ResNet50.ipynb

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    "colab": {
      "name": "ResNet50.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/liuxx479/Outlier/blob/master/ResNet50.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stTiXCP38WMh",
        "colab_type": "code",
        "outputId": "dfd7f468-4543-48e9-b949-09ef80c2684b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "%pylab inline\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras.initializers import glorot_uniform\n",
        "from tensorflow.keras import Model\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow_datasets as tfds"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1TFhPhC8-8T",
        "colab_type": "code",
        "outputId": "510179cf-9d1e-42ae-ee3b-b6acb9ee751a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "\n",
        "\n",
        "!pip install --quiet git+https://github.com/liuxx479/Outlier.git\n",
        "from outlier.datasets import gaussian_convergence\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for Outlier (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JcfZXpBN8WM7",
        "colab_type": "code",
        "outputId": "2ff226dd-7185-4ff9-f06a-ce5ce531dce3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "root = '/content/drive/My Drive'\n",
        "data_path = os.path.join(root,'tensorflow_datasets/')\n",
        "model_path= os.path.join(root,'models/Keras')\n",
        "\n",
        "if not os.path.isdir(model_path):\n",
        "    os.makedirs(model_path)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "arJDnswa8WNK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset, info = tfds.load(name=\"gaussian_convergence\", split=\"train\", data_dir=data_path,with_info=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZF61hgrB8WNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATASET_SIZE = info.splits['train'].num_examples"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gaoMb8IR8WNk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## Implementing a simple resnet model\n",
        "def redefine(data):\n",
        "    x    = data['map'] \n",
        "    x    = tf.expand_dims(x,-1)\n",
        "    y    = data['params']\n",
        "    return x, y\n",
        "\n",
        "def downsample(x,y):\n",
        "    x    = tf.image.resize(x,[128,128])\n",
        "    return x, y\n",
        "  \n",
        "def augmentation(x, y):\n",
        "  x = tf.image.random_flip_left_right(x)\n",
        "  x = tf.image.random_flip_up_down(x)\n",
        "  x = x + 0.02*tf.random.normal(shape=(128,128,1))\n",
        "  return x, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O8-k4TeY8WNp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATASET_SIZE = 20000\n",
        "BATCH_SIZE = 8\n",
        "\n",
        "train_size = int(0.8 * DATASET_SIZE)\n",
        "test_size = int(0.2 * DATASET_SIZE)\n",
        "\n",
        "full_dataset = dataset.map(redefine).map(downsample)\n",
        "\n",
        "train_dataset= full_dataset.take(train_size).repeat().map(augmentation).shuffle(buffer_size=2000).batch(BATCH_SIZE)\n",
        "test_dataset = full_dataset.skip(train_size).take(test_size).batch(BATCH_SIZE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7CDt0Bs8WNu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def identity_block(X, f, filters, stage, block):\n",
        "    \"\"\"\n",
        "    Implementation of the identity block\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
        "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
        "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
        "    stage -- integer, used to name the layers, depending on their position in the network\n",
        "    block -- string/character, used to name the layers, depending on their position in the network\n",
        "    \n",
        "    Returns:\n",
        "    X -- output of the identity block, tensor of shape (n_H, n_W, n_C)\n",
        "    \"\"\"\n",
        "    \n",
        "    # defining name basis\n",
        "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
        "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
        "    \n",
        "    # Retrieve Filters\n",
        "    F1, F2, F3 = filters\n",
        "    \n",
        "    # Save the input value. You'll need this later to add back to the main path. \n",
        "    X_shortcut = X\n",
        "    \n",
        "    # First component of main path\n",
        "    X = Conv2D(filters = F1, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "    \n",
        "    # Second component of main path\n",
        "    X = Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
        "    X = Activation('relu')(X)\n",
        "\n",
        "    # Third component of main path \n",
        "    X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
        "\n",
        "    # Final step: Add shortcut value to main path, and pass it through a RELU activation \n",
        "    X = Add()([X, X_shortcut])\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "    \n",
        "    return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zJVSNEzb9bC2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def convolutional_block(X, f, filters, stage, block, s = 2):\n",
        "    \"\"\"\n",
        "    Implementation of the convolutional block\n",
        "    \n",
        "    Arguments:\n",
        "    X -- input tensor of shape (m, n_H_prev, n_W_prev, n_C_prev)\n",
        "    f -- integer, specifying the shape of the middle CONV's window for the main path\n",
        "    filters -- python list of integers, defining the number of filters in the CONV layers of the main path\n",
        "    stage -- integer, used to name the layers, depending on their position in the network\n",
        "    block -- string/character, used to name the layers, depending on their position in the network\n",
        "    s -- Integer, specifying the stride to be used\n",
        "    \n",
        "    Returns:\n",
        "    X -- output of the convolutional block, tensor of shape (n_H, n_W, n_C)\n",
        "    \"\"\"\n",
        "    \n",
        "    # defining name basis\n",
        "    conv_name_base = 'res' + str(stage) + block + '_branch'\n",
        "    bn_name_base = 'bn' + str(stage) + block + '_branch'\n",
        "    \n",
        "    # Retrieve Filters\n",
        "    F1, F2, F3 = filters\n",
        "    \n",
        "    # Save the input value\n",
        "    X_shortcut = X\n",
        "\n",
        "\n",
        "    ##### MAIN PATH #####\n",
        "    # First component of main path \n",
        "    X = Conv2D(F1, (1, 1), strides = (s,s), name = conv_name_base + '2a', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2a')(X)\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "\n",
        "    # Second component of main path \n",
        "    X = Conv2D(F2, (f,f), strides = (1,1), padding = 'same', name = conv_name_base + '2b', kernel_initializer=glorot_uniform(seed =0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2b')(X)\n",
        "    X = Activation('relu') (X)\n",
        "\n",
        "    # Third component of main path \n",
        "    X = Conv2D(F3, (1,1), strides = (1,1), padding = 'valid', name = conv_name_base + '2c', kernel_initializer=glorot_uniform(seed =0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = bn_name_base + '2c')(X)\n",
        "\n",
        "    ##### SHORTCUT PATH #### \n",
        "    X_shortcut = Conv2D(F3, (1,1), strides = (s,s), padding = 'valid', name = conv_name_base + '1', kernel_initializer=glorot_uniform(seed =0))(X_shortcut)\n",
        "    X_shortcut = BatchNormalization(axis = 3, name = bn_name_base + '1')(X_shortcut)\n",
        "\n",
        "    # Final step: Add shortcut value to main path, and pass it through a RELU activation \n",
        "    X = Add()([X, X_shortcut])\n",
        "    X = Activation('relu')(X)\n",
        "    \n",
        "    \n",
        "    return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n7lPsXd69fX4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def ResNet50(input_shape = (128, 128, 1), params = 2):\n",
        "    \"\"\"\n",
        "    Implementation of the popular ResNet50 the following architecture:\n",
        "    CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n",
        "    -> CONVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2 -> AVGPOOL -> TOPLAYER\n",
        "\n",
        "    Arguments:\n",
        "    input_shape -- shape of the images of the dataset\n",
        "    classes -- integer, number of classes\n",
        "\n",
        "    Returns:\n",
        "    model -- a Model() instance in Keras\n",
        "    \"\"\"\n",
        "    \n",
        "    # Define the input as a tensor with shape input_shape\n",
        "    X_input = Input(input_shape)\n",
        "\n",
        "    \n",
        "    # Zero-Padding\n",
        "    X = ZeroPadding2D((3, 3))(X_input)\n",
        "    \n",
        "    # Stage 1\n",
        "    X = Conv2D(64, (7, 7), strides = (2, 2), name = 'conv1', kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    X = BatchNormalization(axis = 3, name = 'bn_conv1')(X)\n",
        "    X = Activation('relu')(X)\n",
        "    X = MaxPooling2D((3, 3), strides=(2, 2))(X)\n",
        "\n",
        "    # Stage 2\n",
        "    X = convolutional_block(X, f = 3, filters = [64, 64, 256], stage = 2, block='a', s = 1)\n",
        "    X = identity_block(X, 3, [64, 64, 256], stage=2, block='b')\n",
        "    X = identity_block(X, 3, [64, 64, 256], stage=2, block='c')\n",
        "\n",
        "\n",
        "    # Stage 3 \n",
        "    X = convolutional_block(X, f = 3, filters = [128, 128, 512], stage = 3, block='a', s = 2)\n",
        "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='b')\n",
        "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='c')\n",
        "    X = identity_block(X, 3, [128, 128, 512], stage=3, block='d')\n",
        "\n",
        "    # # Stage 4 \n",
        "    # X = convolutional_block(X, f = 3, filters = [256, 256, 1024], stage = 4, block='a', s = 2)\n",
        "    # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='b')\n",
        "    # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='c')\n",
        "    # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='d')\n",
        "    # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='e')\n",
        "    # X = identity_block(X, 3, [256, 256, 1024], stage=4, block='f')\n",
        "\n",
        "    # # Stage 5 \n",
        "    # X = convolutional_block(X, f = 3, filters = [512, 512, 2048], stage = 5, block='a', s = 2)\n",
        "    # X = identity_block(X, 3, [512, 512, 2048], stage=5, block='b')\n",
        "    # X = identity_block(X, 3, [512, 512, 2048], stage=5, block='c')\n",
        "\n",
        "    # AVGPOOL . Use \"X = AveragePooling2D(...)(X)\"\n",
        "    X = AveragePooling2D()(X)\n",
        "\n",
        "    # output layer\n",
        "    X = Flatten()(X)\n",
        "    X = Dense(params, name='fc' + str(params), kernel_initializer = glorot_uniform(seed=0))(X)\n",
        "    \n",
        "    \n",
        "    # Create model\n",
        "    model = Model(inputs = X_input, outputs = X, name='ResNet50')\n",
        "\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvEFW8FN-A5-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fHnSuAMQ8WOW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e0668c5a-b282-4739-8eff-2b314f05bbcf"
      },
      "source": [
        "res_net_model = ResNet50()\n",
        "res_net_model.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"ResNet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 128, 128, 1) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "zero_padding2d (ZeroPadding2D)  (None, 134, 134, 1)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1 (Conv2D)                  (None, 64, 64, 64)   3200        zero_padding2d[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn_conv1 (BatchNormalization)   (None, 64, 64, 64)   256         conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "activation (Activation)         (None, 64, 64, 64)   0           bn_conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 31, 31, 64)   0           activation[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2a (Conv2D)         (None, 31, 31, 64)   4160        max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2a (BatchNormalizati (None, 31, 31, 64)   256         res2a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_1 (Activation)       (None, 31, 31, 64)   0           bn2a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2b (Conv2D)         (None, 31, 31, 64)   36928       activation_1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2b (BatchNormalizati (None, 31, 31, 64)   256         res2a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_2 (Activation)       (None, 31, 31, 64)   0           bn2a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch2c (Conv2D)         (None, 31, 31, 256)  16640       activation_2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "res2a_branch1 (Conv2D)          (None, 31, 31, 256)  16640       max_pooling2d[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch2c (BatchNormalizati (None, 31, 31, 256)  1024        res2a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn2a_branch1 (BatchNormalizatio (None, 31, 31, 256)  1024        res2a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add (Add)                       (None, 31, 31, 256)  0           bn2a_branch2c[0][0]              \n",
            "                                                                 bn2a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_3 (Activation)       (None, 31, 31, 256)  0           add[0][0]                        \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2a (Conv2D)         (None, 31, 31, 64)   16448       activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2a (BatchNormalizati (None, 31, 31, 64)   256         res2b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_4 (Activation)       (None, 31, 31, 64)   0           bn2b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2b (Conv2D)         (None, 31, 31, 64)   36928       activation_4[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2b (BatchNormalizati (None, 31, 31, 64)   256         res2b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_5 (Activation)       (None, 31, 31, 64)   0           bn2b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2b_branch2c (Conv2D)         (None, 31, 31, 256)  16640       activation_5[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2b_branch2c (BatchNormalizati (None, 31, 31, 256)  1024        res2b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_1 (Add)                     (None, 31, 31, 256)  0           bn2b_branch2c[0][0]              \n",
            "                                                                 activation_3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_6 (Activation)       (None, 31, 31, 256)  0           add_1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2a (Conv2D)         (None, 31, 31, 64)   16448       activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2a (BatchNormalizati (None, 31, 31, 64)   256         res2c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_7 (Activation)       (None, 31, 31, 64)   0           bn2c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2b (Conv2D)         (None, 31, 31, 64)   36928       activation_7[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2b (BatchNormalizati (None, 31, 31, 64)   256         res2c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_8 (Activation)       (None, 31, 31, 64)   0           bn2c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res2c_branch2c (Conv2D)         (None, 31, 31, 256)  16640       activation_8[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn2c_branch2c (BatchNormalizati (None, 31, 31, 256)  1024        res2c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_2 (Add)                     (None, 31, 31, 256)  0           bn2c_branch2c[0][0]              \n",
            "                                                                 activation_6[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_9 (Activation)       (None, 31, 31, 256)  0           add_2[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2a (Conv2D)         (None, 16, 16, 128)  32896       activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2a (BatchNormalizati (None, 16, 16, 128)  512         res3a_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_10 (Activation)      (None, 16, 16, 128)  0           bn3a_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2b (Conv2D)         (None, 16, 16, 128)  147584      activation_10[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2b (BatchNormalizati (None, 16, 16, 128)  512         res3a_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_11 (Activation)      (None, 16, 16, 128)  0           bn3a_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch2c (Conv2D)         (None, 16, 16, 512)  66048       activation_11[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3a_branch1 (Conv2D)          (None, 16, 16, 512)  131584      activation_9[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch2c (BatchNormalizati (None, 16, 16, 512)  2048        res3a_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "bn3a_branch1 (BatchNormalizatio (None, 16, 16, 512)  2048        res3a_branch1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "add_3 (Add)                     (None, 16, 16, 512)  0           bn3a_branch2c[0][0]              \n",
            "                                                                 bn3a_branch1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "activation_12 (Activation)      (None, 16, 16, 512)  0           add_3[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2a (Conv2D)         (None, 16, 16, 128)  65664       activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2a (BatchNormalizati (None, 16, 16, 128)  512         res3b_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_13 (Activation)      (None, 16, 16, 128)  0           bn3b_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2b (Conv2D)         (None, 16, 16, 128)  147584      activation_13[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2b (BatchNormalizati (None, 16, 16, 128)  512         res3b_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_14 (Activation)      (None, 16, 16, 128)  0           bn3b_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3b_branch2c (Conv2D)         (None, 16, 16, 512)  66048       activation_14[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3b_branch2c (BatchNormalizati (None, 16, 16, 512)  2048        res3b_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 16, 16, 512)  0           bn3b_branch2c[0][0]              \n",
            "                                                                 activation_12[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_15 (Activation)      (None, 16, 16, 512)  0           add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2a (Conv2D)         (None, 16, 16, 128)  65664       activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2a (BatchNormalizati (None, 16, 16, 128)  512         res3c_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_16 (Activation)      (None, 16, 16, 128)  0           bn3c_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2b (Conv2D)         (None, 16, 16, 128)  147584      activation_16[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2b (BatchNormalizati (None, 16, 16, 128)  512         res3c_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_17 (Activation)      (None, 16, 16, 128)  0           bn3c_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3c_branch2c (Conv2D)         (None, 16, 16, 512)  66048       activation_17[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3c_branch2c (BatchNormalizati (None, 16, 16, 512)  2048        res3c_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 16, 16, 512)  0           bn3c_branch2c[0][0]              \n",
            "                                                                 activation_15[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_18 (Activation)      (None, 16, 16, 512)  0           add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2a (Conv2D)         (None, 16, 16, 128)  65664       activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2a (BatchNormalizati (None, 16, 16, 128)  512         res3d_branch2a[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_19 (Activation)      (None, 16, 16, 128)  0           bn3d_branch2a[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2b (Conv2D)         (None, 16, 16, 128)  147584      activation_19[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2b (BatchNormalizati (None, 16, 16, 128)  512         res3d_branch2b[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "activation_20 (Activation)      (None, 16, 16, 128)  0           bn3d_branch2b[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "res3d_branch2c (Conv2D)         (None, 16, 16, 512)  66048       activation_20[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "bn3d_branch2c (BatchNormalizati (None, 16, 16, 512)  2048        res3d_branch2c[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 16, 16, 512)  0           bn3d_branch2c[0][0]              \n",
            "                                                                 activation_18[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "activation_21 (Activation)      (None, 16, 16, 512)  0           add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 8, 8, 512)    0           activation_21[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 32768)        0           average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "fc2 (Dense)                     (None, 2)            65538       flatten[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 1,519,362\n",
            "Trainable params: 1,509,250\n",
            "Non-trainable params: 10,112\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vB-aiL9N8WOb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "checkpoint_file = os.path.join(model_path+'/weights.{epoch:02d}-{val_loss:.2f}.hdf5')\n",
        "ckpt_callback   = keras.callbacks.ModelCheckpoint(filepath=checkpoint_file, monitor='val_loss')\n",
        "hists = []"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_XoDyr28WOe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def newest(path):\n",
        "    files = os.listdir(path)\n",
        "    paths = [os.path.join(path, basename) for basename in files]\n",
        "    try:\n",
        "        return max(paths, key=os.path.getctime)\n",
        "    except:\n",
        "        return None"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxKKDzjQ8WOi",
        "colab_type": "code",
        "outputId": "0a59366a-59fd-4dd0-bde4-128950057171",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "latest_file=newest(model_path)\n",
        "print(latest_file)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_9bbPyuFuhZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# if you want to use pre-implemented one\n",
        "# inputs = tf.keras.Input(shape=(128, 128, 1))\n",
        "# resnet = tf.keras.applications.resnet_v2.ResNet50V2(include_top=False,\n",
        "#                                                     weights=None,\n",
        "#                                                     input_shape=[128,128,1],\n",
        "#                                                     pooling='avg')\n",
        "# net = resnet(inputs)\n",
        "# outputs = tf.keras.layers.Dense(2)(net)\n",
        "#res_net_model = tf.keras.Model(inputs, outputs)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mVJLIw5AWye5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0jOXhZsJW3hx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res_net_model.compile(optimizer=tf.keras.optimizers.Adam(),loss='mse')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0t58Sc2N8WOl",
        "colab_type": "code",
        "outputId": "f17ed297-a777-48a6-b48b-2fd38bbad998",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "callbacks     = [ckpt_callback]\n",
        "if latest_file is None:\n",
        "    res_net_model.compile(optimizer=keras.optimizers.Adam(),loss='mse',metrics=['mse'])   \n",
        "else:\n",
        "    res_net_model = keras.models.load_model(latest_file)\n",
        "history = res_net_model.fit(train_dataset, epochs=3, batch_size=BATCH_SIZE,\n",
        "                    validation_data=test_dataset, verbose=1, \n",
        "                    steps_per_epoch=int(0.8*20000/BATCH_SIZE)-1,\n",
        "                    validation_steps=2)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/3\n",
            "1999/1999 [==============================] - 170s 85ms/step - loss: 0.0835 - mse: 0.0835 - val_loss: 0.0183 - val_mse: 0.0183\n",
            "Epoch 2/3\n",
            "1999/1999 [==============================] - 138s 69ms/step - loss: 0.0153 - mse: 0.0153 - val_loss: 0.0171 - val_mse: 0.0171\n",
            "Epoch 3/3\n",
            "1999/1999 [==============================] - 160s 80ms/step - loss: 0.0151 - mse: 0.0151 - val_loss: 0.0194 - val_mse: 0.0194\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1LVumhpn8WO1",
        "colab_type": "code",
        "outputId": "184e5b67-d80d-4346-f8b4-6b234687687a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(history.history['loss'], marker='o')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f8e955b0ef0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_Dp6fi28WO-",
        "colab_type": "code",
        "outputId": "9bf61b8d-1593-4993-b1b2-7ce44fa81ec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "predictions.shape"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7KetQVq8WPE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "truth = list(test_dataset.as_numpy_iterator()) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ae0TCnKmlTo",
        "colab_type": "code",
        "outputId": "d0d6b39e-4eb7-4c99-fc05-56bdb08ff418",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        }
      },
      "source": [
        "truth[1][1]"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.34033588, 0.94652426],\n",
              "       [0.17419967, 0.7330315 ],\n",
              "       [0.35538357, 0.6371391 ],\n",
              "       [0.40783286, 0.79761577],\n",
              "       [0.22505793, 0.9333542 ],\n",
              "       [0.1378838 , 0.7404171 ],\n",
              "       [0.46414724, 0.6698358 ],\n",
              "       [0.07010396, 0.637094  ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3R8zHvck8WPI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160
        },
        "outputId": "0579fe3f-eae9-477a-95b3-34142dce17c4"
      },
      "source": [
        "\n",
        "\n",
        "predictions = res_net_model.predict(x=truth[0][0])\n",
        "\n",
        "print(predictions)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[0.24317081 0.7727258 ]\n",
            " [0.2841931  0.95515   ]\n",
            " [0.24424352 0.7639007 ]\n",
            " [0.2472766  0.7711602 ]\n",
            " [0.25035718 0.7923141 ]\n",
            " [0.24679478 0.7717703 ]\n",
            " [0.24400012 0.76520735]\n",
            " [0.24721651 0.7760195 ]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8RuYuDI4Cm3X",
        "colab_type": "code",
        "outputId": "e79ccd32-b238-46ff-efb5-827c6aca61c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(predictions.shape)\n",
        "print(truth[1][1].shape)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8, 2)\n",
            "(8, 2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vkl7TtwJiRNN",
        "colab_type": "code",
        "outputId": "5ad80f23-07b3-47f0-e229-9ffb502803dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "np.mean((predictions-truth[1][1])**2)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.018599223"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtaaC7Sp8WPN",
        "colab_type": "code",
        "outputId": "9b37fac9-b4a7-4dfb-ba52-c5a36aa9e404",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.plot(truth[1][1][:,0],predictions[:,0], marker='o', ls='')\n",
        "plt.ylim(min(truth[1][1][:,0]),max(truth[1][1][:,0]))"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0701039582490921, 0.4641472399234772)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGmfQS1LjDHpJapxBL0mNM+glqXEGvSQ1bqigT7IryckkM0kOLHD81iQ/SHI+yefmHbszyQvd485RFS5JGs6iQZ9kHXAI+AywHfh8ku3zuv0I+CLw2Lyx7wO+BnwC2AF8LcmG5ZctSRrWMFf0O4CZqjpdVa8Dh4E9gx2q6sWqeg54a97YncATVfVKVb0KPAHsGkHdkqQhDRP0G4GXBvbPdG3DGGpskn1J+kn6Z8+eHfLUkqRhTMRibFU9UlW9qupNTU2NuxxJasowQT8LbB7Y39S1DWM5YyVJIzBM0B8FtiXZmmQ9sBeYHvL8R4BPJ9nQLcJ+umuTJK2SRYO+qs4D+5kL6L8BvllVx5McTLIbIMnHk5wBfhd4OMnxbuwrwH3M/WNxFDjYtUmSVkmqatw1XKTX61W/3x93GZK0piQ5VlW9hY5NxGKsJGnlGPSS1DiDXpIaZ9BLUuMMeklqnEEvSY0z6CWpcQa9JDXOoJekxhn0ktQ4g16SGjdxn3WT5Czwt+Ou4xKuB/5h3EUsYFLrAmu7EpNaF0xubZNaF6xebTdW1YJf6DFxQT/JkvQv9aFB4zSpdYG1XYlJrQsmt7ZJrQsmozZv3UhS4wx6SWqcQb80j4y7gEuY1LrA2q7EpNYFk1vbpNYFE1Cb9+glqXFe0UtS4wx6SWqcQQ8k2ZXkZJKZJAcWOH5rkh8kOZ/kc/OO3Znkhe5x54TV9maSZ7vH9BhquyfJiSTPJflOkhsHjq3YvC2zrnHP2V1Jnu9+/veTbB84dm837mSSnZNQV5ItSc4NzNk3RlnXMLUN9PtskkrSG2hbsTlbTm2rMW8Xqap39ANYB5wCPgCsB/4a2D6vzxbg14H/AnxuoP19wOnuzw3d9oZJqK079n/HPG//Avgn3fbvA/91pedtOXVNyJy9d2B7N/Dfu+3tXf+rga3dedZNQF1bgP81zjnr+l0L/A/gaaC30nM2gtpWdN7mP7yihx3ATFWdrqrXgcPAnsEOVfViVT0HvDVv7E7giap6papeBZ4Adk1IbSttmNqerKqfdbtPA5u67ZWct+XUtdKGqe2nA7vvAS68W2IPcLiqXquqHwIz3fnGXddKW7S2zn3A14GfD7St5Jwtt7ZVZdDDRuClgf0zXdtKj12N8787ST/J00nuGGFdsPTavgR8+wrHrlZdMAFzluTuJKeAB4A/WMrYMdQFsDXJM0m+l+RTI6pp6NqSfAzYXFXfWurYMdYGKztvF7lqJU+usbuxqmaTfAD4bpLnq+rUaheR5AtAD/jN1f7Zl3OJusY+Z1V1CDiU5PeArwIjX/u5Epeo68fADVX1cpJ/Bjye5KZ5/wNYMUneBTwEfHE1ft5SLFLbqs6bV/QwC2we2N/Uta302BU/f1XNdn+eBp4Cblnt2pLcBnwF2F1Vry1l7Bjqmog5G3AYuPC/irHP2UJ1dbdFXu62jzF3z/pDI6prmNquBT4CPJXkReA3gOlu0XPcr89L1rYK83ax1VoMmNQHc/+rOc3cYs2FBZWbLtH3P/PLi7E/ZG5BcUO3/b4JqW0DcHW3fT3wAgssFK1kbcyF5Clg27z2FZu3ZdY1CXO2bWD7t4F+t30TFy8snmZ0i7HLqWvqQh3MLUrOjus10PV/il8seK7YnI2gthWdt1/62St14rX0AP4V8L+7F/9XuraDzF3tAXycuftv/w94GTg+MPbfMrfIMwP8m0mpDfjnwPPdX77ngS+Noba/AP4P8Gz3mF6NebvSuiZkzv4jcLyr68nB4GDufyCngJPAZyahLuCzA+0/AH57tedsXt+n6MJ0pedsObWtxrwNPvwIBElqnPfoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuMMeklq3P8Hwjpldt0c0DkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "77ZKPVc4Dsnr",
        "colab_type": "code",
        "outputId": "a36b7361-221c-45a0-a491-17ba51a2f34e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "plt.plot(truth[1][1][:,1],predictions[:,1], marker='o', ls='')\n",
        "print(predictions[:,1])\n",
        "plt.ylim(min(truth[1][1][:,1]),max(truth[1][1][:,1]))"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.7727258  0.95515    0.7639007  0.7711602  0.7923141  0.7717703\n",
            " 0.76520735 0.7760195 ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.6370940208435059, 0.9465242624282837)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jI2rSIdk8WPS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_S8(om,si8):\n",
        "    S8=si8*sqrt(om/0.3)\n",
        "    return S8"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5U6DA-iq8WPa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "true_s8 = get_S8(truth[1][1][:,0],truth[1][1][:,1])\n",
        "pred_s8 = get_S8(predictions[:,0],predictions[:,1])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y6JVxbin8WPe",
        "colab_type": "code",
        "outputId": "6261be60-1674-4139-d3ed-b3c8628f4580",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        }
      },
      "source": [
        "plt.scatter(true_s8,pred_s8)\n",
        "plt.ylim(min(true_s8),max(true_s8))"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.30797407031059265, 1.0081496238708496)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hx1g-lmO8WPh",
        "colab_type": "code",
        "outputId": "e87455df-dd53-47b8-c1a4-c34cb9dec971",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.scatter(true_s8,truth[1][1][:,1])"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8e953917f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "InEm-z8qsKrc",
        "colab_type": "code",
        "outputId": "4e5ad249-df01-48b0-e461-ad15e59728bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.scatter(true_s8,truth[1][1][:,0])"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8e952f6518>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZRjAcwcsP0p",
        "colab_type": "code",
        "outputId": "174a6d18-675b-4ee4-8e94-1d453dfd2062",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "plt.scatter(truth[1][1][:,0],truth[1][1][:,1])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f8e952d8a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4x43G5lCsVq0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}