Step is always 0 when using tf.estimator.Estimator - tensorflow

Question

I have been trying to learn the layers and estimators framework that was recently moved from contrib to main API. I ran into a rather strange problem. I wrote a simple autoencoder for MNIST, but somehow, when I train it keeps saying I am at step 0 even though the loss value is decreasing, so I guess the model is getting trained. Of course, since it is not counting steps, it is not saving the checkpoints and it is not saving any summaries either. Not sure what I am doing wrong and all the docs point to the old "tf.contrib.learn" framework and a lot of APIs there seem to be marked as deprecated. How do I make this work? Here is my code:
def encoder(x):
l1 = tf.layers.dense(x, 256, activation=tf.nn.relu, name='encode1')
l2 = tf.layers.dense(l1, 128, activation=tf.nn.relu, name='encode2')
return l2
def decoder(x):
l1 = tf.layers.dense(x, 256, activation=tf.nn.relu, name='decode1')
l2 = tf.layers.dense(l1, 784, activation=tf.nn.relu, name='decode2')
return l2
def loss(labels, preds):
return tf.losses.huber_loss(labels, preds)
def train(loss):
optimizer = tf.train.AdamOptimizer()
return optimizer.minimize(loss)
def model_fn(features, labels, mode):
_encoder = encoder(features)
_decoder = decoder(_encoder)
_loss = loss(labels, _decoder)
_train = train(_loss)
return tf.estimator.EstimatorSpec(mode=mode,
predictions=_decoder,
loss=_loss,
train_op=_train)
data = input_data.read_data_sets(".", one_hot=True)
display.clear_output()
# remove current log dir
shutil.rmtree('logs', ignore_errors=True)
def input_fn():
if data.train.epochs_completed <= 10:
features, labels = data.train.next_batch(100)
return tf.constant(features), tf.constant(features)
raise StopIteration
estimator = tf.estimator.Estimator(model_fn, "logs")
estimator.train(input_fn=input_fn)
And here is some sample output
INFO:tensorflow:Using default config.
INFO:tensorflow:Using config: {'_model_dir': 'logs', '_tf_random_seed': 1, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000}
INFO:tensorflow:Create CheckpointSaverHook.
INFO:tensorflow:Saving checkpoints for 0 into logs/model.ckpt.
INFO:tensorflow:loss = 0.0505481, step = 0
INFO:tensorflow:loss = 0.00319921, step = 0 (1.125 sec)
INFO:tensorflow:loss = 0.00277268, step = 0 (1.094 sec)
INFO:tensorflow:loss = 0.00275822, step = 0 (1.106 sec)
INFO:tensorflow:loss = 0.00275116, step = 0 (1.069 sec)
INFO:tensorflow:loss = 0.00275018, step = 0 (1.130 sec)
INFO:tensorflow:loss = 0.00274921, step = 0 (1.161 sec)
INFO:tensorflow:loss = 0.00274908, step = 0 (1.140 sec)
INFO:tensorflow:loss = 0.00274683, step = 0 (1.105 sec)
INFO:tensorflow:loss = 0.00274397, step = 0 (1.111 sec)

Answer 1

In the training op you need to set the global_step parameter, which is the step counter that gets incremented for each model training run. So change to :
optimizer.minimize(loss, global_step=tf.train.get_global_step())