tensorflow-estimator

Notes: this question extends upon a previous question of mine . In that question I ask about the best way to store some dummy data as Example and SequenceExample seeking to know which is better for data similar to dummy data provided. I provide both explicit formulations of the Example and SequenceExample construction as well as, in the answers, a programatic way to do so. Because this is still a lot of code, I am providing a Colab (interactive jupyter notebook hosted by google) file where you can try the code out yourself to assist. All the necessary code is there and it is generously

I am trying to convert Tensorflow's official basic word2vec implementation to use tf.Estimator. The issue is that the loss function( sampled_softmax_loss or nce_loss ) gives an error when using Tensorflow Estimators. It works perfectly fine in the original implementation. Here's is Tensorflow's official basic word2vec implementation: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py Here is the Google Colab notebook where I implemented this code, which is working. https://colab.research.google.com/drive/1nTX77dRBHmXx6PEF5pmYpkIVxj

The google cloud documentation (see Binary data in prediction input) states: Your encoded string must be formatted as a JSON object with a single key named b64. The following Python example encodes a buffer of raw JPEG data using the base64 library to make an instance: {"image_bytes":{"b64": base64.b64encode(jpeg_data)}} In your TensorFlow model code, you must name the aliases for your input and output tensors so that they end with '_bytes'. I would like to understand more about how this process works on the google cloud side. Is the ml-engine automatically decoding any content after the "b64"

Suppose you are training a custom tf.estimator.Estimator with tf.estimator.train_and_evaluate using a validation dataset in a setup similar to that of @simlmx's : classifier = tf.estimator.Estimator( model_fn=model_fn, model_dir=model_dir, params=params) train_spec = tf.estimator.TrainSpec( input_fn = training_data_input_fn, ) eval_spec = tf.estimator.EvalSpec( input_fn = validation_data_input_fn, ) tf.estimator.train_and_evaluate( classifier, train_spec, eval_spec ) Often, one uses a validation dataset to cut off training to prevent over-fitting when the loss continues to improve for the