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machinelearning147 / min-char-rnn.py
Created June 19, 2018 02:42 — forked from karpathy/min-char-rnn.py
Minimal character-level language model with a Vanilla Recurrent Neural Network, in Python/numpy
"""
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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machinelearning147 / mnist_estimator.py
Created July 3, 2018 18:39 — forked from shravankumar147/mnist_estimator.py
Example using TensorFlow Estimator, Experiment & Dataset on MNIST data.
"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data as mnist_data
from tensorflow.contrib import slim
from tensorflow.contrib.learn import ModeKeys
from tensorflow.contrib.learn import learn_runner
# Show debugging output
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machinelearning147 / np_to_tfrecords.py
Created August 12, 2018 18:55 — forked from swyoon/np_to_tfrecords.py
From numpy ndarray to tfrecords
import numpy as np
import tensorflow as tf
__author__ = "Sangwoong Yoon"
def np_to_tfrecords(X, Y, file_path_prefix, verbose=True):
"""
Converts a Numpy array (or two Numpy arrays) into a tfrecord file.
For supervised learning, feed training inputs to X and training labels to Y.
For unsupervised learning, only feed training inputs to X, and feed None to Y.