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// VAE by modifying official autoencoder code
// https://github.com/tensorflow/swift-models/blob/2fa11ba1d28ef09454af9da77e22b585cf3e5b7b/Autoencoder/main.swift


// Copyright 2019 The TensorFlow Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//

t-ae

struct Kollection<T> {
    var array: Array<T>
    let indices: [Int]
    
    subscript(indice: Int) -> T {
        get {
            precondition(indices.contains(indice))
            return array[indice]
        }
        set {

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import torch
import torch.nn as nn
import torch.nn.init as init

class MinibatchDiscrimination(nn.Module):
    def __init__(self, in_features, out_features, kernel_dims, mean=False):
        super().__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.kernel_dims = kernel_dims

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import tensorflow as tf
from keras import backend as K

config = tf.ConfigProto()
config.gpu_options.allow_growth=True
sess = tf.Session(config=config)
K.set_session(sess)

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#!/usr/bin/env python

import numpy as np
from keras.models import Sequential
from keras.layers import InputLayer, Reshape, Flatten, Dense
from keras.layers.convolutional import Conv2D, MaxPooling2D, Conv2DTranspose
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import ELU
from keras.datasets import cifar10
from keras.optimizers import Adam

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#!/usr/bin/env python

import numpy as np
from keras.models import Sequential
from keras.layers import InputLayer
from keras.backend import image_dim_ordering, set_image_dim_ordering
from pixel_shuffler import PixelShuffler

batch_size = 6
in_height = 4

t-ae

"""
The MIT License (MIT)

Copyright (c) 2018 Takehiro Araki.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is

t-ae

#!/usr/bin/env python

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

def set_trainable(model, trainable):
    model.trainable = trainable
    for layer in model.layers:
        layer.trainable = trainable