25+Lights+2nd+embed.ipynb

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   "source": [
    "from keras.layers import Input, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "# this is the size of our encoded representations\n",
    "encoding_dim = 32  # 32 floats, \n",
    "array_size = imageSize*imageSize\n",
    "# this is our input placeholder\n",
    "input_img = Input(shape=(array_size,))\n",
    "# \"encoded\" is the encoded representation of the input\n",
    "encoded = Dense(encoding_dim, activation='relu')(input_img)\n",
    "# \"decoded\" is the lossy reconstruction of the input\n",
    "decoded = Dense(array_size, activation='sigmoid')(encoded)\n",
    "\n",
    "# this model maps an input to its reconstruction\n",
    "autoencoder = Model(input_img, decoded)\n",
    "\n",
    "encoder = Model(input_img, encoded)\n",
    "\n",
    "# create a placeholder for an encoded (32-dimensional) input\n",
    "encoded_input = Input(shape=(encoding_dim,))\n",
    "# retrieve the last layer of the autoencoder model\n",
    "decoder_layer = autoencoder.layers[-1]\n",
    "# create the decoder model\n",
    "decoder = Model(encoded_input, decoder_layer(encoded_input))\n",
    "\n",
    "autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "training_images = imageSet(batchSize=batch_size, imageSize=imageSize, dotsPerSide=4)\n",
    "training_images = training_images.reshape((len(training_images), np.prod(training_images.shape[1:])))\n",
    "for i in range(0,500):\n",
    "    autoencoder.fit(training_images, training_images,\n",
    "                    epochs=1,\n",
    "                    batch_size=batch_size)"
   ]
  }
 ],
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