How to save estimator in Tensorflow for later use?

Question

I followed the tutorial \"A Guide to TF Layers: Building a Convolutional Neural Network\" (here is the code: https://github.com/tensorflow/tensorflow/blob/r1.1/tensorflow/exampl

Answer 1

Update to David Valenzuela Urrutia's answer(codes)

David Valenzuela Urrutia's answer was for Python 3.6.3, Tensorflow 1.4.0 so i thought of updating the answer(code samples) to Tensorflow 2.x because some funtionalities like tf.Session is not supported in Tensorflow version 2 so you need to replace it with tf.compat.v1.Session for it to work. Visit this link to know more about the changes added to tensorflow version 2

Training script updated code

def serving_input_receiver_fn():
   serialized_tf_example = tf.compat.v1.placeholder(dtype=tf.string, shape=[None], 
       name='input_tensors')
   receiver_tensors      = {"predictor_inputs": serialized_tf_example}
   feature_spec          = {"words": tf.io.FixedLenFeature([25],tf.int64)}
   features              = tf.io.parse_example(serialized=serialized_tf_example, 
       features=feature_spec)
   return tf.estimator.export.ServingInputReceiver(features, receiver_tensors)

def estimator_spec_for_softmax_classification(logits, labels, mode):
   predicted_classes = tf.argmax(input=logits, axis=1)
   if (mode == tf.estimator.ModeKeys.PREDICT):
      export_outputs = {'predict_output': 
   tf.estimator.export.PredictOutput({"pred_output_classes": predicted_classes, 'probabilities': tf.nn.softmax(logits)})}
   return tf.estimator.EstimatorSpec(mode=mode, predictions={'class': predicted_classes, 'prob': tf.nn.softmax(logits)}, export_outputs=export_outputs) # IMPORTANT!!!
   onehot_labels = tf.one_hot(labels, 31, 1, 0)
   loss        =tf.compat.v1.losses.softmax_cross_entropy(onehot_labels=onehot_labels, logits=logits)
   if (mode == tf.estimator.ModeKeys.TRAIN):
       optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.01)
       train_op  = optimizer.minimize(loss, global_step=tf.compat.v1.train.get_global_step())
       return tf.estimator.EstimatorSpec(mode, loss=loss, train_op=train_op)
   eval_metric_ops = {'accuracy': tf.compat.v1.metrics.accuracy(labels=labels, predictions=predicted_classes)}
   return tf.estimator.EstimatorSpec(mode=mode, loss=loss, eval_metric_ops=eval_metric_ops)

def model_custom(features, labels, mode):
   bow_column           = tf.feature_column.categorical_column_with_identity("words", num_buckets=1000)
   bow_embedding_column = tf.feature_column.embedding_column(bow_column, dimension=50)   
   bow                  = tf.compat.v1.feature_column.input_layer(features, feature_columns=[bow_embedding_column])
   logits               = tf.compat.v1.layers.dense(bow, 31, activation=None)
   return estimator_spec_for_softmax_classification(logits=logits, labels=labels, mode=mode)

def main():
   # ...
   # preprocess-> features_train_set and labels_train_set
   # ...
   classifier     = tf.estimator.Estimator(model_fn = model_custom)
   train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn(x={"words": features_train_set}, y=labels_train_set, batch_size=batch_size_param, num_epochs=None, shuffle=True)
   classifier.train(input_fn=train_input_fn, steps=100)
   full_model_dir = classifier.export_savedmodel(export_dir_base="C:/models/directory_base", serving_input_receiver_fn=serving_input_receiver_fn)

Testing script updated code

def main():
   # ...
   # preprocess-> features_test_set
   # ...
   with tf.compat.v1.Session() as sess:
       tf.compat.v1.saved_model.loader.load(sess, [tf.saved_model.SERVING], full_model_dir)
       predictor   = tf.contrib.predictor.from_saved_model(full_model_dir)
       model_input = tf.train.Example(features=tf.train.Features( feature={"words": tf.train.Feature(int64_list=tf.train.Int64List(value=features_test_set)) })) 
       model_input = model_input.SerializeToString()
       output_dict = predictor({"predictor_inputs":[model_input]})
       y_predicted = output_dict["pred_output_classes"][0]