DibyaranjanSathua

# -*- coding: utf-8 -*-
import json
import scrapy
from scraper4.items import Scraper4Item


class MediumSpider(scrapy.Spider):
    name = 'medium'
    allowed_domains = ['medium.com']
    

DibyaranjanSathua

#!/usr/bin/env python3
# -*- coding: utf-8 -*-

"""
Author:         Dibyaranjan Sathua
Created on:     2019-02-28 12:26:54

"""

import tensorflow as tf

DibyaranjanSathua

import cv2
import numpy as np

def test_dnn_module():
    """ Function to test OpenCV DNN module. """
    model = 'removed_dropout_graph.pb'
    net = cv2.dnn.readNet(model)
    img = cv2.imread('F1_R0_A.png')
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    imgGray = imgGray.astype(np.uint8)

DibyaranjanSathua

import tensorflow as tf
from tensorflow.core.framework import graph_pb2
import copy

def remove_dropout(input_graph, output_graph):
    """ Remove the dropout block from the model. """
    graph_def = load_graph(input_graph)
    new_graph_def = graph_pb2.GraphDef()

    for node in graph_def.node:

DibyaranjanSathua

import tensorflow as tf
from tensorflow.core.framework import graph_pb2
import copy

def optimize_batch_normalization(input_graph, output_graph):
    """ Optimize the batch normalization block. """
    graph_def = load_graph(input_graph)         # Defined above
    new_graph_def = graph_pb2.GraphDef()
    unused_attrs = ['is_training']              # Attributes of FusedBatchNorm. Not needed during inference.

DibyaranjanSathua

import tensorflow as tf
from tensorflow.core.framework import graph_pb2
import copy

def load_graph(filename):
    """ Use to read the tensorflow protobuf graph file. """
    graph_def = tf.GraphDef()
    with tf.gfile.FastGFile(filename, 'rb') as f:
        graph_def.ParseFromString(f.read())
    return graph_def

DibyaranjanSathua

import tensorflow as tf
from tensorflow.tools.graph_transforms import TransformGraph

def transform_graph(input_graph, output_graph):
    """ Use to run different transform function on the input graph and generate a output graph. """
    with tf.gfile.GFile(input_graph, 'rb') as fid:
        od_graph_def = tf.GraphDef()
        serialized_graph = fid.read()
        od_graph_def.ParseFromString(serialized_graph)
        od_graph_def = TransformGraph(od_graph_def, ['input_placeholder/input_image'], ['predicated_output'],