Skand pvskand

VGG-Face model for Keras

This is the Keras model of VGG-Face.

It has been obtained through the following method:

vgg-face-keras:directly convert the vgg-face matconvnet model to keras model
vgg-face-keras-fc:first convert vgg-face caffe model to mxnet model,and then convert it to keras model

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

	# link to package https://github.com/lucidrains/slot-attention

	import torch
	from torch import nn

	class Residual(nn.Module):
	def __init__(self, fn):
	super().__init__()
	self.fn = fn
	def forward(self, x):

	import gym
	import numpy as np
	import torch
	import torch.nn as nn
	import matplotlib.pyplot as plt
	from torch.optim import Adam
	from torch.distributions import Categorical
	from collections import namedtuple

	env = gym.make('CartPole-v0')

	import torch
	from torch.autograd import Variable
	import torch.nn as nn
	import torch.nn.functional as F
	import numpy as np
	import numpy
	import cv2
	import torch.optim as optim
	from theano.tensor import *
	import numpy

	# -- coding: utf-8 --
	import numpy as np
	import os
	import cv2
	import pandas as pd

	from sklearn.cross_validation import train_test_split
	from keras.models import Sequential
	from keras.layers.core import Dense, Dropout, Activation, Flatten
	from keras.layers.convolutional import Convolution2D

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats