pre_2048.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EdWangLoDaSc/Dropout-as-a-Grid-Search_Representing-Model-Uncertainty-in-Deep-Learning/blob/main/pre_2048.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCENnU2LwLgv"
      },
      "source": [
        "It is necessary to use tensorflow 1.14.0 under the environment of Colab."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tensorflow==1.14.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QuOaKZc14cYf",
        "outputId": "aeb858db-7b89-4098-b202-2464252c968e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting tensorflow==1.14.0\n",
            "  Downloading tensorflow-1.14.0-cp37-cp37m-manylinux1_x86_64.whl (109.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 109.3 MB 51 kB/s \n",
            "\u001b[?25hRequirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.37.1)\n",
            "Requirement already satisfied: numpy<2.0,>=1.14.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.21.6)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.14.1)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.15.0)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.5.3)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (3.17.3)\n",
            "Collecting tensorboard<1.15.0,>=1.14.0\n",
            "  Downloading tensorboard-1.14.0-py3-none-any.whl (3.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.1 MB 34.8 MB/s \n",
            "\u001b[?25hCollecting tensorflow-estimator<1.15.0rc0,>=1.14.0rc0\n",
            "  Downloading tensorflow_estimator-1.14.0-py2.py3-none-any.whl (488 kB)\n",
            "\u001b[K     |████████████████████████████████| 488 kB 59.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.2.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.1.2)\n",
            "Collecting keras-applications>=1.0.6\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[K     |████████████████████████████████| 50 kB 6.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.47.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (1.1.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.8.1)\n",
            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==1.14.0) (0.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.7/dist-packages (from keras-applications>=1.0.6->tensorflow==1.14.0) (3.1.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.4.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (1.0.1)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (57.4.0)\n",
            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (4.12.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard<1.15.0,>=1.14.0->tensorflow==1.14.0) (3.8.1)\n",
            "Requirement already satisfied: cached-property in /usr/local/lib/python3.7/dist-packages (from h5py->keras-applications>=1.0.6->tensorflow==1.14.0) (1.5.2)\n",
            "Installing collected packages: tensorflow-estimator, tensorboard, keras-applications, tensorflow\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.8.0\n",
            "    Uninstalling tensorflow-estimator-2.8.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.8.0\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.8.0\n",
            "    Uninstalling tensorboard-2.8.0:\n",
            "      Successfully uninstalled tensorboard-2.8.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.8.2+zzzcolab20220719082949\n",
            "    Uninstalling tensorflow-2.8.2+zzzcolab20220719082949:\n",
            "      Successfully uninstalled tensorflow-2.8.2+zzzcolab20220719082949\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "kapre 0.3.7 requires tensorflow>=2.0.0, but you have tensorflow 1.14.0 which is incompatible.\u001b[0m\n",
            "Successfully installed keras-applications-1.0.8 tensorboard-1.14.0 tensorflow-1.14.0 tensorflow-estimator-1.14.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgeK7ykvkcDP",
        "outputId": "8ed3bc8f-e6ee-40cf-8f88-0feca8cf7b5b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at ./content\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('./content')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "HBSl6GNhl_Gy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5e1da746-a230-4316-ae29-26c92931dedf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.7/dist-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "## TensorFlow\n",
        "import tensorflow as tf\n",
        "#tf.compat.v1.reset_default_graph()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/content/MyDrive/2048_final.csv')\n"
      ],
      "metadata": {
        "id": "dZPoUthYTwoC"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.values"
      ],
      "metadata": {
        "id": "8I93WiXxT8pE",
        "outputId": "f2ab0bf6-ca0f-46af-c4a6-f033aa6ea82a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 1, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IyVnITvXlvKW"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('/content/content/MyDrive/CGAN_Regression/CGAN_code/Datasets/Datasets.csv')\n",
        "df = df.drop(['Calculated-pChEMBL'], axis = 1).values"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "2048-384-384-384"
      ],
      "metadata": {
        "id": "ajr39Q8VUjHJ",
        "outputId": "49094065-ff13-4325-dfc2-94bef5228a3a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "896"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "4RykJLy-m5hk"
      },
      "outputs": [],
      "source": [
        "num_inputs = 2048\n",
        "\n",
        "## Encoder section\n",
        "hidden_1 = 1664     ## 256 (Number of nodes)\n",
        "hidden_2 = 1280  ## 128\n",
        "hidden_3 = 896  ## 64\n",
        "\n",
        "## Latent Space\n",
        "hidden_4 = 512  ## 32\n",
        "\n",
        "## Decoder \n",
        "hidden_5 = hidden_3      ## 64\n",
        "hidden_6 = hidden_2      ## 128\n",
        "hidden_7 = hidden_1      ## 256\n",
        "\n",
        "num_outputs = num_inputs ## 512"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "BCfc-5Lfnt8d"
      },
      "outputs": [],
      "source": [
        "## Hyper Parameters: \n",
        "\n",
        "learning_rate = 0.001\n",
        "\n",
        "## Activation function\n",
        "\n",
        "\"\"\"\n",
        "Here we are using Rectified Linear Unit( ReLu). It is at the left for the user to test and experiment \n",
        "with other activation functions and learning rate\n",
        "\"\"\"\n",
        "\n",
        "activ_fn = tf.nn.relu\n",
        "\n",
        "## Input Layer\n",
        "\n",
        "\"\"\"\n",
        "Defining Input data\n",
        "\"\"\"\n",
        "\n",
        "X = tf.compat.v1.placeholder(tf.float32,shape = [None,num_inputs])\n",
        "\n",
        "## scaling keras.initializers.\n",
        "#initializer = tf.keras.initializers.variance_scaling_initializer()\n",
        "initializer = tf.contrib.layers.variance_scaling_initializer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "YWii3KL_ovTv"
      },
      "outputs": [],
      "source": [
        "## Weights(Parameters)\n",
        "\"\"\"\n",
        "Here we are randomly initializing the weights which are later updated based on the gradient descent and learning rate \n",
        "such that it minimizes the cost function\n",
        "\"\"\"\n",
        "\n",
        "w1 = tf.Variable(initializer([num_inputs,hidden_1]),dtype=tf.float32)\n",
        "w2 = tf.Variable(initializer([hidden_1,hidden_2]),dtype=tf.float32)\n",
        "w3 = tf.Variable(initializer([hidden_2,hidden_3]),dtype=tf.float32)\n",
        "w4 = tf.Variable(initializer([hidden_3,hidden_4]),dtype=tf.float32)\n",
        "w5 = tf.transpose(w4)\n",
        "w6 = tf.transpose(w3)\n",
        "w7 = tf.transpose(w2)\n",
        "w8 = tf.transpose(w1)\n",
        "\n",
        "b1 = tf.Variable(tf.zeros(hidden_1))\n",
        "b2 = tf.Variable(tf.zeros(hidden_2))\n",
        "b3 = tf.Variable(tf.zeros(hidden_3))\n",
        "b4 = tf.Variable(tf.zeros(hidden_4))\n",
        "b5 = b3\n",
        "b6 = b2\n",
        "b7 = b1\n",
        "b8 = tf.Variable(tf.zeros(num_outputs))\n",
        "\n",
        "hid_layer1 = activ_fn(tf.matmul(X,w1)+b1)\n",
        "hid_layer2 = activ_fn(tf.matmul(hid_layer1,w2)+b2)\n",
        "hid_layer3 = activ_fn(tf.matmul(hid_layer2,w3)+b3)\n",
        "hid_layer4 = activ_fn(tf.matmul(hid_layer3,w4)+b4)\n",
        "hid_layer5 = activ_fn(tf.matmul(hid_layer4,w5)+b5)\n",
        "hid_layer6 = activ_fn(tf.matmul(hid_layer5,w6)+b6)\n",
        "hid_layer7 = activ_fn(tf.matmul(hid_layer6,w7)+b7)\n",
        "output_layer = activ_fn(tf.matmul(hid_layer7,w8)+b8)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "IT9Rmwnawl22",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 371
        },
        "outputId": "f8d8513d-b181-44ec-dadf-869637e11dd9"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-8022c797d210>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0minit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_variables_initializer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSaver\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, var_list, reshape, sharded, max_to_keep, keep_checkpoint_every_n_hours, name, restore_sequentially, saver_def, builder, defer_build, allow_empty, write_version, pad_step_number, save_relative_paths, filename)\u001b[0m\n\u001b[1;32m    823\u001b[0m           time.time() + self._keep_checkpoint_every_n_hours * 3600)\n\u001b[1;32m    824\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mdefer_build\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver_def\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_saver_def\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    835\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    836\u001b[0m       \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Use save/restore instead of build in eager mode.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 837\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuild_save\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuild_restore\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    838\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    839\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_build_eager\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuild_save\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbuild_restore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36m_build\u001b[0;34m(self, checkpoint_path, build_save, build_restore)\u001b[0m\n\u001b[1;32m    873\u001b[0m           \u001b[0mfilename\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheckpoint_path\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    874\u001b[0m           \u001b[0mbuild_save\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbuild_save\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 875\u001b[0;31m           build_restore=build_restore)\n\u001b[0m\u001b[1;32m    876\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msaver_def\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_name\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    877\u001b[0m       \u001b[0;31m# Since self._name is used as a name_scope by builder(), we are\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36m_build_internal\u001b[0;34m(self, names_to_saveables, reshape, sharded, max_to_keep, keep_checkpoint_every_n_hours, name, restore_sequentially, filename, build_save, build_restore)\u001b[0m\n\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     saveables = saveable_object_util.validate_and_slice_inputs(\n\u001b[0;32m--> 482\u001b[0;31m         names_to_saveables)\n\u001b[0m\u001b[1;32m    483\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmax_to_keep\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m       \u001b[0mmax_to_keep\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saving/saveable_object_util.py\u001b[0m in \u001b[0;36mvalidate_and_slice_inputs\u001b[0;34m(names_to_saveables)\u001b[0m\n\u001b[1;32m    333\u001b[0m   \"\"\"\n\u001b[1;32m    334\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 335\u001b[0;31m     \u001b[0mnames_to_saveables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop_list_to_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    336\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    337\u001b[0m   \u001b[0msaveables\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saving/saveable_object_util.py\u001b[0m in \u001b[0;36mop_list_to_dict\u001b[0;34m(op_list, convert_variable_to_tensor)\u001b[0m\n\u001b[1;32m    290\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    291\u001b[0m           raise ValueError(\"At least two variables have the same name: %s\" %\n\u001b[0;32m--> 292\u001b[0;31m                            name)\n\u001b[0m\u001b[1;32m    293\u001b[0m         \u001b[0mnames_to_saveables\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: At least two variables have the same name: Variable/Adam"
          ]
        }
      ],
      "source": [
        "## Loss Function(Cost function)\n",
        "\n",
        "\"\"\"\n",
        "Here we are defining the cost function, Minimizing loss between input and output layers\n",
        "\"\"\"\n",
        "\n",
        "loss = tf.reduce_mean(tf.square(output_layer - X))\n",
        "\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
        "\n",
        "## Optimizer\n",
        "train = optimizer.minimize(loss)\n",
        "\n",
        "\n",
        "## Initialzing\n",
        "init = tf.global_variables_initializer()\n",
        "\n",
        "saver = tf.train.Saver()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "bb54nOZ-w_d4",
        "outputId": "6019925f-f577-470b-fb65-097991f0b1a2"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "DataLossError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mDataLossError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1355\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1356\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1357\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1340\u001b[0m       return self._call_tf_sessionrun(\n\u001b[0;32m-> 1341\u001b[0;31m           options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[1;32m   1342\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[0;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[1;32m   1428\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1429\u001b[0;31m         run_metadata)\n\u001b[0m\u001b[1;32m   1430\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mDataLossError\u001b[0m: Checksum does not match: stored 1652297395 vs. calculated on the restored bytes 17055889\n\t [[{{node save_6/RestoreV2}}]]",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mDataLossError\u001b[0m                             Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-2048838b7079>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0msaver\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimport_meta_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'./content/MyDrive/checkpoint_dir/my_model.meta'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0msaver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msess\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlatest_checkpoint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/content/MyDrive/checkpoint_dir'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;31m# 加载变量值\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mepoch\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moffset\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlsd1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\u001b[0m in \u001b[0;36mrestore\u001b[0;34m(self, sess, save_path)\u001b[0m\n\u001b[1;32m   1284\u001b[0m       \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1285\u001b[0m         sess.run(self.saver_def.restore_op_name,\n\u001b[0;32m-> 1286\u001b[0;31m                  {self.saver_def.filename_tensor_name: save_path})\n\u001b[0m\u001b[1;32m   1287\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNotFoundError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1288\u001b[0m       \u001b[0;31m# There are three common conditions that might cause this error:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    948\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    949\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 950\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    951\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    952\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1171\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1173\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1174\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1348\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1349\u001b[0m       return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[0;32m-> 1350\u001b[0;31m                            run_metadata)\n\u001b[0m\u001b[1;32m   1351\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1352\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1368\u001b[0m           \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1370\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mDataLossError\u001b[0m: Checksum does not match: stored 1652297395 vs. calculated on the restored bytes 17055889\n\t [[node save_6/RestoreV2 (defined at <ipython-input-21-c1d7abc8abab>:14) ]]\n\nOriginal stack trace for 'save_6/RestoreV2':\n  File \"/usr/lib/python3.7/runpy.py\", line 193, in _run_module_as_main\n    \"__main__\", mod_spec)\n  File \"/usr/lib/python3.7/runpy.py\", line 85, in _run_code\n    exec(code, run_globals)\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py\", line 16, in <module>\n    app.launch_new_instance()\n  File \"/usr/local/lib/python3.7/dist-packages/traitlets/config/application.py\", line 846, in launch_instance\n    app.start()\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelapp.py\", line 612, in start\n    self.io_loop.start()\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/platform/asyncio.py\", line 132, in start\n    self.asyncio_loop.run_forever()\n  File \"/usr/lib/python3.7/asyncio/base_events.py\", line 541, in run_forever\n    self._run_once()\n  File \"/usr/lib/python3.7/asyncio/base_events.py\", line 1786, in _run_once\n    handle._run()\n  File \"/usr/lib/python3.7/asyncio/events.py\", line 88, in _run\n    self._context.run(self._callback, *self._args)\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/ioloop.py\", line 758, in _run_callback\n    ret = callback()\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/stack_context.py\", line 300, in null_wrapper\n    return fn(*args, **kwargs)\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1233, in inner\n    self.run()\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 1147, in run\n    yielded = self.gen.send(value)\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 365, in process_one\n    yield gen.maybe_future(dispatch(*args))\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 268, in dispatch_shell\n    yield gen.maybe_future(handler(stream, idents, msg))\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel/kernelbase.py\", line 545, in execute_request\n    user_expressions, allow_stdin,\n  File \"/usr/local/lib/python3.7/dist-packages/tornado/gen.py\", line 326, in wrapper\n    yielded = next(result)\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel/ipkernel.py\", line 306, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)\n  File \"/usr/local/lib/python3.7/dist-packages/ipykernel/zmqshell.py\", line 536, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2855, in run_cell\n    raw_cell, store_history, silent, shell_futures)\n  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 2881, in _run_cell\n    return runner(coro)\n  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/async_helpers.py\", line 68, in _pseudo_sync_runner\n    coro.send(None)\n  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3058, in run_cell_async\n    interactivity=interactivity, compiler=compiler, result=result)\n  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3249, in run_ast_nodes\n    if (await self.run_code(code, result,  async_=asy)):\n  File \"/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py\", line 3326, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)\n  File \"<ipython-input-21-c1d7abc8abab>\", line 14, in <module>\n    tf.train.Saver().restore(sess, './content/MyDrive/checkpoint_dir/my_model')\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\", line 825, in __init__\n    self.build()\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\", line 837, in build\n    self._build(self._filename, build_save=True, build_restore=True)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\", line 875, in _build\n    build_restore=build_restore)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\", line 508, in _build_internal\n    restore_sequentially, reshape)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\", line 328, in _AddRestoreOps\n    restore_sequentially)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py\", line 575, in bulk_restore\n    return io_ops.restore_v2(filename_tensor, names, slices, dtypes)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/ops/gen_io_ops.py\", line 1696, in restore_v2\n    name=name)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/op_def_library.py\", line 788, in _apply_op_helper\n    op_def=op_def)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py\", line 507, in new_func\n    return func(*args, **kwargs)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\", line 3616, in create_op\n    op_def=op_def)\n  File \"/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/ops.py\", line 2005, in __init__\n    self._traceback = tf_stack.extract_stack()\n"
          ]
        }
      ],
      "source": [
        "## Training the model\n",
        "\n",
        "\"\"\"\n",
        "Epoch: Number of training steps/iterations\n",
        "Batch Size: Number of obseravtions that are used during each iterations to update weights. \n",
        "\"\"\"\n",
        "num_epochs =300\n",
        "batch_size = 8\n",
        "\n",
        "to_plot_220 = []\n",
        "from sklearn.utils import shuffle\n",
        "#df = shuffle(df) \n",
        "\n",
        "\n",
        "with tf.Session() as sess:\n",
        "    saver = tf.train.import_meta_graph('./content/MyDrive/checkpoint_dir/my_model.meta')\n",
        "    saver.restore(sess, tf.train.latest_checkpoint('/content/content/MyDrive/checkpoint_dir'))# 加载变量值\n",
        "    for epoch in range(num_epochs):\n",
        "        for index, offset in enumerate(range(0, lsd1.shape[0], batch_size)):\n",
        "            xs = lsd1[offset: offset + batch_size]\n",
        "            #print(offset)\n",
        "            sess.run(train, feed_dict={X: xs})\n",
        "        training_loss = loss.eval(feed_dict={X: xs})\n",
        "        to_plot_220.append(training_loss)\n",
        "        print(\"Epoch: {} Loss: {}\".format(epoch,training_loss))\n",
        "        \n",
        "        \n",
        "    \"\"\"\n",
        "    Saving the trained model\n",
        "    \"\"\"\n",
        "    saver.save(sess,'./checkpoint_/my_model')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lsd1 = pd.read_csv('/content/content/MyDrive/2048_dataset.csv').values"
      ],
      "metadata": {
        "id": "7HvY4qPqeq9n"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## With the weights obtained from training earlier, lets obtains the feature vectors at the latent space\n",
        "with tf.Session() as sess:\n",
        "    saver.restore(sess,'./checkpoint_dir/my_model')\n",
        "    res = hid_layer4.eval(feed_dict={X: lsd1})"
      ],
      "metadata": {
        "id": "CP7Jlv_mtZWI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "res"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8LcqAYrH3u7-",
        "outputId": "0f76767a-a730-4aee-e84c-3eaea9527cf1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 0.        , 0.        , ..., 0.5813594 , 0.22038461,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.38060668, 0.18699382,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.6538844 , 0.08100873,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 0.27658787, 0.32678458,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.35579535, 0.33068544,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.53077865,\n",
              "        0.        ]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "all_v =tf.global_variables()\n",
        "for i in all_v: \n",
        "  print (i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJakzA8q5Ph2",
        "outputId": "b698a722-8fe4-4d3e-b815-cc95692ff934"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<tf.Variable 'Variable:0' shape=(2048, 1664) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_1:0' shape=(1664, 1280) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_2:0' shape=(1280, 896) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_3:0' shape=(896, 512) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_4:0' shape=(1664,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_5:0' shape=(1280,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_6:0' shape=(896,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_7:0' shape=(512,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_8:0' shape=(2048,) dtype=float32_ref>\n",
            "<tf.Variable 'beta1_power:0' shape=() dtype=float32_ref>\n",
            "<tf.Variable 'beta2_power:0' shape=() dtype=float32_ref>\n",
            "<tf.Variable 'Variable/Adam:0' shape=(2048, 1664) dtype=float32_ref>\n",
            "<tf.Variable 'Variable/Adam_1:0' shape=(2048, 1664) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_1/Adam:0' shape=(1664, 1280) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_1/Adam_1:0' shape=(1664, 1280) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_2/Adam:0' shape=(1280, 896) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_2/Adam_1:0' shape=(1280, 896) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_3/Adam:0' shape=(896, 512) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_3/Adam_1:0' shape=(896, 512) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_4/Adam:0' shape=(1664,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_4/Adam_1:0' shape=(1664,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_5/Adam:0' shape=(1280,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_5/Adam_1:0' shape=(1280,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_6/Adam:0' shape=(896,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_6/Adam_1:0' shape=(896,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_7/Adam:0' shape=(512,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_7/Adam_1:0' shape=(512,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_8/Adam:0' shape=(2048,) dtype=float32_ref>\n",
            "<tf.Variable 'Variable_8/Adam_1:0' shape=(2048,) dtype=float32_ref>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "lGvlQ-tByw54",
        "outputId": "167bb02d-c854-4b46-8b47-8d5ab3fe3ca4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "## Visualizing\n",
        "import matplotlib.pyplot as plt\n",
        "plt.plot(to_plot_220)\n",
        "plt.xlabel(\"Number of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Loss Vs number epochs for latent space of 32 Dimensions\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rEqCkHsQybSk",
        "outputId": "a4fa3cef-7d44-4710-a207-ed6be5b8d4bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     0    1    2    3    4    5         6         7    8         9    ...  \\\n",
            "0    0.0  0.0  0.0  0.0  0.0  0.0  0.193804  0.000000  0.0  0.648849  ...   \n",
            "1    0.0  0.0  0.0  0.0  0.0  0.0  0.000000  0.404883  0.0  0.369937  ...   \n",
            "2    0.0  0.0  0.0  0.0  0.0  0.0  0.298939  0.612330  0.0  0.697678  ...   \n",
            "3    0.0  0.0  0.0  0.0  0.0  0.0  0.695686  0.386214  0.0  0.041313  ...   \n",
            "4    0.0  0.0  0.0  0.0  0.0  0.0  0.238818  0.694935  0.0  0.339270  ...   \n",
            "..   ...  ...  ...  ...  ...  ...       ...       ...  ...       ...  ...   \n",
            "926  0.0  0.0  0.0  0.0  0.0  0.0  0.930748  0.476785  0.0  0.371940  ...   \n",
            "927  0.0  0.0  0.0  0.0  0.0  0.0  0.315975  0.532350  0.0  0.072464  ...   \n",
            "928  0.0  0.0  0.0  0.0  0.0  0.0  0.239083  0.630502  0.0  0.124235  ...   \n",
            "929  0.0  0.0  0.0  0.0  0.0  0.0  0.032177  0.000000  0.0  0.156478  ...   \n",
            "930  0.0  0.0  0.0  0.0  0.0  0.0  0.328920  0.175904  0.0  0.247453  ...   \n",
            "\n",
            "          502       503  504  505  506       507  508       509       510  511  \n",
            "0    0.565905  0.533701  0.0  0.0  0.0  0.247640  0.0  0.581359  0.220385  0.0  \n",
            "1    0.518416  0.831457  0.0  0.0  0.0  0.000000  0.0  0.380607  0.186994  0.0  \n",
            "2    0.297393  0.753897  0.0  0.0  0.0  0.356754  0.0  0.653884  0.081009  0.0  \n",
            "3    0.856224  0.281456  0.0  0.0  0.0  0.264230  0.0  0.329462  0.000000  0.0  \n",
            "4    0.378957  0.031224  0.0  0.0  0.0  0.521267  0.0  0.419258  0.089600  0.0  \n",
            "..        ...       ...  ...  ...  ...       ...  ...       ...       ...  ...  \n",
            "926  0.206811  0.584552  0.0  0.0  0.0  0.000000  0.0  0.066137  0.149211  0.0  \n",
            "927  0.293827  0.341465  0.0  0.0  0.0  0.114575  0.0  0.035162  0.462875  0.0  \n",
            "928  0.296113  0.389068  0.0  0.0  0.0  0.116494  0.0  0.276588  0.326785  0.0  \n",
            "929  1.015718  0.582232  0.0  0.0  0.0  0.264288  0.0  0.355795  0.330685  0.0  \n",
            "930  0.074484  0.919062  0.0  0.0  0.0  0.558066  0.0  0.000000  0.530779  0.0  \n",
            "\n",
            "[931 rows x 512 columns]\n"
          ]
        }
      ],
      "source": [
        "res.shape\n",
        "dr = pd.DataFrame(res)\n",
        "print(dr)\n",
        "dr.to_csv('2048_521.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "61CtakHyzeYb"
      },
      "source": [
        "# More larger Dimensions\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJ5U160AUnLR",
        "outputId": "d32dee0c-1d65-4b30-ad73-b40eeba24fb0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch: 0 Loss: 0.10121292620897293\n",
            "Epoch: 20 Loss: 0.012367009185254574\n",
            "Epoch: 40 Loss: 0.01190527155995369\n",
            "Epoch: 60 Loss: 0.009825997985899448\n",
            "Epoch: 80 Loss: 0.012089080177247524\n",
            "Epoch: 100 Loss: 0.012143723666667938\n",
            "Epoch: 120 Loss: 0.008897398598492146\n",
            "Epoch: 140 Loss: 0.00812164694070816\n",
            "Epoch: 160 Loss: 0.010212169960141182\n",
            "Epoch: 180 Loss: 0.008470087312161922\n",
            "Epoch: 200 Loss: 0.010004487819969654\n",
            "Epoch: 220 Loss: 0.008345128037035465\n",
            "Epoch: 240 Loss: 0.008415118791162968\n",
            "Epoch: 260 Loss: 0.008509389124810696\n",
            "Epoch: 280 Loss: 0.00817402359098196\n",
            "Epoch: 300 Loss: 0.0077760168351233006\n",
            "Epoch: 320 Loss: 0.007885734550654888\n",
            "Epoch: 340 Loss: 0.008110381662845612\n",
            "Epoch: 360 Loss: 0.006951645016670227\n",
            "Epoch: 380 Loss: 0.007551573216915131\n",
            "Epoch: 400 Loss: 0.0071871024556458\n",
            "Epoch: 420 Loss: 0.007503820117563009\n",
            "Epoch: 440 Loss: 0.009221826680004597\n",
            "Epoch: 460 Loss: 0.007303739432245493\n",
            "Epoch: 480 Loss: 0.007328186649829149\n"
          ]
        }
      ],
      "source": [
        "num_inputs = 512\n",
        "\n",
        "hidden_1 = 512//2       ## 256\n",
        "hidden_2 = hidden_1 //2  ## 128\n",
        "hidden_3 = hidden_2 //2  ## 64\n",
        "hidden_4 = hidden_2\n",
        "hidden_5 = hidden_1\n",
        "\n",
        "num_outputs = num_inputs ## 512\n",
        "\n",
        "learning_rate = 0.001\n",
        "activ_fn = tf.nn.relu\n",
        "#norm = tf.nn.batch_normalization()\n",
        "\n",
        "X = tf.placeholder(tf.float32,shape = [None,num_inputs])\n",
        "\n",
        "## scaling \n",
        "initializer = tf.contrib.layers.variance_scaling_initializer()\n",
        "\n",
        "## Weights\n",
        "\n",
        "w1 = tf.Variable(initializer([num_inputs,hidden_1]),dtype=tf.float32)\n",
        "w2 = tf.Variable(initializer([hidden_1,hidden_2]),dtype=tf.float32)\n",
        "w3 = tf.Variable(initializer([hidden_2,hidden_3]),dtype=tf.float32)\n",
        "w4 = tf.transpose(w3)\n",
        "w5 = tf.transpose(w2)\n",
        "w6 = tf.transpose(w1)\n",
        "\n",
        "\n",
        "\n",
        "## Biases\n",
        "\n",
        "b1 = tf.Variable(tf.zeros(hidden_1))\n",
        "b2 = tf.Variable(tf.zeros(hidden_2))\n",
        "b3 = tf.Variable(tf.zeros(hidden_3))\n",
        "\n",
        "b4 = b2\n",
        "b5 = b1\n",
        "\n",
        "b6 = tf.Variable(tf.zeros(num_outputs))\n",
        "\n",
        "\n",
        "hid_layer1 = activ_fn(tf.matmul(X,w1)+b1)\n",
        "hid_layer2 = activ_fn(tf.matmul(hid_layer1,w2)+b2)\n",
        "hid_layer3 = activ_fn(tf.matmul(hid_layer2,w3)+b3)\n",
        "hid_layer4 = activ_fn(tf.matmul(hid_layer3,w4)+b4)\n",
        "hid_layer5 = activ_fn(tf.matmul(hid_layer4,w5)+b5)\n",
        "output_layer = activ_fn(tf.matmul(hid_layer5,w6)+b6)\n",
        "\n",
        "## Loss Function\n",
        "\n",
        "loss = tf.reduce_mean(tf.square(output_layer - X))\n",
        "\n",
        "## Optimzer\n",
        "optimizer = tf.train.AdamOptimizer(learning_rate)\n",
        "\n",
        "train = optimizer.minimize(loss)\n",
        "\n",
        "## Initialzing\n",
        "init = tf.global_variables_initializer()\n",
        "\n",
        "saver = tf.train.Saver()\n",
        "\n",
        "num_epochs = 500\n",
        "batch_size = 8\n",
        "to_plot = []\n",
        "from sklearn.utils import shuffle\n",
        "#X = shuffle(X) \n",
        "\n",
        "with tf.Session() as sess:\n",
        "    sess.run(init)\n",
        "    for epoch in range(num_epochs):\n",
        "        for index, offset in enumerate(range(0, df.shape[0], batch_size)):\n",
        "            xs = df[offset: offset + batch_size]\n",
        "            #print(offset)\n",
        "            sess.run(train, feed_dict={X: xs})\n",
        "        training_loss = loss.eval(feed_dict={X: xs})\n",
        "        to_plot.append(training_loss)\n",
        "        if epoch % 20 == 0:\n",
        "          print(\"Epoch: {} Loss: {}\".format(epoch,training_loss))\n",
        "    \n",
        "    saver.save(sess,'./stacked_autoencoder_32_50D_tanh.ckpt')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DPJDGHFJdIp4"
      },
      "outputs": [],
      "source": [
        "with tf.Session() as sess:\n",
        "    saver.restore(sess,'./stacked_autoencoder_32_50D_tanh.ckpt')\n",
        "    d50 = hid_layer3.eval(feed_dict={X: df})  \n",
        "\n",
        "np.savetxt('Dim_64.tsv',d50,delimiter='\\t')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UgD_uW2tdRyg",
        "outputId": "a34f8279-1126-4cfd-edde-3418b54b49f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[0.60872406, 0.        , 0.        , ..., 0.01195174, 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 1.5955508 , 0.        , ..., 0.        , 0.        ,\n",
              "        1.7521322 ],\n",
              "       [0.        , 2.2621021 , 0.        , ..., 0.        , 0.        ,\n",
              "        2.5632324 ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        1.000032  ],\n",
              "       [0.        , 0.        , 0.        , ..., 1.340622  , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.56723374, 0.        ,\n",
              "        0.0072813 ]], dtype=float32)"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "d50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "v9gM0WKMdT6F",
        "outputId": "e89a1752-6538-47e7-bfb8-e1a856d0a2b0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "Dim_51,= plt.plot(to_plot, label=\"Dimension 51\")\n",
        "Dim_220, =  plt.plot(to_plot_220, label=\"Dimension 220\")\n",
        "plt.legend(handles=[Dim_51, Dim_220])\n",
        "plt.xlabel(\"Number of Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title(\"Loss Vs number epochs for different latent space\")\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "JcuxAkntsrKb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6p_zlz9psrVI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "iz6RHa56srXb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "FltyGhPbsrZk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bXMhWFoEsrcI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "54B6kWA8sreK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "eqii8Fsasrgo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "g0Q0cLiDsri7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yxDz-uaksrk9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "gQKbJAo2srnT"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "“Untitled8.ipynb”的副本",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "13CA2fuoXESKGJTteevSjMCORxzUkc1I6",
      "authorship_tag": "ABX9TyM0BBo15JXLFdwaYicI6zaT",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}