train_experiment function in TensorFlow

gistfile1.txt

def train_experiment(training_steps, clean_start, incremental, run_config):

    if clean_start == True: 
        if tf.gfile.Exists(run_config.model_dir):
            print("Removing previous artefacts...")
            
            tf.gfile.DeleteRecursively(run_config.model_dir)

    print ""
    estimator = create_estimator(run_config)
    print ""
    
    time_start = datetime.utcnow() 
    print("Experiment started at {}".format(time_start.strftime("%H:%M:%S")))
    print(".......................................") 
   
    if incremental:
        # Use steps parameter
        estimator.train(train_input_fn, steps=training_steps)
    else:
        # Use max_steps parameter
        estimator.train(train_input_fn, max_steps=training_steps)
        
    time_end = datetime.utcnow() 
    print(".......................................")
    print("Experiment finished at {}".format(time_end.strftime("%H:%M:%S")))
    print("")
    time_elapsed = time_end - time_start
    print("Experiment elapsed time: {} seconds".format(time_elapsed.total_seconds()))
    
    return estimator
 
Then we defined several different calls to the train_experiment function to highlight the impact of the  steps or max_steps values.  The first function call trains to 1000 steps using the max_steps parameter.

train_experiment(
    training_steps=1000, 
    clean_start=True,
    incremental=False,
    run_config=run_config
)

Experiment started at 16:52:35
.......................................
INFO:tensorflow:Calling model_fn.
INFO:tensorflow:Done calling model_fn.
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Graph was finalized.
INFO:tensorflow:Running local_init_op.
INFO:tensorflow:Done running local_init_op.
INFO:tensorflow:Saving checkpoints for 0 into models/census/dnn_classifier/model.ckpt.
INFO:tensorflow:loss = 31132416.0, step = 1
INFO:tensorflow:global_step/sec: 82.2509
INFO:tensorflow:loss = 12670475.0, step = 101 (1.218 sec)
INFO:tensorflow:global_step/sec: 169.578
INFO:tensorflow:loss = 11341477.0, step = 201 (0.590 sec)
INFO:tensorflow:global_step/sec: 177.614
INFO:tensorflow:loss = 12852321.0, step = 301 (0.563 sec)
INFO:tensorflow:global_step/sec: 163.928
INFO:tensorflow:loss = 13684520.0, step = 401 (0.610 sec)
INFO:tensorflow:global_step/sec: 169.234
INFO:tensorflow:loss = 12090486.0, step = 501 (0.591 sec)
INFO:tensorflow:global_step/sec: 187.021
INFO:tensorflow:loss = 13600504.0, step = 601 (0.534 sec)
INFO:tensorflow:global_step/sec: 167.494
INFO:tensorflow:loss = 14767286.0, step = 701 (0.597 sec)
INFO:tensorflow:global_step/sec: 145.886
INFO:tensorflow:loss = 10702760.0, step = 801 (0.685 sec)
INFO:tensorflow:global_step/sec: 153.083
INFO:tensorflow:loss = 13668747.0, step = 901 (0.654 sec)
INFO:tensorflow:Saving checkpoints for 1000 into models/census/dnn_classifier/model.ckpt.
INFO:tensorflow:Loss for final step: 14524872.0.
.......................................
Experiment finished at 16:52:47

Experiment elapsed time: 12.109969 seconds