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I have a project using a canned estimator in Tensorflow and trying to produce train_and_evaluate method.

estimator = tf.estimator.DNNClassifier(
 feature_columns=my_feature_columns,
 hidden_units=hidden_units,
 model_dir=model_dir,
 optimizer=tf.train.ProximalAdagradOptimizer(learning_rate=0.01,l1_regularization_strength=0.001))

Every time that I see the console output it shows that the loss is always zero.

INFO:tensorflow:loss = 2.896826e-06, step = 875
INFO:tensorflow:global_step/sec: 5.96785
INFO:tensorflow:loss = 1.9453131e-05, step = 975 (16.756 sec)
INFO:tensorflow:global_step/sec: 7.2834
INFO:tensorflow:loss = 8.6957414e-05, step = 1075 (13.730 sec)
INFO:tensorflow:global_step/sec: 7.36042
INFO:tensorflow:loss = 0.0004585028, step = 1175 (13.586 sec)
INFO:tensorflow:global_step/sec: 7.38419
INFO:tensorflow:loss = 0.0012249642, step = 1275 (13.542 sec)
INFO:tensorflow:global_step/sec: 7.3658
INFO:tensorflow:loss = 0.002194246, step = 1375 (13.576 sec)
INFO:tensorflow:global_step/sec: 7.33054
INFO:tensorflow:loss = 0.0031063582, step = 1475 (13.641 sec)

This happened since I change my input_fn (I used to load my CSV into a pandas Dataframe and work from there, however my total Dataset is over 10GB (800x1500000 in dimensions), and every time that I used to save the model, the model folder size used to be crazy large (over 200GB), so I decided to use iterators instead (I found this input function in a tutorial somewhere and it works well):

def input_fn_train(filenames,
 num_epochs=None,
 shuffle=True,
 skip_header_lines=0,
 batch_size=200,
 modeTrainEval=True):
 filename_dataset = tf.data.Dataset.from_tensor_slices(filenames)
 if shuffle:
 filename_dataset = filename_dataset.shuffle(len(filenames))
 dataset = filename_dataset.flat_map(lambda filename: tf.data.TextLineDataset(filename).skip(skip_header_lines))
 dataset = dataset.map(parse_csv)
 if shuffle:
 dataset = dataset.shuffle(buffer_size=batch_size * 10)
 dataset = dataset.repeat(num_epochs)
 dataset = dataset.batch(batch_size)
 iterator = dataset.make_one_shot_iterator()
 features = iterator.get_next()
 features, labels = features, features.pop(LABEL_COLUMN)
 if not modeTrainEval:
 return features, None
 return features, labels

Unfortunately, this change caused my loss being always be zero, and as a consequence the predictions are terribly bad (50% accuracy), and I can't find the reason why.

(github link with sample dataset and my code)