Samitha Jayaweera samithaj

## batch_image_resize.sh
#!/bin/bash
# This script resizes all the images it finds in a folder (and its subfolders) and resizes them
# The resized image is placed in the /resized folder which will reside in the same directory as the image
#
# Usage: > ./batch_resize.sh

initial_folder="./" # You can use "." to target the folder in which you are running the script for example
resized_folder_name="./resized"

all_images=$(find -E $initial_folder -iregex ".*\.(jpg|gif|png|jpeg)")

## test_import.md

      
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                samithaj
                / test_import.md
            
            
              Created
              August 19, 2017 21:04
                — forked from avloss/test_import.md
            
          
    TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

Tutorial examples have C++ code (which I don't know)
Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

  
## seq2seq.py
import logging

import numpy as np
import tensorflow as tf
from tensorflow.contrib import layers


GO_TOKEN = 0
END_TOKEN = 1
UNK_TOKEN = 2

## tf_seq2seq_single_str_inference.py
from pydoc import locate
import tensorflow as tf
import numpy as np
from seq2seq import tasks, models
from seq2seq.training import utils as training_utils
from seq2seq.tasks.inference_task import InferenceTask, unbatch_dict


class DecodeOnce(InferenceTask):
  '''

## mem-ts-RNN.py
# Code for Jupyter/IPython Notebook environment

from keras.models import Sequential
from keras.layers.core import TimeDistributedDense, Activation, Dropout
from keras.layers.recurrent import GRU
import numpy as np
from keras.utils.layer_utils import print_layer_shapes

%matplotlib inline
import matplotlib.pyplot as plt

## hyperband.py
"""
We implement additional hyperparameter optimization methods not present in
https://scikit-optimize.github.io/.

Gist: https://gist.github.com/Deepblue129/2c5fae9daf0529ed589018c6353c9f7b
"""

import math
import logging
import random
	#!/bin/bash
	# This script resizes all the images it finds in a folder (and its subfolders) and resizes them
	# The resized image is placed in the /resized folder which will reside in the same directory as the image
	#
	# Usage: > ./batch_resize.sh

	initial_folder="./" # You can use "." to target the folder in which you are running the script for example
	resized_folder_name="./resized"

	all_images=$(find -E $initial_folder -iregex ".*\.(jpg\|gif\|png\|jpeg)")
	import logging

	import numpy as np
	import tensorflow as tf
	from tensorflow.contrib import layers


	GO_TOKEN = 0
	END_TOKEN = 1
	UNK_TOKEN = 2
	from pydoc import locate
	import tensorflow as tf
	import numpy as np
	from seq2seq import tasks, models
	from seq2seq.training import utils as training_utils
	from seq2seq.tasks.inference_task import InferenceTask, unbatch_dict


	class DecodeOnce(InferenceTask):
	'''
	# Code for Jupyter/IPython Notebook environment

	from keras.models import Sequential
	from keras.layers.core import TimeDistributedDense, Activation, Dropout
	from keras.layers.recurrent import GRU
	import numpy as np
	from keras.utils.layer_utils import print_layer_shapes

	%matplotlib inline
	import matplotlib.pyplot as plt
	"""
	We implement additional hyperparameter optimization methods not present in
	https://scikit-optimize.github.io/.

	Gist: https://gist.github.com/Deepblue129/2c5fae9daf0529ed589018c6353c9f7b
	"""

	import math
	import logging
	import random