sjain07

Verifying that +sj7 is my openname (Bitcoin username). https://onename.com/sj7

sjain07

graph = tf.Graph()
with graph.as_default():
  train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
  train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
  valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
  with tf.device('/cpu:0'):
    embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
    embed = tf.nn.embedding_lookup(embeddings, train_inputs)
    nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0 / math.sqrt(embedding_size)))
    nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

sjain07

with tf.Session(graph=graph) as session:
  init.run()
  average_loss = 0
  for step in range(10001):
    batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}
    _, loss_val, normalized_embeddings_np = session.run([optimizer, loss, normalized_embeddings], feed_dict=feed_dict)
    average_loss += loss_val
  final_embeddings = normalized_embeddings.eval()

sjain07

events {}

http {
  upstream pgdb {
    postgres_server urlshortnertestdb.cccccccccccc.us-west-2.rds.amazonaws.com dbname=redirect  user=root password=password;
  }

  server {
    listen 80;
    server_name localhost;

sjain07

#!/bin/bash
set -e
sudo apt-get update
sudo apt-get install -y --no-install-recommends \
      build-essential \
      cmake \
      git \
      libgoogle-glog-dev \
      libprotobuf-dev \
      protobuf-compiler \

sjain07

FROM tensorflow/tensorflow:1.5.0-gpu
RUN apt-get update && apt-get install -y --no-install-recommends \
    python-pip \
    protobuf-compiler \
    python-pil \
    python-lxml \
    python-numpy \
    python-scipy \
    python-dev \
    python-pip \

sjain07

#this is an Image of size 140x140. We will assume it to be black and white (ie only one channel, it would have been 140x140x3 for rgb)
image = readImage()

#We will break the Image into 7 coloumns and 7 rows and process each of the 49 different parts independently
NoOfCells = 7

#we will try and predict if an image is a dog, cat, cow or wolf. Therfore the number of classes is 4
NoOfClasses = 4
threshold = 0.7

sjain07

image = readImage()
NoOfCells = 7
NoOfClasses = 4
threshold = 0.7
step = height(image)/NoOfCells
prediction_class_array = new_array(size(NoOfCells,NoOfCells,NoOfClasses))
predictions_bounding_box_array = new_array(size(NoOfCells,NoOfCells,NoOfCells,NoOfCells))
final_predictions = []
for (i<0; i<NoOfCells; i=i+1):
	for (j<0; j<NoOfCells;j=j+1):

sjain07

import requests, os, sys

model_id = os.environ.get('NANONETS_MODEL_ID')
api_key = os.environ.get('NANONETS_API_KEY')
image_path = sys.argv[1]

url = 'https://app.nanonets.com/api/v2/ObjectDetection/Model/' + model_id + '/LabelFile/'

data = {'file': open(image_path, 'rb'),    'modelId': ('', model_id)}

sjain07

import picamera, os
from PIL import Image, ImageDraw
camera = picamera.PiCamera()
camera.capture('image1.jpg')
os.system("xdg-open image1.jpg")