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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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# Copyright 2016 The TensorFlow Authors All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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import numpy as np
import tensorflow as tf
import os

from tensorflow.python.platform import app
from tensorflow.python.platform import flags

from prediction_input import build_tfrecord_input
from prediction_model import construct_model

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tf.estimator

The Estimator is a high-level API that is officially provided by TensorFlow. One of the advantages of using tf.estimator is that the code automatically builds a graph and a tensorboard itself. A user only needs to specify required arguments, such as features, optimizer, and labels, and plug into the estimator class. Just like PyTorch, the Estimator can easily switch to training, evaluation, or test modes without creating individual methods. Also, estimators are built upon the tf.keras.layers which simplifies the customization. It is preferred to know the Estimator usage because TensorFlow offers TPUEstimator which extends to the Estimator class.

The entire structure of constructing estimator model is shown below.

def model_fn(features, targets, mode, params):

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from sklearn.datasets import fetch_mldata
import urllib
import scipy

try:
    mnist = fetch_mldata('MNIST original')
except urllib.error.HTTPError as ex:
    print("Could not download MNIST data from mldata.org, trying alternative...")

    # Alternative method to load MNIST, if mldata.org is down

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# extending on code from
# https://github.com/58402140/Fruit
import os
import numpy as np
import matplotlib
matplotlib.use('TkAgg')
from matplotlib import pyplot as plt
import copy
import time
from collections import Counter