Rishikesh (ऋषिकेश) rishikksh20

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:

	import functools
	import numpy as np
	import tensorflow.compat.v1 as tf
	from tensorflow.python.tpu import tpu_function


	BATCH_NORM_DECAY = 0.9
	BATCH_NORM_EPSILON = 1e-5

	#!/usr/bin/python
	import os,sys,shutil
	from playsound import playsound
	phrase = sys.argv[1]
	with open('harvard_sentences.txt','w') as f:
	f.write("some horseshit it never reads this line\n1. "+str(phrase)+"\n")

	os.system('python synthesize.py')
	phrase=phrase.replace(" ","")
	shutil.move('samples/1.wav','backup/'+phrase+'.wav')

	from __future__ import print_function
	import torch
	import torch.nn as nn
	import torch.nn.functional as F
	from torch.autograd import Variable

	def sample_gumbel(shape, eps=1e-20):
	U = torch.rand(shape).cuda()
	return -Variable(torch.log(-torch.log(U + eps) + eps))

	from keras.optimizers import Adam
	from keras import backend as K
	from keras.datasets import mnist
	from keras.utils.np_utils import to_categorical
	from keras.metrics import categorical_accuracy
	from keras.initializations import glorot_uniform, zero
	import numpy as np

	# inputs and targets are placeholders
	input_dim = 28*28

	-- Xception model
	-- a Torch7 implementation of: https://arxiv.org/abs/1610.02357
	-- E. Culurciello, October 2016

	require 'nn'
	local nClasses = 1000

	function nn.SpatialSeparableConvolution(nInputPlane, nOutputPlane, kW, kH)
	local block = nn.Sequential()
	block:add(nn.SpatialConvolutionMap(nn.tables.oneToOne(nInputPlane), kW,kH, 1,1, 1,1))