asimjalis

How to deploy a Python application as a zip file

by Asim Jalis, MetaProse.com

Create a file __main__.py containing:

print "Hello world from Python"

Zip up the Python files (in this case just this one file) into app.zip by typing:

karpathy

"""
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)

bamos

$ tree data/mydataset/raw
person-1
├── image-1.jpg
├── image-2.png
...
└── image-p.png

...

person-m

swyoon

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.

gangliao

import tensorflow as tf

filenames = ["hdfs://10.152.104.73:8020/sogou/train_data/1_final.feature_transform"]

dataset = tf.data.TextLineDataset(filenames)

iterator = dataset.make_one_shot_iterator()

next_batch = iterator.get_next()

shravankumar147

"""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