Michal Lukac Cospel

I hereby claim:

To claim this, I am signing this object:

	# create the base model
	base_model = tf.keras.applications.MobileNetV2(include_top=False, weights="imagenet", input_shape=shape)

	# apply normalization on input
	inputs = tf.keras.Input(shape=shape, name="input")
	x = PreprocessTFLayer()(inputs)
	x = base_model(x)
	x = tf.keras.layers.GlobalAveragePooling2D()(x)
	outputs = tf.keras.layers.Dense(2, activation="softmax", name="probs")(x)

	class PreprocessTFLayer(tf.keras.layers.Layer):
	def __init__(self, name="preprocess_tf", **kwargs):
	super(PreprocessTFLayer, self).__init__(name=name, **kwargs)
	self.preprocess = preprocess_tf

	def call(self, input):
	return self.preprocess(input)

	def get_config(self):
	config = super(PreprocessTFLayer, self).get_config()

	@tf.function
	def preprocess_tf(x):
	"""
	Preprocessing for Keras (MobileNetV2, ResNetV2).
	:param x: np.asarray([image, image, ...], dtype="float32") in RGB
	:return: normalized image tf style (RGB)
	"""
	batch, height, width, channels = x.shape
	x = tf.cast(x, tf.float32)

	import cv2
	import numpy as np

	# load image in cv2
	img = cv2.imread('test_image.jpg')

	# the image should be converted to grayscale
	img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

	# apply threshod

	import tensorflow as tf
	import numpy as np


	class BlurPool(tf.keras.layers.Layer):
	"""
	https://arxiv.org/abs/1904.11486
	https://github.com/adobe/antialiased-cnns
	https://github.com/adobe/antialiased-cnns/issues/10
	"""

	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<title>D3 Confusion/Correlation Matrix</title>
	<meta name="description" content="D3 Heatmap Demo">
	<meta name="author" content="Marcos Iglesias">
	<script src="https://d3js.org/d3.v5.min.js"></script>
	<link rel="stylesheet" href="src/css/styles.css">
	</head>

	from redis import Redis
	from redis_hashring import RingNode
	import gevent
	import threading
	import random
	import signal
	N_KEYS = 10

	# we cannot use thread object
	class Listener():#threading.Thread):

	import sys, getopt

	import tensorflow as tf

	usage_str = 'python tensorflow_rename_variables.py --checkpoint_dir=path/to/dir/ ' \
	'--replace_from=substr --replace_to=substr --add_prefix=abc --dry_run'


	def rename(checkpoint_dir, replace_from, replace_to, add_prefix, dry_run):
	checkpoint = tf.train.get_checkpoint_state(checkpoint_dir)