diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..363b456
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,13 @@
+# ignore the results of running code 'results/'
+results/*/*
+results/*.csv
+!/results/exp1/results.csv
+
+*.pyc
+
+*.hdf5
+*.ipynb_checkpoints
+*.p
+*.h5
+*.HDF5
+__pycache__
\ No newline at end of file
diff --git a/Keras-DEC.ipynb b/Keras-DEC.ipynb
new file mode 100644
index 0000000..40e72c9
--- /dev/null
+++ b/Keras-DEC.ipynb
@@ -0,0 +1,2764 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# [How to do Unsupervised Clustering with Keras](https://www.dlology.com/blog/how-to-do-unsupervised-clustering-with-keras/) | DLology\n",
+    "\n",
+    "Read my blog post for details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\h5py\\__init__.py:34: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
+      "  from ._conv import register_converters as _register_converters\n",
+      "Using TensorFlow backend.\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.datasets import mnist\n",
+    "import numpy as np\n",
+    "np.random.seed(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from time import time\n",
+    "import numpy as np\n",
+    "import keras.backend as K\n",
+    "from keras.engine.topology import Layer, InputSpec\n",
+    "from keras.layers import Dense, Input\n",
+    "from keras.models import Model\n",
+    "from keras.optimizers import SGD\n",
+    "from keras import callbacks\n",
+    "from keras.initializers import VarianceScaling\n",
+    "from sklearn.cluster import KMeans\n",
+    "import metrics\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def autoencoder(dims, act='relu', init='glorot_uniform'):\n",
+    "    \"\"\"\n",
+    "    Fully connected auto-encoder model, symmetric.\n",
+    "    Arguments:\n",
+    "        dims: list of number of units in each layer of encoder. dims[0] is input dim, dims[-1] is units in hidden layer.\n",
+    "            The decoder is symmetric with encoder. So number of layers of the auto-encoder is 2*len(dims)-1\n",
+    "        act: activation, not applied to Input, Hidden and Output layers\n",
+    "    return:\n",
+    "        (ae_model, encoder_model), Model of autoencoder and model of encoder\n",
+    "    \"\"\"\n",
+    "    n_stacks = len(dims) - 1\n",
+    "    # input\n",
+    "    input_img = Input(shape=(dims[0],), name='input')\n",
+    "    x = input_img\n",
+    "    # internal layers in encoder\n",
+    "    for i in range(n_stacks-1):\n",
+    "        x = Dense(dims[i + 1], activation=act, kernel_initializer=init, name='encoder_%d' % i)(x)\n",
+    "\n",
+    "    # hidden layer\n",
+    "    encoded = Dense(dims[-1], kernel_initializer=init, name='encoder_%d' % (n_stacks - 1))(x)  # hidden layer, features are extracted from here\n",
+    "\n",
+    "    x = encoded\n",
+    "    # internal layers in decoder\n",
+    "    for i in range(n_stacks-1, 0, -1):\n",
+    "        x = Dense(dims[i], activation=act, kernel_initializer=init, name='decoder_%d' % i)(x)\n",
+    "\n",
+    "    # output\n",
+    "    x = Dense(dims[0], kernel_initializer=init, name='decoder_0')(x)\n",
+    "    decoded = x\n",
+    "    return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder, encoder = autoencoder(dims, init=init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "x = np.concatenate((x_train, x_test))\n",
+    "y = np.concatenate((y_train, y_test))\n",
+    "x = x.reshape((x.shape[0], -1))\n",
+    "x = np.divide(x, 255.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000, 784)"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n_clusters = len(np.unique(y))\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Base line K-Means clustering accuracy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)\n",
+    "y_pred_kmeans = kmeans.fit_predict(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5322857142857143"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "metrics.acc(y, y_pred_kmeans)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Hyper-params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dims = [x.shape[-1], 500, 500, 2000, 10]\n",
+    "init = VarianceScaling(scale=1. / 3., mode='fan_in',\n",
+    "                           distribution='uniform')\n",
+    "pretrain_optimizer = SGD(lr=1, momentum=0.9)\n",
+    "pretrain_epochs = 300\n",
+    "batch_size = 256\n",
+    "save_dir = './results'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder, encoder = autoencoder(dims, init=init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from keras.utils import plot_model\n",
+    "plot_model(autoencoder, to_file='autoencoder.png', show_shapes=True)\n",
+    "from IPython.display import Image\n",
+    "Image(filename='autoencoder.png') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from keras.utils import plot_model\n",
+    "plot_model(encoder, to_file='encoder.png', show_shapes=True)\n",
+    "from IPython.display import Image\n",
+    "Image(filename='encoder.png') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Pretrain auto-encoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/300\n",
+      "70000/70000 [==============================] - 4s 59us/step - loss: 0.0647\n",
+      "Epoch 2/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0424\n",
+      "Epoch 3/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0310\n",
+      "Epoch 4/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0261\n",
+      "Epoch 5/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0238\n",
+      "Epoch 6/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0223\n",
+      "Epoch 7/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0211\n",
+      "Epoch 8/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0203\n",
+      "Epoch 9/300\n",
+      "70000/70000 [==============================] - 2s 36us/step - loss: 0.0195\n",
+      "Epoch 10/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0189\n",
+      "Epoch 11/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0184\n",
+      "Epoch 12/300\n",
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+      "Epoch 13/300\n",
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+      "Epoch 14/300\n",
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+      "Epoch 15/300\n",
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+      "Epoch 16/300\n",
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+      "Epoch 17/300\n",
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+      "Epoch 18/300\n",
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+      "Epoch 19/300\n",
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+      "Epoch 20/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0157\n",
+      "Epoch 21/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0155\n",
+      "Epoch 22/300\n",
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+      "Epoch 23/300\n",
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+      "Epoch 24/300\n",
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+      "Epoch 25/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0148\n",
+      "Epoch 26/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0147\n",
+      "Epoch 27/300\n",
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+      "Epoch 28/300\n",
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+      "Epoch 29/300\n",
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+      "Epoch 30/300\n",
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+      "Epoch 31/300\n",
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+      "Epoch 32/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0140\n",
+      "Epoch 33/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0139\n",
+      "Epoch 34/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0138\n",
+      "Epoch 35/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0137\n",
+      "Epoch 36/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0136\n",
+      "Epoch 37/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0135\n",
+      "Epoch 38/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0134\n",
+      "Epoch 39/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0133\n",
+      "Epoch 40/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0133\n",
+      "Epoch 41/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0132\n",
+      "Epoch 42/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0131\n",
+      "Epoch 43/300\n",
+      "70000/70000 [==============================] - 2s 36us/step - loss: 0.0130\n",
+      "Epoch 44/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0130\n",
+      "Epoch 45/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0129\n",
+      "Epoch 46/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0128\n",
+      "Epoch 47/300\n",
+      "70000/70000 [==============================] - 2s 36us/step - loss: 0.0128\n",
+      "Epoch 48/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0127\n",
+      "Epoch 49/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0127\n",
+      "Epoch 50/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0126\n",
+      "Epoch 51/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0125\n",
+      "Epoch 52/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0125\n",
+      "Epoch 53/300\n",
+      "70000/70000 [==============================] - 2s 36us/step - loss: 0.0124\n",
+      "Epoch 54/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0124\n",
+      "Epoch 55/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0123\n",
+      "Epoch 56/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0123\n",
+      "Epoch 57/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0122\n",
+      "Epoch 58/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0122\n",
+      "Epoch 59/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0121\n",
+      "Epoch 60/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0121\n",
+      "Epoch 61/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0120\n",
+      "Epoch 62/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0120\n",
+      "Epoch 63/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0119\n",
+      "Epoch 64/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0119\n",
+      "Epoch 65/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0118\n",
+      "Epoch 66/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0118\n",
+      "Epoch 67/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0118\n",
+      "Epoch 68/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0117\n",
+      "Epoch 69/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0117\n",
+      "Epoch 70/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0116\n",
+      "Epoch 71/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0116\n",
+      "Epoch 72/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0115\n",
+      "Epoch 73/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0115\n",
+      "Epoch 74/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0115\n",
+      "Epoch 75/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0114\n",
+      "Epoch 76/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0114\n",
+      "Epoch 77/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0114\n",
+      "Epoch 78/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0113\n",
+      "Epoch 79/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0113\n",
+      "Epoch 80/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0113\n",
+      "Epoch 81/300\n",
+      "70000/70000 [==============================] - 2s 36us/step - loss: 0.0112\n",
+      "Epoch 82/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0112\n",
+      "Epoch 83/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0112\n",
+      "Epoch 84/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0111\n",
+      "Epoch 85/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0111\n",
+      "Epoch 86/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0111\n",
+      "Epoch 87/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0110\n",
+      "Epoch 88/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0110\n",
+      "Epoch 89/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0110\n",
+      "Epoch 90/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0110\n",
+      "Epoch 91/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0109\n",
+      "Epoch 92/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0109\n",
+      "Epoch 93/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0109\n",
+      "Epoch 94/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0108\n",
+      "Epoch 95/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0108\n",
+      "Epoch 96/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0108\n",
+      "Epoch 97/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0108\n",
+      "Epoch 98/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0107\n",
+      "Epoch 99/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0107\n",
+      "Epoch 100/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0107\n",
+      "Epoch 101/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0107\n",
+      "Epoch 102/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0106\n",
+      "Epoch 103/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0106\n",
+      "Epoch 104/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0106\n",
+      "Epoch 105/300\n",
+      "70000/70000 [==============================] - 3s 36us/step - loss: 0.0106\n",
+      "Epoch 106/300\n",
+      "70000/70000 [==============================] - 3s 36us/step - loss: 0.0105\n",
+      "Epoch 107/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0105\n",
+      "Epoch 108/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0105\n",
+      "Epoch 109/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0105\n",
+      "Epoch 110/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0104\n",
+      "Epoch 111/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0104\n",
+      "Epoch 112/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0104\n",
+      "Epoch 113/300\n",
+      "70000/70000 [==============================] - 3s 37us/step - loss: 0.0104\n",
+      "Epoch 114/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0104\n",
+      "Epoch 115/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0103\n",
+      "Epoch 116/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0103\n",
+      "Epoch 117/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0103\n",
+      "Epoch 118/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0103\n",
+      "Epoch 119/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0103\n",
+      "Epoch 120/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0102\n",
+      "Epoch 121/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0102\n",
+      "Epoch 122/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0102\n",
+      "Epoch 123/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0102\n",
+      "Epoch 124/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0102\n",
+      "Epoch 125/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0101\n",
+      "Epoch 126/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0101\n",
+      "Epoch 127/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0101\n",
+      "Epoch 128/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0101\n",
+      "Epoch 129/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0101\n",
+      "Epoch 130/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0100\n",
+      "Epoch 131/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0100\n",
+      "Epoch 132/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0100\n",
+      "Epoch 133/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0100\n",
+      "Epoch 134/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0100\n",
+      "Epoch 135/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0100\n",
+      "Epoch 136/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0099\n",
+      "Epoch 137/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0099\n",
+      "Epoch 138/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0099\n",
+      "Epoch 139/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0099\n",
+      "Epoch 140/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0099\n",
+      "Epoch 141/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0099\n",
+      "Epoch 142/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0098\n",
+      "Epoch 143/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0098\n",
+      "Epoch 144/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0098\n",
+      "Epoch 145/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0098\n",
+      "Epoch 146/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0098\n",
+      "Epoch 147/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0098\n",
+      "Epoch 148/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0098\n",
+      "Epoch 149/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0097\n",
+      "Epoch 150/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0097\n",
+      "Epoch 151/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0097\n",
+      "Epoch 152/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0097\n",
+      "Epoch 153/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0097\n",
+      "Epoch 154/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0097\n",
+      "Epoch 155/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0096\n",
+      "Epoch 156/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0096\n",
+      "Epoch 157/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0096\n",
+      "Epoch 158/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0096\n",
+      "Epoch 159/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0096\n",
+      "Epoch 160/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0096\n",
+      "Epoch 161/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0096\n",
+      "Epoch 162/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0095\n",
+      "Epoch 163/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0095\n",
+      "Epoch 164/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0095\n",
+      "Epoch 165/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0095\n",
+      "Epoch 166/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0095\n",
+      "Epoch 167/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0095\n",
+      "Epoch 168/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0095\n",
+      "Epoch 169/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0095\n",
+      "Epoch 170/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 171/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 172/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 173/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 174/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 175/300\n",
+      "70000/70000 [==============================] - 2s 36us/step - loss: 0.0094\n",
+      "Epoch 176/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 177/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0094\n",
+      "Epoch 178/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 179/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 180/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 181/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 182/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 183/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 184/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 185/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0093\n",
+      "Epoch 186/300\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 187/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 188/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 189/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 190/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0092\n",
+      "Epoch 191/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0092\n",
+      "Epoch 192/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 193/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 194/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 195/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0092\n",
+      "Epoch 196/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 197/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 198/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 199/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 200/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 201/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 202/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 203/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 204/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0091\n",
+      "Epoch 205/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0090\n",
+      "Epoch 206/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0090\n",
+      "Epoch 207/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0090\n",
+      "Epoch 208/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0090\n",
+      "Epoch 209/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0090\n",
+      "Epoch 210/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0090\n",
+      "Epoch 211/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0090\n",
+      "Epoch 212/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0090\n",
+      "Epoch 213/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0090\n",
+      "Epoch 214/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0090\n",
+      "Epoch 215/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 216/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0089\n",
+      "Epoch 217/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 218/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 219/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0089\n",
+      "Epoch 220/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 221/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 222/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 223/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0089\n",
+      "Epoch 224/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0089\n",
+      "Epoch 225/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0089\n",
+      "Epoch 226/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 227/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 228/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 229/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0088\n",
+      "Epoch 230/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 231/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0088\n",
+      "Epoch 232/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0088\n",
+      "Epoch 233/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0088\n",
+      "Epoch 234/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 235/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 236/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 237/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0088\n",
+      "Epoch 238/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0088\n",
+      "Epoch 239/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 240/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0087\n",
+      "Epoch 241/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 242/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 243/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 244/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 245/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 246/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 247/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 248/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0087\n",
+      "Epoch 249/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0087\n",
+      "Epoch 250/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 251/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 252/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 253/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 254/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0086\n",
+      "Epoch 255/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 256/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 257/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 258/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0086\n",
+      "Epoch 259/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0086\n",
+      "Epoch 260/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0086\n",
+      "Epoch 261/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 262/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 263/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 264/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0086\n",
+      "Epoch 265/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 266/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0085\n",
+      "Epoch 267/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 268/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0085\n",
+      "Epoch 269/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 270/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0085\n",
+      "Epoch 271/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0085\n",
+      "Epoch 272/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 273/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0085\n",
+      "Epoch 274/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0085\n",
+      "Epoch 275/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 276/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 277/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0085\n",
+      "Epoch 278/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 279/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 280/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 281/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 282/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 283/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 284/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 285/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 286/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 287/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 288/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 289/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 290/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 291/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0084\n",
+      "Epoch 292/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0084\n",
+      "Epoch 293/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0083\n",
+      "Epoch 294/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0083\n",
+      "Epoch 295/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0083\n",
+      "Epoch 296/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0083\n",
+      "Epoch 297/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0083\n",
+      "Epoch 298/300\n",
+      "70000/70000 [==============================] - 2s 34us/step - loss: 0.0083\n",
+      "Epoch 299/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0083\n",
+      "Epoch 300/300\n",
+      "70000/70000 [==============================] - 2s 35us/step - loss: 0.0083\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "Unable to create file (Unable to open file: name = './results/results/ae_weights.h5', errno = 2, error message = 'no such file or directory', flags = 13, o_flags = 302)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-12-4174a3d64316>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mautoencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpretrain_optimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'mse'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mautoencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpretrain_epochs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#, callbacks=cb)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mautoencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_dir\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'/results/ae_weights.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;32mc:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\keras\\engine\\topology.py\u001b[0m in \u001b[0;36msave_weights\u001b[1;34m(self, filepath, overwrite)\u001b[0m\n\u001b[0;32m   2608\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mproceed\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2609\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2610\u001b[1;33m         \u001b[1;32mwith\u001b[0m \u001b[0mh5py\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2611\u001b[0m             \u001b[0msave_weights_to_hdf5_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2612\u001b[0m             \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\h5py\\_hl\\files.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, name, mode, driver, libver, userblock_size, swmr, **kwds)\u001b[0m\n\u001b[0;32m    269\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    270\u001b[0m                 \u001b[0mfapl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_fapl\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdriver\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlibver\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 271\u001b[1;33m                 \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmake_fid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muserblock_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mswmr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mswmr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    272\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    273\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mc:\\users\\hasee\\appdata\\local\\programs\\python\\python35\\lib\\site-packages\\h5py\\_hl\\files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[1;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[0;32m    105\u001b[0m         \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mACC_EXCL\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    106\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'w'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 107\u001b[1;33m         \u001b[0mfid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mACC_TRUNC\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfcpl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    108\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'a'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    109\u001b[0m         \u001b[1;31m# Open in append mode (read/write).\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (D:\\Build\\h5py\\h5py-2.7.0\\h5py\\_objects.c:2853)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mh5py\\_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper (D:\\Build\\h5py\\h5py-2.7.0\\h5py\\_objects.c:2811)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;32mh5py\\h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.create (D:\\Build\\h5py\\h5py-2.7.0\\h5py\\h5f.c:2297)\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mOSError\u001b[0m: Unable to create file (Unable to open file: name = './results/results/ae_weights.h5', errno = 2, error message = 'no such file or directory', flags = 13, o_flags = 302)"
+     ]
+    }
+   ],
+   "source": [
+    "autoencoder.compile(optimizer=pretrain_optimizer, loss='mse')\n",
+    "autoencoder.fit(x, x, batch_size=batch_size, epochs=pretrain_epochs) #, callbacks=cb)\n",
+    "autoencoder.save_weights(save_dir + '/ae_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.save_weights(save_dir + '/ae_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the pre-trained auto encoder weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.load_weights(save_dir + '/ae_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build clustering model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ClusteringLayer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class ClusteringLayer(Layer):\n",
+    "    \"\"\"\n",
+    "    Clustering layer converts input sample (feature) to soft label, i.e. a vector that represents the probability of the\n",
+    "    sample belonging to each cluster. The probability is calculated with student's t-distribution.\n",
+    "\n",
+    "    # Example\n",
+    "    ```\n",
+    "        model.add(ClusteringLayer(n_clusters=10))\n",
+    "    ```\n",
+    "    # Arguments\n",
+    "        n_clusters: number of clusters.\n",
+    "        weights: list of Numpy array with shape `(n_clusters, n_features)` witch represents the initial cluster centers.\n",
+    "        alpha: degrees of freedom parameter in Student's t-distribution. Default to 1.0.\n",
+    "    # Input shape\n",
+    "        2D tensor with shape: `(n_samples, n_features)`.\n",
+    "    # Output shape\n",
+    "        2D tensor with shape: `(n_samples, n_clusters)`.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, n_clusters, weights=None, alpha=1.0, **kwargs):\n",
+    "        if 'input_shape' not in kwargs and 'input_dim' in kwargs:\n",
+    "            kwargs['input_shape'] = (kwargs.pop('input_dim'),)\n",
+    "        super(ClusteringLayer, self).__init__(**kwargs)\n",
+    "        self.n_clusters = n_clusters\n",
+    "        self.alpha = alpha\n",
+    "        self.initial_weights = weights\n",
+    "        self.input_spec = InputSpec(ndim=2)\n",
+    "\n",
+    "    def build(self, input_shape):\n",
+    "        assert len(input_shape) == 2\n",
+    "        input_dim = input_shape[1]\n",
+    "        self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim))\n",
+    "        self.clusters = self.add_weight((self.n_clusters, input_dim), initializer='glorot_uniform', name='clusters')\n",
+    "        if self.initial_weights is not None:\n",
+    "            self.set_weights(self.initial_weights)\n",
+    "            del self.initial_weights\n",
+    "        self.built = True\n",
+    "\n",
+    "    def call(self, inputs, **kwargs):\n",
+    "        \"\"\" student t-distribution, as same as used in t-SNE algorithm.\n",
+    "         Measure the similarity between embedded point z_i and centroid µ_j.\n",
+    "                 q_ij = 1/(1+dist(x_i, µ_j)^2), then normalize it.\n",
+    "                 q_ij can be interpreted as the probability of assigning sample i to cluster j.\n",
+    "                 (i.e., a soft assignment)\n",
+    "        Arguments:\n",
+    "            inputs: the variable containing data, shape=(n_samples, n_features)\n",
+    "        Return:\n",
+    "            q: student's t-distribution, or soft labels for each sample. shape=(n_samples, n_clusters)\n",
+    "        \"\"\"\n",
+    "        q = 1.0 / (1.0 + (K.sum(K.square(K.expand_dims(inputs, axis=1) - self.clusters), axis=2) / self.alpha))\n",
+    "        q **= (self.alpha + 1.0) / 2.0\n",
+    "        q = K.transpose(K.transpose(q) / K.sum(q, axis=1)) # Make sure each sample's 10 values add up to 1.\n",
+    "        return q\n",
+    "\n",
+    "    def compute_output_shape(self, input_shape):\n",
+    "        assert input_shape and len(input_shape) == 2\n",
+    "        return input_shape[0], self.n_clusters\n",
+    "\n",
+    "    def get_config(self):\n",
+    "        config = {'n_clusters': self.n_clusters}\n",
+    "        base_config = super(ClusteringLayer, self).get_config()\n",
+    "        return dict(list(base_config.items()) + list(config.items()))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)\n",
+    "model = Model(inputs=encoder.input, outputs=clustering_layer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from keras.utils import plot_model\n",
+    "plot_model(model, to_file='model.png', show_shapes=True)\n",
+    "from IPython.display import Image\n",
+    "Image(filename='model.png') \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=SGD(0.01, 0.9), loss='kld')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: initialize cluster centers using k-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters=n_clusters, n_init=20)\n",
+    "y_pred = kmeans.fit_predict(encoder.predict(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_last = np.copy(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: deep clustering\n",
+    "Compute p_i by first raising q_i to the second power and then normalizing by frequency per cluster:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# computing an auxiliary target distribution\n",
+    "def target_distribution(q):\n",
+    "    weight = q ** 2 / q.sum(0)\n",
+    "    return (weight.T / weight.sum(1)).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = 0\n",
+    "index = 0\n",
+    "maxiter = 8000\n",
+    "update_interval = 140\n",
+    "index_array = np.arange(x.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tol = 0.001 # tolerance threshold to stop training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 0: acc = 0.91809, nmi = 0.82679, ari = 0.82477  ; loss= 0\n",
+      "Iter 140: acc = 0.92616, nmi = 0.83796, ari = 0.84332  ; loss= 0\n",
+      "Iter 280: acc = 0.93726, nmi = 0.85669, ari = 0.86660  ; loss= 0\n",
+      "Iter 420: acc = 0.94481, nmi = 0.87004, ari = 0.88237  ; loss= 0\n",
+      "Iter 560: acc = 0.94941, nmi = 0.87850, ari = 0.89198  ; loss= 0\n",
+      "Iter 700: acc = 0.95199, nmi = 0.88358, ari = 0.89743  ; loss= 0\n",
+      "Iter 840: acc = 0.95420, nmi = 0.88779, ari = 0.90200  ; loss= 0\n",
+      "Iter 980: acc = 0.95551, nmi = 0.89040, ari = 0.90477  ; loss= 0\n",
+      "Iter 1120: acc = 0.95694, nmi = 0.89310, ari = 0.90774  ; loss= 0\n",
+      "Iter 1260: acc = 0.95801, nmi = 0.89533, ari = 0.90999  ; loss= 0\n",
+      "Iter 1400: acc = 0.95894, nmi = 0.89705, ari = 0.91190  ; loss= 0\n",
+      "Iter 1540: acc = 0.95937, nmi = 0.89805, ari = 0.91283  ; loss= 0\n",
+      "Iter 1680: acc = 0.95990, nmi = 0.89908, ari = 0.91389  ; loss= 0\n",
+      "Iter 1820: acc = 0.96036, nmi = 0.90005, ari = 0.91486  ; loss= 0\n",
+      "Iter 1960: acc = 0.96076, nmi = 0.90074, ari = 0.91569  ; loss= 0\n",
+      "Iter 2100: acc = 0.96099, nmi = 0.90150, ari = 0.91620  ; loss= 0\n",
+      "Iter 2240: acc = 0.96127, nmi = 0.90209, ari = 0.91677  ; loss= 0\n",
+      "Iter 2380: acc = 0.96143, nmi = 0.90261, ari = 0.91711  ; loss= 0\n",
+      "Iter 2520: acc = 0.96153, nmi = 0.90287, ari = 0.91735  ; loss= 0\n",
+      "Iter 2660: acc = 0.96166, nmi = 0.90325, ari = 0.91762  ; loss= 0\n",
+      "Iter 2800: acc = 0.96174, nmi = 0.90343, ari = 0.91779  ; loss= 0\n",
+      "Iter 2940: acc = 0.96154, nmi = 0.90320, ari = 0.91740  ; loss= 0\n",
+      "Iter 3080: acc = 0.96194, nmi = 0.90411, ari = 0.91821  ; loss= 0\n",
+      "Iter 3220: acc = 0.96171, nmi = 0.90362, ari = 0.91776  ; loss= 0\n",
+      "Iter 3360: acc = 0.96207, nmi = 0.90435, ari = 0.91848  ; loss= 0\n",
+      "Iter 3500: acc = 0.96183, nmi = 0.90387, ari = 0.91799  ; loss= 0\n",
+      "Iter 3640: acc = 0.96211, nmi = 0.90455, ari = 0.91856  ; loss= 0\n",
+      "Iter 3780: acc = 0.96196, nmi = 0.90414, ari = 0.91825  ; loss= 0\n",
+      "Iter 3920: acc = 0.96216, nmi = 0.90457, ari = 0.91866  ; loss= 0\n",
+      "Iter 4060: acc = 0.96200, nmi = 0.90425, ari = 0.91833  ; loss= 0\n",
+      "Iter 4200: acc = 0.96199, nmi = 0.90425, ari = 0.91831  ; loss= 0\n",
+      "Iter 4340: acc = 0.96207, nmi = 0.90449, ari = 0.91847  ; loss= 0\n",
+      "Iter 4480: acc = 0.96219, nmi = 0.90467, ari = 0.91871  ; loss= 0\n",
+      "Iter 4620: acc = 0.96207, nmi = 0.90458, ari = 0.91849  ; loss= 0\n",
+      "Iter 4760: acc = 0.96220, nmi = 0.90475, ari = 0.91874  ; loss= 0\n",
+      "Iter 4900: acc = 0.96211, nmi = 0.90469, ari = 0.91857  ; loss= 0\n",
+      "Iter 5040: acc = 0.96223, nmi = 0.90491, ari = 0.91880  ; loss= 0\n",
+      "Iter 5180: acc = 0.96229, nmi = 0.90501, ari = 0.91892  ; loss= 0\n",
+      "Iter 5320: acc = 0.96237, nmi = 0.90521, ari = 0.91909  ; loss= 0\n",
+      "Iter 5460: acc = 0.96231, nmi = 0.90502, ari = 0.91899  ; loss= 0\n",
+      "Iter 5600: acc = 0.96233, nmi = 0.90502, ari = 0.91899  ; loss= 0\n",
+      "Iter 5740: acc = 0.96231, nmi = 0.90500, ari = 0.91897  ; loss= 0\n",
+      "delta_label  0.0009 < tol  0.001\n",
+      "Reached tolerance threshold. Stopping training.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for ite in range(int(maxiter)):\n",
+    "    if ite % update_interval == 0:\n",
+    "        q = model.predict(x, verbose=0)\n",
+    "        p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "        # evaluate the clustering performance\n",
+    "        y_pred = q.argmax(1)\n",
+    "        if y is not None:\n",
+    "            acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "            nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "            ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "            loss = np.round(loss, 5)\n",
+    "            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)\n",
+    "\n",
+    "        # check stop criterion - model convergence\n",
+    "        delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]\n",
+    "        y_pred_last = np.copy(y_pred)\n",
+    "        if ite > 0 and delta_label < tol:\n",
+    "            print('delta_label ', delta_label, '< tol ', tol)\n",
+    "            print('Reached tolerance threshold. Stopping training.')\n",
+    "            break\n",
+    "    idx = index_array[index * batch_size: min((index+1) * batch_size, x.shape[0])]\n",
+    "    model.train_on_batch(x=x[idx], y=p[idx])\n",
+    "    index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0\n",
+    "\n",
+    "model.save_weights(save_dir + '/DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the clustering model trained weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_weights(save_dir + '/DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Acc = 0.96231, nmi = 0.90500, ari = 0.91897  ; loss= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Eval.\n",
+    "q = model.predict(x, verbose=0)\n",
+    "p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "# evaluate the clustering performance\n",
+    "y_pred = q.argmax(1)\n",
+    "if y is not None:\n",
+    "    acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "    nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "    ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "    loss = np.round(loss, 5)\n",
+    "    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc, nmi, ari), ' ; loss=', loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SCTqnQ4cOQMTSOMOGDYt1wqVdu3axaNEiAIoUKUKDBg2iHW/Xrh2pU6cmPDyc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\nCCFKlP8HzP4TIrWDyS4AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1916c499940>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import sklearn.metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set(font_scale=3)\n",
+    "confusion_matrix = sklearn.metrics.confusion_matrix(y, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(16, 14))\n",
+    "sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", annot_kws={\"size\": 20});\n",
+    "plt.title(\"Confusion matrix\", fontsize=30)\n",
+    "plt.ylabel('True label', fontsize=25)\n",
+    "plt.xlabel('Clustering label', fontsize=25)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Break down Accuracy metric\n",
+    "\n",
+    "linear assignment- [Munkres' Assignment Algorithm](http://csclab.murraystate.edu/~bob.pilgrim/445/munkres.html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.9623142857142857"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.utils.linear_assignment_ import linear_assignment\n",
+    "\n",
+    "y_true = y.astype(np.int64)\n",
+    "D = max(y_pred.max(), y_true.max()) + 1\n",
+    "w = np.zeros((D, D), dtype=np.int64)\n",
+    "# Confusion matrix.\n",
+    "for i in range(y_pred.size):\n",
+    "    w[y_pred[i], y_true[i]] += 1\n",
+    "ind = linear_assignment(-w)\n",
+    "\n",
+    "sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[   0, 7656,    9,    2,    4,    3,   10,   22,   16,    9],\n",
+       "       [   1,   23,   45,   50,    6,    0,    0, 6976,    5,  110],\n",
+       "       [   4,   22,   48, 6796,    0,   83,    1,    5,   61,  121],\n",
+       "       [6813,    2,   21,    3,    4,   31,   33,    7,   18,   29],\n",
+       "       [  38,   41,   59,   58,    8,   25,    9,    4, 6512,   64],\n",
+       "       [  12,   14,   11,   14,   90,   34,    0,  119,  108, 6491],\n",
+       "       [   5,    3,    1,  107,    0, 6080,  165,    1,   49,   20],\n",
+       "       [  22,    4,   12,    5,   40,   39, 6642,    0,   12,    5],\n",
+       "       [   4,   96, 6737,  105,   13,   12,    5,  113,   26,    6],\n",
+       "       [   4,   16,   47,    1, 6659,    6,   11,   46,   18,  103]],\n",
+       "      dtype=int64)"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0, 1],\n",
+       "       [1, 7],\n",
+       "       [2, 3],\n",
+       "       [3, 0],\n",
+       "       [4, 8],\n",
+       "       [5, 9],\n",
+       "       [6, 5],\n",
+       "       [7, 6],\n",
+       "       [8, 2],\n",
+       "       [9, 4]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ind"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 7, 3, 0, 8, 9, 5, 6, 2, 4], dtype=int64)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "w.argmax(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Experimental\n",
+    "Convolutional auto encoder."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.models import Model\n",
+    "from keras import backend as K\n",
+    "from keras import layers\n",
+    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D, Flatten, Reshape, Conv2DTranspose\n",
+    "from keras.models import Model\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convolutional auto encoder option 1\n",
+    "Use [`Conv2DTranspose`](https://keras.io/layers/convolutional/#conv2dtranspose) to reconstruct the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def autoencoderConv2D_1(input_shape=(28, 28, 1), filters=[32, 64, 128, 10]):\n",
+    "    input_img = Input(shape=input_shape)\n",
+    "    if input_shape[0] % 8 == 0:\n",
+    "        pad3 = 'same'\n",
+    "    else:\n",
+    "        pad3 = 'valid'\n",
+    "    x = Conv2D(filters[0], 5, strides=2, padding='same', activation='relu', name='conv1', input_shape=input_shape)(input_img)\n",
+    "\n",
+    "    x = Conv2D(filters[1], 5, strides=2, padding='same', activation='relu', name='conv2')(x)\n",
+    "\n",
+    "    x = Conv2D(filters[2], 3, strides=2, padding=pad3, activation='relu', name='conv3')(x)\n",
+    "\n",
+    "    x = Flatten()(x)\n",
+    "    encoded = Dense(units=filters[3], name='embedding')(x)\n",
+    "    x = Dense(units=filters[2]*int(input_shape[0]/8)*int(input_shape[0]/8), activation='relu')(encoded)\n",
+    "\n",
+    "    x = Reshape((int(input_shape[0]/8), int(input_shape[0]/8), filters[2]))(x)\n",
+    "    x = Conv2DTranspose(filters[1], 3, strides=2, padding=pad3, activation='relu', name='deconv3')(x)\n",
+    "\n",
+    "    x = Conv2DTranspose(filters[0], 5, strides=2, padding='same', activation='relu', name='deconv2')(x)\n",
+    "\n",
+    "    decoded = Conv2DTranspose(input_shape[2], 5, strides=2, padding='same', name='deconv1')(x)\n",
+    "    return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convolutional auto encoder option 2\n",
+    "Use [`UpSampling2D`](https://keras.io/layers/convolutional/#upsampling2d) to reconstruct the image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def autoencoderConv2D_2(img_shape=(28, 28, 1)):\n",
+    "    \"\"\"\n",
+    "    Conv2D auto-encoder model.\n",
+    "    Arguments:\n",
+    "        img_shape: e.g. (28, 28, 1) for MNIST\n",
+    "    return:\n",
+    "        (autoencoder, encoder), Model of autoencoder and model of encoder\n",
+    "    \"\"\"\n",
+    "    input_img = Input(shape=img_shape)\n",
+    "    # Encoder\n",
+    "    x = Conv2D(16, (3, 3), activation='relu', padding='same', strides=(2, 2))(input_img)\n",
+    "    x = Conv2D(8, (3, 3), activation='relu', padding='same', strides=(2, 2))(x)\n",
+    "    x = Conv2D(8, (3, 3), activation='relu', padding='same', strides=(2, 2))(x)\n",
+    "    shape_before_flattening = K.int_shape(x)\n",
+    "    # at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
+    "    x = Flatten()(x)\n",
+    "    encoded = Dense(10, activation='relu', name='encoded')(x)\n",
+    "\n",
+    "    # Decoder\n",
+    "    x = Dense(np.prod(shape_before_flattening[1:]),\n",
+    "                activation='relu')(encoded)\n",
+    "    # Reshape into an image of the same shape as before our last `Flatten` layer\n",
+    "    x = Reshape(shape_before_flattening[1:])(x)\n",
+    "\n",
+    "    x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    x = Conv2D(16, (3, 3), activation='relu')(x)\n",
+    "    x = UpSampling2D((2, 2))(x)\n",
+    "    decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)\n",
+    "\n",
+    "    return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pick a convolutional autoencoder\n",
+    "`autoencoderConv2D_1` or `autoencoderConv2D_2`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder, encoder = autoencoderConv2D_1()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_1 (InputLayer)         (None, 28, 28, 1)         0         \n",
+      "_________________________________________________________________\n",
+      "conv1 (Conv2D)               (None, 14, 14, 32)        832       \n",
+      "_________________________________________________________________\n",
+      "conv2 (Conv2D)               (None, 7, 7, 64)          51264     \n",
+      "_________________________________________________________________\n",
+      "conv3 (Conv2D)               (None, 3, 3, 128)         73856     \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 1152)              0         \n",
+      "_________________________________________________________________\n",
+      "embedding (Dense)            (None, 10)                11530     \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 1152)              12672     \n",
+      "_________________________________________________________________\n",
+      "reshape_1 (Reshape)          (None, 3, 3, 128)         0         \n",
+      "_________________________________________________________________\n",
+      "deconv3 (Conv2DTranspose)    (None, 7, 7, 64)          73792     \n",
+      "_________________________________________________________________\n",
+      "deconv2 (Conv2DTranspose)    (None, 14, 14, 32)        51232     \n",
+      "_________________________________________________________________\n",
+      "deconv1 (Conv2DTranspose)    (None, 28, 28, 1)         801       \n",
+      "=================================================================\n",
+      "Total params: 275,979\n",
+      "Trainable params: 275,979\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "autoencoder.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load MNIST data for convolutional input"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.datasets import mnist\n",
+    "\n",
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "x = np.concatenate((x_train, x_test))\n",
+    "y = np.concatenate((y_train, y_test))\n",
+    "x = x.reshape(x.shape + (1,))\n",
+    "x = np.divide(x, 255.)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pretrain covolutional autoencoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pretrain_epochs = 100\n",
+    "batch_size = 256"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.compile(optimizer='adadelta', loss='mse')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100\n",
+      "70000/70000 [==============================] - 8s 108us/step - loss: 0.0797\n",
+      "Epoch 2/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0622\n",
+      "Epoch 3/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0482\n",
+      "Epoch 4/100\n",
+      "70000/70000 [==============================] - 5s 71us/step - loss: 0.0377\n",
+      "Epoch 5/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0325\n",
+      "Epoch 6/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0290\n",
+      "Epoch 7/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0269\n",
+      "Epoch 8/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0254\n",
+      "Epoch 9/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0243\n",
+      "Epoch 10/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0235\n",
+      "Epoch 11/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0226\n",
+      "Epoch 12/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0221\n",
+      "Epoch 13/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0215\n",
+      "Epoch 14/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0210\n",
+      "Epoch 15/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0206\n",
+      "Epoch 16/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0202\n",
+      "Epoch 17/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0199\n",
+      "Epoch 18/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0195\n",
+      "Epoch 19/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0192\n",
+      "Epoch 20/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0190\n",
+      "Epoch 21/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0187\n",
+      "Epoch 22/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0185\n",
+      "Epoch 23/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0183\n",
+      "Epoch 24/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0181\n",
+      "Epoch 25/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0179\n",
+      "Epoch 26/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0177\n",
+      "Epoch 27/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0176\n",
+      "Epoch 28/100\n",
+      "70000/70000 [==============================] - 5s 71us/step - loss: 0.0174\n",
+      "Epoch 29/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0173\n",
+      "Epoch 30/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0171\n",
+      "Epoch 31/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0170\n",
+      "Epoch 32/100\n",
+      "70000/70000 [==============================] - 5s 71us/step - loss: 0.0169\n",
+      "Epoch 33/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0168\n",
+      "Epoch 34/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0167\n",
+      "Epoch 35/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0166\n",
+      "Epoch 36/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0164\n",
+      "Epoch 37/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0163\n",
+      "Epoch 38/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0162\n",
+      "Epoch 39/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0161\n",
+      "Epoch 40/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0160\n",
+      "Epoch 41/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0160\n",
+      "Epoch 42/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0159\n",
+      "Epoch 43/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0158\n",
+      "Epoch 44/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0157\n",
+      "Epoch 45/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0156\n",
+      "Epoch 46/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0155\n",
+      "Epoch 47/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0155\n",
+      "Epoch 48/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0154\n",
+      "Epoch 49/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0154\n",
+      "Epoch 50/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0153\n",
+      "Epoch 51/100\n",
+      "70000/70000 [==============================] - 5s 71us/step - loss: 0.0152\n",
+      "Epoch 52/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0152\n",
+      "Epoch 53/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0151\n",
+      "Epoch 54/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0151\n",
+      "Epoch 55/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0150\n",
+      "Epoch 56/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0150\n",
+      "Epoch 57/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0149\n",
+      "Epoch 58/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0148\n",
+      "Epoch 59/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0148\n",
+      "Epoch 60/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0147\n",
+      "Epoch 61/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0147\n",
+      "Epoch 62/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0147\n",
+      "Epoch 63/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0146\n",
+      "Epoch 64/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0145\n",
+      "Epoch 65/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0145\n",
+      "Epoch 66/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0144\n",
+      "Epoch 67/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0144\n",
+      "Epoch 68/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0144\n",
+      "Epoch 69/100\n",
+      "70000/70000 [==============================] - 5s 71us/step - loss: 0.0143\n",
+      "Epoch 70/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0143\n",
+      "Epoch 71/100\n",
+      "70000/70000 [==============================] - 5s 71us/step - loss: 0.0142\n",
+      "Epoch 72/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0142\n",
+      "Epoch 73/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0142\n",
+      "Epoch 74/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0141\n",
+      "Epoch 75/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0141\n",
+      "Epoch 76/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0140\n",
+      "Epoch 77/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0140\n",
+      "Epoch 78/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0140\n",
+      "Epoch 79/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0140\n",
+      "Epoch 80/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0139\n",
+      "Epoch 81/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0139\n",
+      "Epoch 82/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0139\n",
+      "Epoch 83/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0138\n",
+      "Epoch 84/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0137\n",
+      "Epoch 85/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0137\n",
+      "Epoch 86/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0137\n",
+      "Epoch 87/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0137\n",
+      "Epoch 88/100\n",
+      "70000/70000 [==============================] - 5s 68us/step - loss: 0.0136\n",
+      "Epoch 89/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0136\n",
+      "Epoch 90/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0136\n",
+      "Epoch 91/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0135\n",
+      "Epoch 92/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0136\n",
+      "Epoch 93/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0135\n",
+      "Epoch 94/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0135\n",
+      "Epoch 95/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0135\n",
+      "Epoch 96/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0134\n",
+      "Epoch 97/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0134\n",
+      "Epoch 98/100\n",
+      "70000/70000 [==============================] - 5s 70us/step - loss: 0.0134\n",
+      "Epoch 99/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0134\n",
+      "Epoch 100/100\n",
+      "70000/70000 [==============================] - 5s 69us/step - loss: 0.0133\n"
+     ]
+    },
+    {
+     "ename": "NameError",
+     "evalue": "name 'save_dir' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-9-899334520b52>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mautoencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpretrain_epochs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mautoencoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msave_weights\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msave_dir\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;34m'/conv_ae_weights.h5'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m: name 'save_dir' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "autoencoder.fit(x, x, batch_size=batch_size, epochs=pretrain_epochs)\n",
+    "autoencoder.save_weights(save_dir+'/conv_ae_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder.load_weights(save_dir+'/conv_ae_weights.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Build clustering model with convolutional autoencoder"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)\n",
+    "model = Model(inputs=encoder.input, outputs=clustering_layer)\n",
+    "model.compile(optimizer='adam', loss='kld')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 1: initialize cluster centers using k-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters=n_clusters, n_init=20)\n",
+    "y_pred = kmeans.fit_predict(encoder.predict(x))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_last = np.copy(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Step 2: deep clustering"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss = 0\n",
+    "index = 0\n",
+    "maxiter = 8000\n",
+    "update_interval = 140\n",
+    "index_array = np.arange(x.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tol = 0.001 # tolerance threshold to stop training"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Start training"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 0: acc = 0.72430, nmi = 0.63668, ari = 0.56010  ; loss= 0\n",
+      "Iter 140: acc = 0.74214, nmi = 0.66361, ari = 0.59420  ; loss= 0\n",
+      "Iter 280: acc = 0.75629, nmi = 0.68979, ari = 0.62008  ; loss= 0\n",
+      "Iter 420: acc = 0.76767, nmi = 0.71165, ari = 0.64043  ; loss= 0\n",
+      "Iter 560: acc = 0.77524, nmi = 0.72788, ari = 0.65566  ; loss= 0\n",
+      "Iter 700: acc = 0.77896, nmi = 0.73462, ari = 0.66287  ; loss= 0\n",
+      "Iter 840: acc = 0.78224, nmi = 0.74151, ari = 0.66893  ; loss= 0\n",
+      "Iter 980: acc = 0.78376, nmi = 0.74488, ari = 0.67161  ; loss= 0\n",
+      "Iter 1120: acc = 0.78567, nmi = 0.74940, ari = 0.67503  ; loss= 0\n",
+      "Iter 1260: acc = 0.78653, nmi = 0.75230, ari = 0.67713  ; loss= 0\n",
+      "Iter 1400: acc = 0.78821, nmi = 0.75562, ari = 0.67967  ; loss= 0\n",
+      "Iter 1540: acc = 0.78701, nmi = 0.75534, ari = 0.67847  ; loss= 0\n",
+      "Iter 1680: acc = 0.78701, nmi = 0.75607, ari = 0.67870  ; loss= 0\n",
+      "Iter 1820: acc = 0.78756, nmi = 0.75760, ari = 0.67940  ; loss= 0\n",
+      "Iter 1960: acc = 0.78813, nmi = 0.75823, ari = 0.68092  ; loss= 0\n",
+      "Iter 2100: acc = 0.78831, nmi = 0.75846, ari = 0.68053  ; loss= 0\n",
+      "Iter 2240: acc = 0.78824, nmi = 0.75930, ari = 0.68047  ; loss= 0\n",
+      "Iter 2380: acc = 0.78879, nmi = 0.76018, ari = 0.68158  ; loss= 0\n",
+      "Iter 2520: acc = 0.78944, nmi = 0.76130, ari = 0.68251  ; loss= 0\n",
+      "Iter 2660: acc = 0.78990, nmi = 0.76235, ari = 0.68298  ; loss= 0\n",
+      "Iter 2800: acc = 0.78940, nmi = 0.76183, ari = 0.68216  ; loss= 0\n",
+      "Iter 2940: acc = 0.79001, nmi = 0.76245, ari = 0.68349  ; loss= 0\n",
+      "Iter 3080: acc = 0.78943, nmi = 0.76167, ari = 0.68229  ; loss= 0\n",
+      "Iter 3220: acc = 0.78887, nmi = 0.76165, ari = 0.68182  ; loss= 0\n",
+      "Iter 3360: acc = 0.78937, nmi = 0.76246, ari = 0.68273  ; loss= 0\n",
+      "Iter 3500: acc = 0.78947, nmi = 0.76335, ari = 0.68329  ; loss= 0\n",
+      "Iter 3640: acc = 0.78930, nmi = 0.76307, ari = 0.68287  ; loss= 0\n",
+      "Iter 3780: acc = 0.78786, nmi = 0.76180, ari = 0.68043  ; loss= 0\n",
+      "Iter 3920: acc = 0.78866, nmi = 0.76267, ari = 0.68203  ; loss= 0\n",
+      "Iter 4060: acc = 0.78916, nmi = 0.76333, ari = 0.68249  ; loss= 0\n",
+      "Iter 4200: acc = 0.78979, nmi = 0.76396, ari = 0.68368  ; loss= 0\n",
+      "Iter 4340: acc = 0.78951, nmi = 0.76322, ari = 0.68276  ; loss= 0\n",
+      "Iter 4480: acc = 0.79037, nmi = 0.76431, ari = 0.68414  ; loss= 0\n",
+      "Iter 4620: acc = 0.79044, nmi = 0.76447, ari = 0.68414  ; loss= 0\n",
+      "Iter 4760: acc = 0.79070, nmi = 0.76480, ari = 0.68486  ; loss= 0\n",
+      "Iter 4900: acc = 0.79071, nmi = 0.76494, ari = 0.68498  ; loss= 0\n",
+      "Iter 5040: acc = 0.79076, nmi = 0.76512, ari = 0.68490  ; loss= 0\n",
+      "Iter 5180: acc = 0.79023, nmi = 0.76447, ari = 0.68390  ; loss= 0\n",
+      "Iter 5320: acc = 0.79014, nmi = 0.76398, ari = 0.68379  ; loss= 0\n",
+      "Iter 5460: acc = 0.78973, nmi = 0.76344, ari = 0.68283  ; loss= 0\n",
+      "Iter 5600: acc = 0.78967, nmi = 0.76344, ari = 0.68297  ; loss= 0\n",
+      "Iter 5740: acc = 0.78976, nmi = 0.76336, ari = 0.68286  ; loss= 0\n",
+      "Iter 5880: acc = 0.78961, nmi = 0.76320, ari = 0.68271  ; loss= 0\n",
+      "Iter 6020: acc = 0.78981, nmi = 0.76337, ari = 0.68294  ; loss= 0\n",
+      "Iter 6160: acc = 0.79031, nmi = 0.76403, ari = 0.68377  ; loss= 0\n",
+      "Iter 6300: acc = 0.79051, nmi = 0.76445, ari = 0.68417  ; loss= 0\n",
+      "Iter 6440: acc = 0.79010, nmi = 0.76376, ari = 0.68345  ; loss= 0\n",
+      "Iter 6580: acc = 0.78991, nmi = 0.76416, ari = 0.68363  ; loss= 0\n",
+      "Iter 6720: acc = 0.78970, nmi = 0.76410, ari = 0.68335  ; loss= 0\n",
+      "Iter 6860: acc = 0.79003, nmi = 0.76458, ari = 0.68398  ; loss= 0\n",
+      "Iter 7000: acc = 0.78936, nmi = 0.76395, ari = 0.68287  ; loss= 0\n",
+      "Iter 7140: acc = 0.78981, nmi = 0.76441, ari = 0.68376  ; loss= 0\n",
+      "Iter 7280: acc = 0.78963, nmi = 0.76465, ari = 0.68353  ; loss= 0\n",
+      "Iter 7420: acc = 0.78979, nmi = 0.76458, ari = 0.68366  ; loss= 0\n",
+      "Iter 7560: acc = 0.79011, nmi = 0.76468, ari = 0.68389  ; loss= 0\n",
+      "Iter 7700: acc = 0.79031, nmi = 0.76498, ari = 0.68422  ; loss= 0\n",
+      "Iter 7840: acc = 0.79031, nmi = 0.76486, ari = 0.68415  ; loss= 0\n",
+      "Iter 7980: acc = 0.79061, nmi = 0.76511, ari = 0.68458  ; loss= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for ite in range(int(maxiter)):\n",
+    "    if ite % update_interval == 0:\n",
+    "        q = model.predict(x, verbose=0)\n",
+    "        p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "        # evaluate the clustering performance\n",
+    "        y_pred = q.argmax(1)\n",
+    "        if y is not None:\n",
+    "            acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "            nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "            ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "            loss = np.round(loss, 5)\n",
+    "            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)\n",
+    "\n",
+    "        # check stop criterion\n",
+    "        delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]\n",
+    "        y_pred_last = np.copy(y_pred)\n",
+    "        if ite > 0 and delta_label < tol:\n",
+    "            print('delta_label ', delta_label, '< tol ', tol)\n",
+    "            print('Reached tolerance threshold. Stopping training.')\n",
+    "            break\n",
+    "    idx = index_array[index * batch_size: min((index+1) * batch_size, x.shape[0])]\n",
+    "    model.train_on_batch(x=x[idx], y=p[idx])\n",
+    "    index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0\n",
+    "\n",
+    "model.save_weights(save_dir + '/conv_DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the clustering model trained weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_weights(save_dir + '/conv_DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Acc = 0.79063, nmi = 0.76515, ari = 0.68468  ; loss= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Eval.\n",
+    "q = model.predict(x, verbose=0)\n",
+    "p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "# evaluate the clustering performance\n",
+    "y_pred = q.argmax(1)\n",
+    "if y is not None:\n",
+    "    acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "    nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "    ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "    loss = np.round(loss, 5)\n",
+    "    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc, nmi, ari), ' ; loss=', loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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iAMqWLcs777xjtL1jx45YW1sTFRXFiBEj4tzn+uzZM0aMGEFUVBTW1tZ06dLF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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1cc00b14780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import sklearn.metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set(font_scale=3)\n",
+    "confusion_matrix = sklearn.metrics.confusion_matrix(y, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(16, 14))\n",
+    "sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", annot_kws={\"size\": 20});\n",
+    "plt.title(\"Confusion matrix\", fontsize=30)\n",
+    "plt.ylabel('True label', fontsize=25)\n",
+    "plt.xlabel('Clustering label', fontsize=25)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model to train clustering and autoencoder at same time(Convolutional)\n",
+    "Multiple outputs model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder, encoder = autoencoderConv2D_1()\n",
+    "autoencoder.load_weights(save_dir+'/conv_ae_weights.h5')\n",
+    "clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)\n",
+    "model = Model(inputs=encoder.input,\n",
+    "                           outputs=[clustering_layer, autoencoder.output])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from keras.utils import plot_model\n",
+    "plot_model(model, to_file='model.png', show_shapes=True)\n",
+    "from IPython.display import Image\n",
+    "Image(filename='model.png') \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize cluster centers using k-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters=n_clusters, n_init=20)\n",
+    "y_pred = kmeans.fit_predict(encoder.predict(x))\n",
+    "model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])\n",
+    "y_pred_last = np.copy(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(loss=['kld', 'mse'], loss_weights=[0.1, 1], optimizer='adam')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 0: acc = 0.72437, nmi = 0.63676, ari = 0.56020  ; loss= 0\n",
+      "Iter 140: acc = 0.74751, nmi = 0.67206, ari = 0.60064  ; loss= 0\n",
+      "Iter 280: acc = 0.76076, nmi = 0.69533, ari = 0.62546  ; loss= 0\n",
+      "Iter 420: acc = 0.77276, nmi = 0.71713, ari = 0.64724  ; loss= 0\n",
+      "Iter 560: acc = 0.78280, nmi = 0.73480, ari = 0.66497  ; loss= 0\n",
+      "Iter 700: acc = 0.78761, nmi = 0.74414, ari = 0.67341  ; loss= 0\n",
+      "Iter 840: acc = 0.79120, nmi = 0.75001, ari = 0.67983  ; loss= 0\n",
+      "Iter 980: acc = 0.79526, nmi = 0.75672, ari = 0.68676  ; loss= 0\n",
+      "Iter 1120: acc = 0.79941, nmi = 0.76276, ari = 0.69397  ; loss= 0\n",
+      "Iter 1260: acc = 0.80180, nmi = 0.76759, ari = 0.69808  ; loss= 0\n",
+      "Iter 1400: acc = 0.80424, nmi = 0.77263, ari = 0.70262  ; loss= 0\n",
+      "Iter 1540: acc = 0.80597, nmi = 0.77566, ari = 0.70563  ; loss= 0\n",
+      "Iter 1680: acc = 0.80723, nmi = 0.77782, ari = 0.70774  ; loss= 0\n",
+      "Iter 1820: acc = 0.80863, nmi = 0.78037, ari = 0.71017  ; loss= 0\n",
+      "Iter 1960: acc = 0.80977, nmi = 0.78191, ari = 0.71249  ; loss= 0\n",
+      "Iter 2100: acc = 0.81104, nmi = 0.78371, ari = 0.71471  ; loss= 0\n",
+      "Iter 2240: acc = 0.81157, nmi = 0.78459, ari = 0.71579  ; loss= 0\n",
+      "Iter 2380: acc = 0.81241, nmi = 0.78609, ari = 0.71765  ; loss= 0\n",
+      "Iter 2520: acc = 0.81361, nmi = 0.78724, ari = 0.71930  ; loss= 0\n",
+      "Iter 2660: acc = 0.81409, nmi = 0.78885, ari = 0.72032  ; loss= 0\n",
+      "Iter 2800: acc = 0.81486, nmi = 0.78957, ari = 0.72186  ; loss= 0\n",
+      "Iter 2940: acc = 0.81590, nmi = 0.79066, ari = 0.72352  ; loss= 0\n",
+      "Iter 3080: acc = 0.81679, nmi = 0.79171, ari = 0.72483  ; loss= 0\n",
+      "Iter 3220: acc = 0.81680, nmi = 0.79231, ari = 0.72488  ; loss= 0\n",
+      "Iter 3360: acc = 0.81829, nmi = 0.79439, ari = 0.72710  ; loss= 0\n",
+      "Iter 3500: acc = 0.81869, nmi = 0.79502, ari = 0.72787  ; loss= 0\n",
+      "Iter 3640: acc = 0.81974, nmi = 0.79573, ari = 0.72945  ; loss= 0\n",
+      "Iter 3780: acc = 0.81914, nmi = 0.79562, ari = 0.72856  ; loss= 0\n",
+      "Iter 3920: acc = 0.81980, nmi = 0.79603, ari = 0.72947  ; loss= 0\n",
+      "Iter 4060: acc = 0.81949, nmi = 0.79611, ari = 0.72918  ; loss= 0\n",
+      "Iter 4200: acc = 0.82006, nmi = 0.79689, ari = 0.73023  ; loss= 0\n",
+      "Iter 4340: acc = 0.82016, nmi = 0.79723, ari = 0.73047  ; loss= 0\n",
+      "Iter 4480: acc = 0.82019, nmi = 0.79791, ari = 0.73069  ; loss= 0\n",
+      "Iter 4620: acc = 0.82016, nmi = 0.79794, ari = 0.73081  ; loss= 0\n",
+      "Iter 4760: acc = 0.82093, nmi = 0.79901, ari = 0.73212  ; loss= 0\n",
+      "Iter 4900: acc = 0.82094, nmi = 0.79916, ari = 0.73235  ; loss= 0\n",
+      "Iter 5040: acc = 0.82079, nmi = 0.79891, ari = 0.73184  ; loss= 0\n",
+      "Iter 5180: acc = 0.82073, nmi = 0.79868, ari = 0.73155  ; loss= 0\n",
+      "Iter 5320: acc = 0.82116, nmi = 0.79934, ari = 0.73256  ; loss= 0\n",
+      "Iter 5460: acc = 0.82094, nmi = 0.79920, ari = 0.73205  ; loss= 0\n",
+      "Iter 5600: acc = 0.82113, nmi = 0.79909, ari = 0.73231  ; loss= 0\n",
+      "Iter 5740: acc = 0.82157, nmi = 0.79955, ari = 0.73298  ; loss= 0\n",
+      "Iter 5880: acc = 0.82123, nmi = 0.79932, ari = 0.73263  ; loss= 0\n",
+      "Iter 6020: acc = 0.82110, nmi = 0.79963, ari = 0.73261  ; loss= 0\n",
+      "Iter 6160: acc = 0.82124, nmi = 0.80020, ari = 0.73299  ; loss= 0\n",
+      "Iter 6300: acc = 0.82130, nmi = 0.80037, ari = 0.73328  ; loss= 0\n",
+      "Iter 6440: acc = 0.82120, nmi = 0.80034, ari = 0.73303  ; loss= 0\n",
+      "Iter 6580: acc = 0.82207, nmi = 0.80109, ari = 0.73443  ; loss= 0\n",
+      "Iter 6720: acc = 0.82187, nmi = 0.80115, ari = 0.73432  ; loss= 0\n",
+      "Iter 6860: acc = 0.82234, nmi = 0.80147, ari = 0.73476  ; loss= 0\n",
+      "Iter 7000: acc = 0.82213, nmi = 0.80153, ari = 0.73466  ; loss= 0\n",
+      "Iter 7140: acc = 0.82211, nmi = 0.80137, ari = 0.73445  ; loss= 0\n",
+      "Iter 7280: acc = 0.82213, nmi = 0.80160, ari = 0.73478  ; loss= 0\n",
+      "Iter 7420: acc = 0.82254, nmi = 0.80243, ari = 0.73553  ; loss= 0\n",
+      "Iter 7560: acc = 0.82229, nmi = 0.80196, ari = 0.73506  ; loss= 0\n",
+      "Iter 7700: acc = 0.82241, nmi = 0.80227, ari = 0.73524  ; loss= 0\n",
+      "Iter 7840: acc = 0.82254, nmi = 0.80242, ari = 0.73549  ; loss= 0\n",
+      "Iter 7980: acc = 0.82210, nmi = 0.80193, ari = 0.73467  ; loss= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for ite in range(int(maxiter)):\n",
+    "    if ite % update_interval == 0:\n",
+    "        q, _  = model.predict(x, verbose=0)\n",
+    "        p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "        # evaluate the clustering performance\n",
+    "        y_pred = q.argmax(1)\n",
+    "        if y is not None:\n",
+    "            acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "            nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "            ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "            loss = np.round(loss, 5)\n",
+    "            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)\n",
+    "\n",
+    "        # check stop criterion\n",
+    "        delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]\n",
+    "        y_pred_last = np.copy(y_pred)\n",
+    "        if ite > 0 and delta_label < tol:\n",
+    "            print('delta_label ', delta_label, '< tol ', tol)\n",
+    "            print('Reached tolerance threshold. Stopping training.')\n",
+    "            break\n",
+    "    idx = index_array[index * batch_size: min((index+1) * batch_size, x.shape[0])]\n",
+    "    model.train_on_batch(x=x[idx], y=[p[idx], x[idx]])\n",
+    "    index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0\n",
+    "\n",
+    "model.save_weights(save_dir + '/conv_b_DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the clustering model trained weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_weights(save_dir + '/conv_b_DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Acc = 0.82233, nmi = 0.80238, ari = 0.73511  ; loss= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Eval.\n",
+    "q, _ = model.predict(x, verbose=0)\n",
+    "p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "# evaluate the clustering performance\n",
+    "y_pred = q.argmax(1)\n",
+    "if y is not None:\n",
+    "    acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "    nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "    ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "    loss = np.round(loss, 5)\n",
+    "    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc, nmi, ari), ' ; loss=', loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FkNJKakcjg4ODUzxW+fLlKV++PEOHDiUwMJCDBw+ye/duNm3aRHBwMBcuXGDGjBmGW+ok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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a58a214588>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import sklearn.metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set(font_scale=3)\n",
+    "confusion_matrix = sklearn.metrics.confusion_matrix(y, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(16, 14))\n",
+    "sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", annot_kws={\"size\": 20});\n",
+    "plt.title(\"Confusion matrix\", fontsize=30)\n",
+    "plt.ylabel('True label', fontsize=25)\n",
+    "plt.xlabel('Clustering label', fontsize=25)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model to train clustering and autoencoder at same time(Fully connected)\n",
+    "Multiple outputs model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(70000, 784)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
+    "\n",
+    "x = np.concatenate((x_train, x_test))\n",
+    "y = np.concatenate((y_train, y_test))\n",
+    "x = x.reshape((x.shape[0], -1))\n",
+    "x = np.divide(x, 255.)\n",
+    "n_clusters = len(np.unique(y))\n",
+    "x.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dims = [x.shape[-1], 500, 500, 2000, 10]\n",
+    "init = VarianceScaling(scale=1. / 3., mode='fan_in',\n",
+    "                           distribution='uniform')\n",
+    "pretrain_optimizer = SGD(lr=1, momentum=0.9)\n",
+    "pretrain_epochs = 300\n",
+    "batch_size = 256\n",
+    "save_dir = './results'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "def autoencoder(dims, act='relu', init='glorot_uniform'):\n",
+    "    \"\"\"\n",
+    "    Fully connected auto-encoder model, symmetric.\n",
+    "    Arguments:\n",
+    "        dims: list of number of units in each layer of encoder. dims[0] is input dim, dims[-1] is units in hidden layer.\n",
+    "            The decoder is symmetric with encoder. So number of layers of the auto-encoder is 2*len(dims)-1\n",
+    "        act: activation, not applied to Input, Hidden and Output layers\n",
+    "    return:\n",
+    "        (ae_model, encoder_model), Model of autoencoder and model of encoder\n",
+    "    \"\"\"\n",
+    "    n_stacks = len(dims) - 1\n",
+    "    # input\n",
+    "    input_img = Input(shape=(dims[0],), name='input')\n",
+    "    x = input_img\n",
+    "    # internal layers in encoder\n",
+    "    for i in range(n_stacks-1):\n",
+    "        x = Dense(dims[i + 1], activation=act, kernel_initializer=init, name='encoder_%d' % i)(x)\n",
+    "\n",
+    "    # hidden layer\n",
+    "    encoded = Dense(dims[-1], kernel_initializer=init, name='encoder_%d' % (n_stacks - 1))(x)  # hidden layer, features are extracted from here\n",
+    "\n",
+    "    x = encoded\n",
+    "    # internal layers in decoder\n",
+    "    for i in range(n_stacks-1, 0, -1):\n",
+    "        x = Dense(dims[i], activation=act, kernel_initializer=init, name='decoder_%d' % i)(x)\n",
+    "\n",
+    "    # output\n",
+    "    x = Dense(dims[0], kernel_initializer=init, name='decoder_0')(x)\n",
+    "    decoded = x\n",
+    "    return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "autoencoder, encoder = autoencoder(dims, init=init)\n",
+    "autoencoder.load_weights(save_dir+'/ae_weights.h5')\n",
+    "clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)\n",
+    "model = Model(inputs=encoder.input,\n",
+    "            outputs=[clustering_layer, autoencoder.output])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<IPython.core.display.Image object>"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from keras.utils import plot_model\n",
+    "plot_model(model, to_file='model.png', show_shapes=True)\n",
+    "from IPython.display import Image\n",
+    "Image(filename='model.png') "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initialize cluster centers using k-means"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kmeans = KMeans(n_clusters=n_clusters, n_init=20)\n",
+    "y_pred = kmeans.fit_predict(encoder.predict(x))\n",
+    "model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])\n",
+    "y_pred_last = np.copy(y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(loss=['kld', 'mse'], loss_weights=[0.1, 1], optimizer=pretrain_optimizer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iter 0: acc = 0.91821, nmi = 0.82700, ari = 0.82504  ; loss= 0\n",
+      "Iter 140: acc = 0.93051, nmi = 0.84571, ari = 0.85230  ; loss= 0\n",
+      "Iter 280: acc = 0.94000, nmi = 0.86251, ari = 0.87220  ; loss= 0\n",
+      "Iter 420: acc = 0.94554, nmi = 0.87300, ari = 0.88384  ; loss= 0\n",
+      "Iter 560: acc = 0.94901, nmi = 0.88001, ari = 0.89100  ; loss= 0\n",
+      "Iter 700: acc = 0.95294, nmi = 0.88807, ari = 0.89914  ; loss= 0\n",
+      "Iter 840: acc = 0.95473, nmi = 0.89185, ari = 0.90284  ; loss= 0\n",
+      "Iter 980: acc = 0.95473, nmi = 0.89277, ari = 0.90288  ; loss= 0\n",
+      "Iter 1120: acc = 0.95544, nmi = 0.89402, ari = 0.90433  ; loss= 0\n",
+      "Iter 1260: acc = 0.95584, nmi = 0.89521, ari = 0.90506  ; loss= 0\n",
+      "Iter 1400: acc = 0.95530, nmi = 0.89427, ari = 0.90393  ; loss= 0\n",
+      "Iter 1540: acc = 0.95600, nmi = 0.89517, ari = 0.90540  ; loss= 0\n",
+      "Iter 1680: acc = 0.95673, nmi = 0.89655, ari = 0.90694  ; loss= 0\n",
+      "Iter 1820: acc = 0.95629, nmi = 0.89580, ari = 0.90603  ; loss= 0\n",
+      "Iter 1960: acc = 0.95671, nmi = 0.89661, ari = 0.90694  ; loss= 0\n",
+      "Iter 2100: acc = 0.95661, nmi = 0.89648, ari = 0.90673  ; loss= 0\n",
+      "Iter 2240: acc = 0.95644, nmi = 0.89644, ari = 0.90634  ; loss= 0\n",
+      "Iter 2380: acc = 0.95683, nmi = 0.89708, ari = 0.90714  ; loss= 0\n",
+      "Iter 2520: acc = 0.95670, nmi = 0.89725, ari = 0.90675  ; loss= 0\n",
+      "Iter 2660: acc = 0.95647, nmi = 0.89699, ari = 0.90623  ; loss= 0\n",
+      "Iter 2800: acc = 0.95707, nmi = 0.89787, ari = 0.90753  ; loss= 0\n",
+      "Iter 2940: acc = 0.95660, nmi = 0.89690, ari = 0.90656  ; loss= 0\n",
+      "Iter 3080: acc = 0.95574, nmi = 0.89544, ari = 0.90471  ; loss= 0\n",
+      "Iter 3220: acc = 0.95606, nmi = 0.89603, ari = 0.90536  ; loss= 0\n",
+      "Iter 3360: acc = 0.95556, nmi = 0.89521, ari = 0.90429  ; loss= 0\n",
+      "Iter 3500: acc = 0.95627, nmi = 0.89635, ari = 0.90578  ; loss= 0\n",
+      "Iter 3640: acc = 0.95690, nmi = 0.89756, ari = 0.90718  ; loss= 0\n",
+      "Iter 3780: acc = 0.95591, nmi = 0.89511, ari = 0.90511  ; loss= 0\n",
+      "Iter 3920: acc = 0.95653, nmi = 0.89638, ari = 0.90642  ; loss= 0\n",
+      "Iter 4060: acc = 0.95599, nmi = 0.89579, ari = 0.90520  ; loss= 0\n",
+      "Iter 4200: acc = 0.95650, nmi = 0.89671, ari = 0.90626  ; loss= 0\n",
+      "Iter 4340: acc = 0.95691, nmi = 0.89725, ari = 0.90723  ; loss= 0\n",
+      "Iter 4480: acc = 0.95740, nmi = 0.89814, ari = 0.90828  ; loss= 0\n",
+      "Iter 4620: acc = 0.95673, nmi = 0.89700, ari = 0.90687  ; loss= 0\n",
+      "Iter 4760: acc = 0.95711, nmi = 0.89787, ari = 0.90768  ; loss= 0\n",
+      "Iter 4900: acc = 0.95711, nmi = 0.89785, ari = 0.90767  ; loss= 0\n",
+      "Iter 5040: acc = 0.95701, nmi = 0.89775, ari = 0.90743  ; loss= 0\n",
+      "Iter 5180: acc = 0.95649, nmi = 0.89697, ari = 0.90627  ; loss= 0\n",
+      "Iter 5320: acc = 0.95600, nmi = 0.89639, ari = 0.90518  ; loss= 0\n",
+      "Iter 5460: acc = 0.95534, nmi = 0.89538, ari = 0.90380  ; loss= 0\n",
+      "Iter 5600: acc = 0.95527, nmi = 0.89538, ari = 0.90364  ; loss= 0\n",
+      "Iter 5740: acc = 0.95313, nmi = 0.89300, ari = 0.89909  ; loss= 0\n",
+      "Iter 5880: acc = 0.95417, nmi = 0.89405, ari = 0.90130  ; loss= 0\n",
+      "Iter 6020: acc = 0.95491, nmi = 0.89497, ari = 0.90289  ; loss= 0\n",
+      "Iter 6160: acc = 0.95521, nmi = 0.89539, ari = 0.90353  ; loss= 0\n",
+      "Iter 6300: acc = 0.95531, nmi = 0.89548, ari = 0.90373  ; loss= 0\n",
+      "Iter 6440: acc = 0.95503, nmi = 0.89508, ari = 0.90317  ; loss= 0\n",
+      "Iter 6580: acc = 0.95573, nmi = 0.89601, ari = 0.90466  ; loss= 0\n",
+      "Iter 6720: acc = 0.95587, nmi = 0.89625, ari = 0.90499  ; loss= 0\n",
+      "delta_label  0.0008 < tol  0.001\n",
+      "Reached tolerance threshold. Stopping training.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for ite in range(int(maxiter)):\n",
+    "    if ite % update_interval == 0:\n",
+    "        q, _  = model.predict(x, verbose=0)\n",
+    "        p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "        # evaluate the clustering performance\n",
+    "        y_pred = q.argmax(1)\n",
+    "        if y is not None:\n",
+    "            acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "            nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "            ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "            loss = np.round(loss, 5)\n",
+    "            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc, nmi, ari), ' ; loss=', loss)\n",
+    "\n",
+    "        # check stop criterion\n",
+    "        delta_label = np.sum(y_pred != y_pred_last).astype(np.float32) / y_pred.shape[0]\n",
+    "        y_pred_last = np.copy(y_pred)\n",
+    "        if ite > 0 and delta_label < tol:\n",
+    "            print('delta_label ', delta_label, '< tol ', tol)\n",
+    "            print('Reached tolerance threshold. Stopping training.')\n",
+    "            break\n",
+    "    idx = index_array[index * batch_size: min((index+1) * batch_size, x.shape[0])]\n",
+    "    model.train_on_batch(x=x[idx], y=[p[idx], x[idx]])\n",
+    "    index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0\n",
+    "\n",
+    "model.save_weights(save_dir + '/b_DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load the clustering model trained weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_weights(save_dir + '/b_DEC_model_final.h5')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Final Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Acc = 0.95587, nmi = 0.89625, ari = 0.90499  ; loss= 0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Eval.\n",
+    "q, _ = model.predict(x, verbose=0)\n",
+    "p = target_distribution(q)  # update the auxiliary target distribution p\n",
+    "\n",
+    "# evaluate the clustering performance\n",
+    "y_pred = q.argmax(1)\n",
+    "if y is not None:\n",
+    "    acc = np.round(metrics.acc(y, y_pred), 5)\n",
+    "    nmi = np.round(metrics.nmi(y, y_pred), 5)\n",
+    "    ari = np.round(metrics.ari(y, y_pred), 5)\n",
+    "    loss = np.round(loss, 5)\n",
+    "    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc, nmi, ari), ' ; loss=', loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ZMuHk5ISjoyMAz58/58mTJ9y6dQs/Pz/OnTtHREQE165do2PHjowYMYLGjRub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\npWpx7ty5KVasGO7u7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BmweH7Ra2kw8EXjtyONgH5YxYn82TOy9/wNngaHmDpEgvr77DWe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c3CHIe6TriG1Si6yIiIiIiIhYFSWyIiIiIiIiYlXUtVhERERERGyWRi22TWqRFRER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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x1a5f50e04a8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import seaborn as sns\n",
+    "import sklearn.metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "sns.set(font_scale=3)\n",
+    "confusion_matrix = sklearn.metrics.confusion_matrix(y, y_pred)\n",
+    "\n",
+    "plt.figure(figsize=(16, 14))\n",
+    "sns.heatmap(confusion_matrix, annot=True, fmt=\"d\", annot_kws={\"size\": 20});\n",
+    "plt.title(\"Confusion matrix\", fontsize=30)\n",
+    "plt.ylabel('True label', fontsize=25)\n",
+    "plt.xlabel('Clustering label', fontsize=25)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.5.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..6e45399
--- /dev/null
+++ b/README.md
@@ -0,0 +1,25 @@
+# [How to do Unsupervised Clustering with Keras](https://www.dlology.com/blog/how-to-do-unsupervised-clustering-with-keras/) | DLology Blog
+
+
+## How to Run
+Require [Python 3.5+](https://www.python.org/ftp/python/3.6.4/python-3.6.4.exe) and [Jupyter notebook](https://jupyter.readthedocs.io/en/latest/install.html) installed
+### Clone or download this repo
+```
+git clone https://github.com/Tony607/Keras_Deep_Clustering
+```
+### Install required libraries
+`pip3 install -r requirements.txt`
+
+
+In the project start a command line run
+```
+jupyter notebook
+```
+In the opened browser window open
+```
+Keras-DEC.ipynb
+```
+If you want to skip the training, you can try the pre-trained weights from the releases, [results.zip](https://github.com/Tony607/Keras_Deep_Clustering/releases/download/V0.1/results.zip). Extract 
+`results` folders to the root of the project directory.
+
+Happy coding! Leave a comment if you have any question.
diff --git a/metrics.py b/metrics.py
new file mode 100644
index 0000000..3ae3a35
--- /dev/null
+++ b/metrics.py
@@ -0,0 +1,27 @@
+import numpy as np
+from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
+
+nmi = normalized_mutual_info_score
+ari = adjusted_rand_score
+
+
+def acc(y_true, y_pred):
+    """
+    Calculate clustering accuracy. Require scikit-learn installed
+
+    # Arguments
+        y: true labels, numpy.array with shape `(n_samples,)`
+        y_pred: predicted labels, numpy.array with shape `(n_samples,)`
+
+    # Return
+        accuracy, in [0,1]
+    """
+    y_true = y_true.astype(np.int64)
+    assert y_pred.size == y_true.size
+    D = max(y_pred.max(), y_true.max()) + 1
+    w = np.zeros((D, D), dtype=np.int64)
+    for i in range(y_pred.size):
+        w[y_pred[i], y_true[i]] += 1
+    from sklearn.utils.linear_assignment_ import linear_assignment
+    ind = linear_assignment(w.max() - w)
+    return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..d6acbd5
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,7 @@
+numpy
+tensorflow
+keras
+scipy
+matplotlib
+sklearn
+seaborn
\ No newline at end of file