables_to_train=variables_to_train)
        
        self.__add_summaries(end_points, learning_rate, total_loss)
        
        self.setup_debugging(predictions, localizations, glabels, gbboxes, gdifficults)
        
        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.9)
        config = tf.ConfigProto(log_device_placement=False,
                                gpu_options=gpu_options)
        
        ###########################
        # Kicks off the training. #
        ###########################
       
        slim.learning.train(
                train_op,
                self.train_dir,
                train_step_fn=self.train_step,
                saver=tf_saver.Saver(max_to_keep=500),
                init_fn=self.__get_init_fn(),
                number_of_steps=self.max_number_of_steps,
                log_every_n_steps=self.log_every_n_steps,
                save_summaries_secs=self.save_summaries_secs,
#                 session_config=config,
                save_interval_secs=self.save_interval_secs)
        
        
        return