Ricardo Guerrero Gómez-Olmedo ricgu8086

git clone [email protected]:YOUR-USERNAME/YOUR-FORKED-REPO.git

cd into/cloned/fork-repo
git remote add upstream git://github.com/ORIGINAL-DEV-USERNAME/REPO-YOU-FORKED-FROM.git
git fetch upstream

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

	#!/bin/bash
	sudo apt-get install -y \
	apt-transport-https \
	ca-certificates \
	curl \
	software-properties-common
	curl -fsSL https://download.docker.com/linux/ubuntu/gpg \| sudo apt-key add -
	sudo apt-key fingerprint 0EBFCD88
	sudo add-apt-repository \
	"deb [arch=amd64] https://download.docker.com/linux/ubuntu \

	def seed_everything(seed: int):
	import random, os
	import numpy as np
	import torch

	random.seed(seed)
	os.environ['PYTHONHASHSEED'] = str(seed)
	np.random.seed(seed)
	torch.manual_seed(seed)
	torch.cuda.manual_seed(seed)

	import numpy as np
	from keras.models import Sequential
	from keras.layers.core import Activation, Dense
	from keras.optimizers import SGD

	X = np.array([[0,0],[0,1],[1,0],[1,1]], "float32")
	y = np.array([[0],[1],[1],[0]], "float32")

	model = Sequential()
	model.add(Dense(2, input_dim=2, activation='sigmoid'))