autoencoder/auto_encoder_experiment_with_keras_with_clustering_DEC.ipynb

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  "metadata": {
    "colab": {
      "name": "auto encoder experiment with keras with clustering DEC ",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/manishiitg/ML_Experiments/blob/master/autoencoder/auto_encoder_experiment_with_keras_with_clustering_DEC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3rXWpt4ySOd",
        "colab_type": "text"
      },
      "source": [
        "# Playing around with auto-encoder networks in keras and also clustering the output. Compressing MNIST dataset to a 10D vector space. \n",
        "\n",
        "Auto encoders are networks in which we encode data to a lower dimension space by training a NN to first reduce data to a lower dim vector space and then again generating the same. This is an unsupervised learning approach in which the network compresses the data and then again learns to uncompress to produce the same orignal data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T-Fpf_OVdcCA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.layers import Input, Dense\n",
        "from keras.models import Model\n",
        "from keras.initializers import VarianceScaling\n",
        "from keras.optimizers import SGD\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline  \n",
        "\n",
        "encoding_dim = 10  # since we want to cluster and there like 10 letters \n",
        "\n",
        "\n",
        "def autoencoder(act = \"relu\"):\n",
        "  init = VarianceScaling(scale=1. / 3., mode='fan_in',\n",
        "                            distribution='uniform')\n",
        "\n",
        "  # this is the size of our encoded representations\n",
        "\n",
        "  # this is our input placeholder\n",
        "  input_img = Input(shape=(784,))\n",
        "  x = input_img \n",
        "  x = Dense(500, activation=act, kernel_initializer=init, name='encoder_1')(x)\n",
        "  x = Dense(500, activation=act, kernel_initializer=init, name='encoder_2')(x)\n",
        "  x = Dense(2000, activation=act, kernel_initializer=init, name='encoder_3')(x)\n",
        "\n",
        "                                                                                        \n",
        "  # \"encoded\" is the encoded representation of the input\n",
        "  encoded = Dense(encoding_dim, activation='relu')(x)\n",
        "\n",
        "  x = Dense(2000, activation=act, kernel_initializer=init, name='decorder_1')(encoded)\n",
        "  x = Dense(500, activation=act, kernel_initializer=init, name='decorder_2')(x)\n",
        "  x = Dense(500, activation=act, kernel_initializer=init, name='decorder_3')(x)\n",
        "\n",
        "  x = Dense(784, kernel_initializer=init, name='decoder_0' )(x)\n",
        "  decoded = x\n",
        "\n",
        "  return Model(inputs=input_img, outputs=decoded, name='AE'), Model(inputs=input_img, outputs=encoded, name='encoder')\n",
        "\n",
        "autoencoder, encoder = autoencoder()\n",
        "autoencoder.compile(optimizer=SGD(lr=1, momentum=0.9), loss='mse')\n",
        "# autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yGACz5xCz9Pj",
        "colab_type": "text"
      },
      "source": [
        "The above network has 3 layers to encode data to 10D space and from that decode data generate the same image. \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Lob7FM3extU",
        "colab_type": "code",
        "outputId": "a79e098e-6082-404b-caf9-b080e4f3cedd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 976
        }
      },
      "source": [
        "from keras.utils import plot_model\n",
        "plot_model(autoencoder, to_file='model.png', show_shapes=True)\n",
        "from IPython.display import Image\n",
        "Image(filename='model.png')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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aaa86503Nnj0bs2fP1tv2TEXj+V1Z5zZjxgyVtg77Q64xa47v378fiYmJyMzMVCmT2hEY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\nrkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l0_ErUa61fG9",
        "colab_type": "text"
      },
      "source": [
        "Downloading the MNIST dataset. the training data has 60,000 single channel images of size 28x28"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5WrNKKBvkw3",
        "colab_type": "code",
        "outputId": "a0547e37-9a6e-42b2-dbfc-e53fe59f5488",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from keras.datasets import mnist\n",
        "import numpy as np\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "print(x_train.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 28, 28)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nf8MAqoSvtwi",
        "colab_type": "code",
        "outputId": "481ab020-2543-4be8-cc22-728515b28d70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "source": [
        "x_train = x_train.astype('float32') / 255.\n",
        "x_test = x_test.astype('float32') / 255.\n",
        "x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))\n",
        "x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))\n",
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "\n",
        "y = np.concatenate((y_train, y_test))\n",
        "n_clusters = len(np.unique(y))\n",
        "\n",
        "x = np.concatenate((x_train, x_test))\n",
        "\n",
        "\n",
        "x_plt = x[:10]\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(x_plt[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(60000, 784)\n",
            "(10000, 784)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_qxnmvfy1tdj",
        "colab_type": "text"
      },
      "source": [
        "Plotting dataset. Divide the pixels by 255 to convert the data between 0 and 1. \n",
        "\n",
        "Next convering the data to size (60000, 28x28) \n",
        "\n",
        "To plot the data we reshape it again to 28 x 28 size to plot it "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K22QfUHAR0yo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os.path\n",
        "if os.path.isfile('ae_weights.h5'):\n",
        "  autoencoder.load_weights('ae_weights.h5')\n",
        "else:\n",
        "  autoencoder.fit(x, x,epochs=300,batch_size=256) # epochs = 30\n",
        "  autoencoder.save_weights('ae_weights.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wg8KFhYa66ZK",
        "colab_type": "text"
      },
      "source": [
        "**Training the network takes too long for 300 epoches, so stopping it in between. **"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S-HPJw8n23Fr",
        "colab_type": "text"
      },
      "source": [
        "If the saved the network weights existing, load it onto the model. Else we can train the network. \n",
        "\n",
        "Next we take the first 10 images from our dataset and pass it through the trained network to see the results. \n",
        "\n",
        "This way we can compare the output of our autoencoder with the input images and plot them.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wHzVkrC8w5dn",
        "colab_type": "code",
        "outputId": "5ec77114-0d12-4935-bf37-49d5c4df2ff2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 264
        }
      },
      "source": [
        "decoded_imgs = autoencoder.predict(x_test[:10])\n",
        "\n",
        "\n",
        "n = 10\n",
        "plt.figure(figsize=(20, 4))\n",
        "for i in range(n):\n",
        "    # display original\n",
        "    ax = plt.subplot(2, n, i + 1)\n",
        "    plt.imshow(x_test[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "\n",
        "    # display reconstruction\n",
        "    ax = plt.subplot(2, n, i + 1 + n)\n",
        "    plt.imshow(decoded_imgs[i].reshape(28, 28))\n",
        "    plt.gray()\n",
        "    ax.get_xaxis().set_visible(False)\n",
        "    ax.get_yaxis().set_visible(False)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 1440x288 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vEKDzZ67CaN",
        "colab_type": "text"
      },
      "source": [
        "The network has learned to auto-encode the data very nicely with 30 epoches itself. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lama_E6EpbyD",
        "colab_type": "code",
        "outputId": "a1e833b9-8d2e-46d9-e7b3-a05e06022845",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x.shape[-1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "784"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lifLLRQxubYm",
        "colab_type": "code",
        "outputId": "b2e682c1-f6da-4d66-c6fb-cdc376cd3cfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "n_clusters"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LvWdRdmo7N2e",
        "colab_type": "text"
      },
      "source": [
        "# Clustering\n",
        "\n",
        "Next we will try to cluster that data or cluster the images. Currently we know that we have 10 digits so we should be able to cluster data into 10 unique zones. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ko2p1pIvEcoY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score\n",
        "\n",
        "nmi = normalized_mutual_info_score\n",
        "ari = adjusted_rand_score\n",
        "\n",
        "\n",
        "def acc(y_true, y_pred):\n",
        "    \"\"\"\n",
        "    Calculate clustering accuracy. Require scikit-learn installed\n",
        "    # Arguments\n",
        "        y: true labels, numpy.array with shape `(n_samples,)`\n",
        "        y_pred: predicted labels, numpy.array with shape `(n_samples,)`\n",
        "    # Return\n",
        "        accuracy, in [0,1]\n",
        "    \"\"\"\n",
        "    y_true = y_true.astype(np.int64)\n",
        "    assert y_pred.size == y_true.size\n",
        "    D = max(y_pred.max(), y_true.max()) + 1\n",
        "    w = np.zeros((D, D), dtype=np.int64)\n",
        "    for i in range(y_pred.size):\n",
        "        w[y_pred[i], y_true[i]] += 1\n",
        "    from sklearn.utils.linear_assignment_ import linear_assignment\n",
        "    ind = linear_assignment(w.max() - w)\n",
        "    return sum([w[i, j] for i, j in ind]) * 1.0 / y_pred.size"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Yis9EIB7lQq",
        "colab_type": "text"
      },
      "source": [
        "This is a measure of clusting accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVXSldHGBzbS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "decoded_imgs_x = encoder.predict(x)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cnNkEA9y8_GT",
        "colab_type": "code",
        "outputId": "6690e042-a82a-41a4-f457-4eda6765d2b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(decoded_imgs_x.shape)\n",
        "print(decoded_imgs_x[0].shape)\n",
        "# input size"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(70000, 10)\n",
            "(10,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aVw7j-8BCaxk",
        "colab_type": "code",
        "outputId": "be7c68c8-1ab5-4020-a0ba-f7225ebbf001",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)\n",
        "y_pred_kmeans = kmeans.fit_predict(x)\n",
        "acc(y, y_pred_kmeans)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5348285714285714"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0EtvennCbZE",
        "colab_type": "text"
      },
      "source": [
        "Let's just simply try to cluster the initial data i.e x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e_M3ACWg8gWx",
        "colab_type": "text"
      },
      "source": [
        "Simple k-means cluster gives an accurancy of 53%\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tS0WKKUebDnX",
        "colab_type": "code",
        "outputId": "53805047-1347-4a4b-c883-47f385b50fdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "\n",
        "kmeans = KMeans(n_clusters=n_clusters, n_init=20, n_jobs=4)\n",
        "y_pred_last = kmeans.fit_predict(decoded_imgs_x)\n",
        "acc(y, y_pred_last)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8229142857142857"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sjWdvJQ8CltP",
        "colab_type": "text"
      },
      "source": [
        "After encoding data we get an accuracy of 82% which is much better. **So encoding the data improved clustering by upto 25%**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nrRxwfGy712k",
        "colab_type": "text"
      },
      "source": [
        "Clustering using deep leaning based on https://www.dlology.com/blog/how-to-do-unsupervised-clustering-with-keras/"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gddcqJwhc5TB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.engine.topology import Layer, InputSpec\n",
        "import keras.backend as K\n",
        "\n",
        "\n",
        "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    # no of clusters \n",
        "        self.alpha = alpha \n",
        "        self.initial_weights = weights  #initial weights. this will be set by the k-means distribution \n",
        "        self.input_spec = InputSpec(ndim=2) #input is a 2D vector of (n_samples, n_features)\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(initializer='glorot_uniform', name='clusters', shape=(self.n_clusters, input_dim))\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()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bmnyt0zhdQWc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clustering_layer = ClusteringLayer(n_clusters, name='clustering')(encoder.output)\n",
        "model = Model(inputs=encoder.input, outputs=clustering_layer)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4yPL5Qze3-X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from keras.optimizers import SGD\n",
        "\n",
        "model.compile(optimizer=SGD(0.01, 0.9), loss='kld')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BuRBQR9Ifkf_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.get_layer(name='clustering').set_weights([kmeans.cluster_centers_])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L35GykUQf6BN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# computing an auxiliary target distribution\n",
        "def target_distribution(q):\n",
        "    # print(q)\n",
        "    weight = q ** 2 / q.sum(0)\n",
        "    return (weight.T / weight.sum(1)).T"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPxiWAJAhMvB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "loss = 0\n",
        "index = 0\n",
        "maxiter = 8000\n",
        "update_interval = 140\n",
        "index_array = np.arange(x.shape[0])\n",
        "tol = 0.001\n",
        "batch_size = 256"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZoWQpECVhUR1",
        "colab_type": "code",
        "outputId": "78ed25f0-0c6f-4a2c-dac1-99b8905f01fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "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",
        "            acc1 = np.round(acc(y, y_pred), 5)\n",
        "            nmi1 = np.round(nmi(y, y_pred), 5)\n",
        "            ari1 = np.round(ari(y, y_pred), 5)\n",
        "            loss1 = np.round(loss, 5)\n",
        "            print('Iter %d: acc = %.5f, nmi = %.5f, ari = %.5f' % (ite, acc1, nmi1, ari1), ' ; loss=', loss1)\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",
        "    loss = model.train_on_batch(x=x[idx], y=p[idx])\n",
        "    index = index + 1 if (index + 1) * batch_size <= x.shape[0] else 0"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 0: acc = 0.82291, nmi = 0.76054, ari = 0.71496  ; loss= 0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 140: acc = 0.84446, nmi = 0.80155, ari = 0.76709  ; loss= 0.05902\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 280: acc = 0.85816, nmi = 0.82609, ari = 0.79603  ; loss= 0.14202\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 420: acc = 0.86103, nmi = 0.83518, ari = 0.80528  ; loss= 0.20347\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 560: acc = 0.86517, nmi = 0.84154, ari = 0.81224  ; loss= 0.2158\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 700: acc = 0.86684, nmi = 0.84482, ari = 0.81560  ; loss= 0.21243\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 840: acc = 0.86911, nmi = 0.84705, ari = 0.81753  ; loss= 0.20628\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 980: acc = 0.86989, nmi = 0.84935, ari = 0.81978  ; loss= 0.19952\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1120: acc = 0.87114, nmi = 0.85085, ari = 0.82157  ; loss= 0.19476\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1260: acc = 0.87153, nmi = 0.85186, ari = 0.82229  ; loss= 0.19121\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1400: acc = 0.87224, nmi = 0.85262, ari = 0.82334  ; loss= 0.18588\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1540: acc = 0.87253, nmi = 0.85328, ari = 0.82383  ; loss= 0.17677\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1680: acc = 0.87281, nmi = 0.85378, ari = 0.82449  ; loss= 0.17282\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1820: acc = 0.87323, nmi = 0.85483, ari = 0.82534  ; loss= 0.17592\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 1960: acc = 0.87280, nmi = 0.85483, ari = 0.82524  ; loss= 0.1661\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
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          "output_type": "stream",
          "text": [
            "Iter 2100: acc = 0.87309, nmi = 0.85536, ari = 0.82579  ; loss= 0.16393\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 2240: acc = 0.87280, nmi = 0.85530, ari = 0.82562  ; loss= 0.1664\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 2380: acc = 0.87346, nmi = 0.85585, ari = 0.82623  ; loss= 0.16318\n"
          ],
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        },
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 2520: acc = 0.87337, nmi = 0.85621, ari = 0.82639  ; loss= 0.15675\n"
          ],
          "name": "stdout"
        },
        {
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          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "Iter 2660: acc = 0.87379, nmi = 0.85649, ari = 0.82681  ; loss= 0.17039\n"
          ],
          "name": "stdout"
        },
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          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
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            "Iter 2800: acc = 0.87369, nmi = 0.85646, ari = 0.82655  ; loss= 0.15188\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
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            "Iter 2940: acc = 0.87350, nmi = 0.85636, ari = 0.82623  ; loss= 0.14547\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "Iter 3080: acc = 0.87350, nmi = 0.85635, ari = 0.82635  ; loss= 0.14399\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 3220: acc = 0.87323, nmi = 0.85617, ari = 0.82596  ; loss= 0.14036\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
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          "output_type": "stream",
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            "Iter 3360: acc = 0.87340, nmi = 0.85636, ari = 0.82619  ; loss= 0.13657\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 3500: acc = 0.87331, nmi = 0.85624, ari = 0.82582  ; loss= 0.13972\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 3640: acc = 0.87339, nmi = 0.85686, ari = 0.82647  ; loss= 0.13542\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 3780: acc = 0.87327, nmi = 0.85668, ari = 0.82609  ; loss= 0.1319\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 3920: acc = 0.87319, nmi = 0.85659, ari = 0.82610  ; loss= 0.13009\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 4060: acc = 0.87329, nmi = 0.85683, ari = 0.82612  ; loss= 0.13084\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "Iter 4200: acc = 0.87300, nmi = 0.85660, ari = 0.82600  ; loss= 0.12612\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "Iter 4340: acc = 0.87311, nmi = 0.85662, ari = 0.82601  ; loss= 0.12388\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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            "Iter 4480: acc = 0.87314, nmi = 0.85690, ari = 0.82622  ; loss= 0.12302\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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            "Iter 4620: acc = 0.87314, nmi = 0.85699, ari = 0.82628  ; loss= 0.12531\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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            "Iter 4760: acc = 0.87336, nmi = 0.85719, ari = 0.82654  ; loss= 0.12134\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 4900: acc = 0.87330, nmi = 0.85714, ari = 0.82646  ; loss= 0.12536\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 5040: acc = 0.87330, nmi = 0.85727, ari = 0.82665  ; loss= 0.12178\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
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            "Iter 5180: acc = 0.87347, nmi = 0.85741, ari = 0.82674  ; loss= 0.11922\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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          "output_type": "stream",
          "text": [
            "Iter 5320: acc = 0.87346, nmi = 0.85729, ari = 0.82674  ; loss= 0.11886\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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            "Iter 5460: acc = 0.87359, nmi = 0.85745, ari = 0.82692  ; loss= 0.11903\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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          "name": "stderr"
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            "Iter 5600: acc = 0.87374, nmi = 0.85767, ari = 0.82716  ; loss= 0.11609\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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            "Iter 5740: acc = 0.87351, nmi = 0.85732, ari = 0.82679  ; loss= 0.10833\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
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            "Iter 5880: acc = 0.87354, nmi = 0.85738, ari = 0.82686  ; loss= 0.11415\n"
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            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6020: acc = 0.87370, nmi = 0.85761, ari = 0.82714  ; loss= 0.11178\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6160: acc = 0.87357, nmi = 0.85733, ari = 0.82684  ; loss= 0.10786\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6300: acc = 0.87369, nmi = 0.85772, ari = 0.82722  ; loss= 0.10445\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6440: acc = 0.87363, nmi = 0.85739, ari = 0.82695  ; loss= 0.10504\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6580: acc = 0.87357, nmi = 0.85738, ari = 0.82688  ; loss= 0.10985\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6720: acc = 0.87361, nmi = 0.85753, ari = 0.82703  ; loss= 0.10309\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 6860: acc = 0.87354, nmi = 0.85738, ari = 0.82688  ; loss= 0.09936\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 7000: acc = 0.87369, nmi = 0.85762, ari = 0.82711  ; loss= 0.10289\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Iter 7140: acc = 0.87360, nmi = 0.85742, ari = 0.82689  ; loss= 0.09864\n",
            "Iter 7280: acc = 0.87373, nmi = 0.85756, ari = 0.82708  ; loss= 0.10111\n",
            "delta_label  0.0007428571428571429 < tol  0.001\n",
            "Reached tolerance threshold. Stopping training.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DblKjs2aPC1f",
        "colab_type": "text"
      },
      "source": [
        "**`DEC improves clustring to 87%`**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s_ZnsM1cH51C",
        "colab_type": "code",
        "outputId": "3ffda523-c334-457d-cc9b-7f6fa8d50b35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        }
      },
      "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",
        "    acc1 = np.round(acc(y, y_pred), 5)\n",
        "    nmi1 = np.round(nmi(y, y_pred), 5)\n",
        "    ari1 = np.round(ari(y, y_pred), 5)\n",
        "    loss1 = np.round(loss, 5)\n",
        "    print('Acc = %.5f, nmi = %.5f, ari = %.5f' % (acc1, nmi1, ari1), ' ; loss=', loss1)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Acc = 0.87373, nmi = 0.85756, ari = 0.82708  ; loss= 0.10111\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/linear_assignment_.py:127: DeprecationWarning: The linear_assignment function is deprecated in 0.21 and will be removed from 0.23. Use scipy.optimize.linear_sum_assignment instead.\n",
            "  DeprecationWarning)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/metrics/cluster/supervised.py:859: FutureWarning: The behavior of NMI will change in version 0.22. To match the behavior of 'v_measure_score', NMI will use average_method='arithmetic' by default.\n",
            "  FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_4zKwqzuIEJ3",
        "colab_type": "code",
        "outputId": "3378e301-7aeb-482a-f74b-9cf3268fd2c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 891
        }
      },
      "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()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ozi61bGJiYpnrVVNTQ+/evbFw4UK4uLjgxo0bgmmFsrOzsXHjxnLFWfR8VddzXZWysrLw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\njXsFbQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x1008 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}