Tugas_7_Modeling.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Tugas_7_Modeling",
      "provenance": [],
      "authorship_tag": "ABX9TyOACw4o0ZINBea8rHvdhLbG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MrezaFd/Tugas-Besar-ML-059-055-/blob/main/Tugas_7_Modeling.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X10Fs0sZdypw"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "id": "cjLzq2S3eRGN",
        "outputId": "bd123257-68e4-41b1-c860-28375f8cc7c6"
      },
      "source": [
        "from google.colab import files #import lib\n",
        "files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-d74bccb0-8b60-472a-8dcd-a259babaa995\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-d74bccb0-8b60-472a-8dcd-a259babaa995\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"radityajs28\",\"key\":\"c68d62e8c60d5e8e868437c6f9256a81\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vyhPRKKSeToo"
      },
      "source": [
        "!mkdir -p ~/.kaggle #membuat directory bernama kaggle \n",
        "!cp kaggle.json ~/.kaggle/ #copy file kaggle.json upload tadi ke directory baru\n",
        "!chmod 600 ~/.kaggle/kaggle.json #berikan izin untuk file kaggle.jsonv"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ITeMh8TVebqz",
        "outputId": "5c06fea2-f72e-42ca-87a7-d3ed088bd89e"
      },
      "source": [
        "#Unduh dataset\n",
        "!kaggle datasets download -d fanconic/skin-cancer-malignant-vs-benign"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading skin-cancer-malignant-vs-benign.zip to /content\n",
            " 97% 314M/325M [00:06<00:00, 75.0MB/s]\n",
            "100% 325M/325M [00:06<00:00, 55.0MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8NVsJfQef1a",
        "outputId": "5ad521e4-dbb5-4b31-bbd9-d9185ae2b191"
      },
      "source": [
        "from zipfile import ZipFile\n",
        "file_name = \"skin-cancer-malignant-vs-benign.zip\"\n",
        "\n",
        "with ZipFile(file_name,'r') as zip :\n",
        "  zip.extractall()\n",
        "  print('Berhasil')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Berhasil\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uYUoxmfEehui"
      },
      "source": [
        "!mkdir a"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7DSHarzgei2h"
      },
      "source": [
        "!mv -t a test train"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIas7VQJela5"
      },
      "source": [
        "# Definisikan path untuk data train dan data validation\n",
        "import os\n",
        "base_dir = '/content/a'\n",
        "\n",
        "\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'test')"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGkhW4t5emYp"
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "# Gather data train\n",
        "train_data = []\n",
        "train_label = []\n",
        "for r, d, f in os.walk(train_dir):\n",
        "    for file in f:\n",
        "        if \".jpg\" in file:\n",
        "            imagePath = os.path.join(r, file)\n",
        "            image = cv2.imread(imagePath)\n",
        "            image = cv2.resize(image, (150,150))\n",
        "            train_data.append(image)\n",
        "            label = imagePath.split(os.path.sep)[-2]\n",
        "            train_label.append(label)\n",
        "\n",
        "train_data = np.array(train_data)\n",
        "train_label = np.array(train_label)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZthypNPeoYO"
      },
      "source": [
        "# Gather data validation\n",
        "val_data = []\n",
        "val_label = []\n",
        "for r, d, f in os.walk(validation_dir):\n",
        "    for file in f:\n",
        "        if \".jpg\" in file:\n",
        "            imagePath = os.path.join(r, file)\n",
        "            image = cv2.imread(imagePath)\n",
        "            image = cv2.resize(image, (150,150))\n",
        "            val_data.append(image)\n",
        "            label = imagePath.split(os.path.sep)[-2]\n",
        "            val_label.append(label)\n",
        "\n",
        "val_data = np.array(val_data)\n",
        "val_label = np.array(val_label)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8D1-BWkeqv2",
        "outputId": "9e3b887f-4f55-4f74-9678-c8461ac427eb"
      },
      "source": [
        "# Tampilkan shape dari data train dan data validation\n",
        "print(\"Train Data = \", train_data.shape)\n",
        "print(\"Train Label = \", train_label.shape)\n",
        "print(\"Validation Data = \", val_data.shape)\n",
        "print(\"Validation Label = \", val_label.shape)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Data =  (2637, 150, 150, 3)\n",
            "Train Label =  (2637,)\n",
            "Validation Data =  (660, 150, 150, 3)\n",
            "Validation Label =  (660,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SpqjZw2Oet8a",
        "outputId": "b2391d18-396c-4138-ed54-bc620e986389"
      },
      "source": [
        "# Normalisasi dataset\n",
        "print(\"Data sebelum di-normalisasi \", train_data[0][0][0])\n",
        "\n",
        "x_train = train_data.astype('float32') / 255.0\n",
        "x_val = val_data.astype('float32') / 255.0\n",
        "print(\"Data setelah di-normalisasi \", x_train[0][0][0])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data sebelum di-normalisasi  [22 12 29]\n",
            "Data setelah di-normalisasi  [0.08627451 0.04705882 0.11372549]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JHU4cZ0e8tR",
        "outputId": "44490af5-a5cd-4f31-836f-043c4a7d0143"
      },
      "source": [
        "# Transformasi label encoder\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "print(\"Label sebelum di-encoder \", train_label[995:2005])\n",
        "\n",
        "lb = LabelEncoder()\n",
        "y_train = lb.fit_transform(train_label)\n",
        "y_val = lb.fit_transform(val_label)\n",
        "\n",
        "print(\"Label setelah di-encoder \", y_train[995:2005])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label sebelum di-encoder  ['malignant' 'malignant' 'malignant' ... 'benign' 'benign' 'benign']\n",
            "Label setelah di-encoder  [1 1 1 ... 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bc2yHBNIe9iJ"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import InputLayer, Dense, Conv2D, MaxPool2D, Flatten\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "import tensorflow as tf"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKBgCCImfDo4"
      },
      "source": [
        "# Feature Extraction Layer\n",
        "model = Sequential()\n",
        "\n",
        "model.add(InputLayer(input_shape=[150,150,3]))\n",
        "model.add(Conv2D(filters=16, kernel_size=3, strides=1, padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2, padding='same'))\n",
        "model.add(Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2, padding='same'))\n",
        "model.add(Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu'))\n",
        "model.add(MaxPool2D(pool_size=2, padding='same'))\n",
        "model.add(Flatten())"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nHRV1cjjfFfy"
      },
      "source": [
        "# Fully Connected Layer\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QgS0A4wwfGvi",
        "outputId": "5923a501-1cfb-4cb9-c836-72d0ad8816be"
      },
      "source": [
        "# Print model summary\n",
        "print(model.summary())"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 150, 150, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 75, 75, 16)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 75, 75, 32)        4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 38, 38, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 38, 38, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 19, 19, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 23104)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               2957440   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,981,153\n",
            "Trainable params: 2,981,153\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sLZshCbTfg45"
      },
      "source": [
        "best_model_cancer_path = 'model-cancer.h5'\n",
        "checkpoint_callback = ModelCheckpoint(best_model_cancer_path,\n",
        "                                     monitor='val_acc',\n",
        "                                     save_best_only=True,\n",
        "                                     verbose=1)\n",
        "callbacks_list = [checkpoint_callback]"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkJhR636fmsB",
        "outputId": "e24a653d-83d4-4c59-8a13-5e6f88efcc43"
      },
      "source": [
        "# Compile model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "model.compile(optimizer=Adam(lr=0.001), \n",
        "              loss='binary_crossentropy', \n",
        "              metrics=['acc'])"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TdNZl-1mfIWt",
        "outputId": "b550c753-7124-4ace-831c-85d1185224b0"
      },
      "source": [
        "H = model.fit(x_train, y_train, batch_size=20, epochs=25, validation_data=(x_val, y_val))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/25\n",
            "132/132 [==============================] - 60s 445ms/step - loss: 0.5847 - acc: 0.6871 - val_loss: 0.5109 - val_acc: 0.7167\n",
            "Epoch 2/25\n",
            "132/132 [==============================] - 59s 444ms/step - loss: 0.4524 - acc: 0.7679 - val_loss: 0.4051 - val_acc: 0.7879\n",
            "Epoch 3/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.4236 - acc: 0.7941 - val_loss: 0.4057 - val_acc: 0.7985\n",
            "Epoch 4/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.4020 - acc: 0.8055 - val_loss: 0.4264 - val_acc: 0.7909\n",
            "Epoch 5/25\n",
            "132/132 [==============================] - 58s 441ms/step - loss: 0.3756 - acc: 0.8123 - val_loss: 0.3344 - val_acc: 0.8288\n",
            "Epoch 6/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.3280 - acc: 0.8415 - val_loss: 0.3668 - val_acc: 0.8091\n",
            "Epoch 7/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.3101 - acc: 0.8570 - val_loss: 0.3374 - val_acc: 0.8379\n",
            "Epoch 8/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.3134 - acc: 0.8578 - val_loss: 0.3555 - val_acc: 0.8167\n",
            "Epoch 9/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.2907 - acc: 0.8627 - val_loss: 0.3888 - val_acc: 0.8106\n",
            "Epoch 10/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.2768 - acc: 0.8631 - val_loss: 0.3259 - val_acc: 0.8530\n",
            "Epoch 11/25\n",
            "132/132 [==============================] - 58s 441ms/step - loss: 0.2459 - acc: 0.8866 - val_loss: 0.4336 - val_acc: 0.8030\n",
            "Epoch 12/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.2368 - acc: 0.8934 - val_loss: 0.3378 - val_acc: 0.8303\n",
            "Epoch 13/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.2237 - acc: 0.9029 - val_loss: 0.3722 - val_acc: 0.8288\n",
            "Epoch 14/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.1895 - acc: 0.9143 - val_loss: 0.3596 - val_acc: 0.8439\n",
            "Epoch 15/25\n",
            "132/132 [==============================] - 58s 441ms/step - loss: 0.1698 - acc: 0.9253 - val_loss: 0.4622 - val_acc: 0.8121\n",
            "Epoch 16/25\n",
            "132/132 [==============================] - 58s 441ms/step - loss: 0.1638 - acc: 0.9298 - val_loss: 0.3843 - val_acc: 0.8364\n",
            "Epoch 17/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.1520 - acc: 0.9344 - val_loss: 0.3993 - val_acc: 0.8348\n",
            "Epoch 18/25\n",
            "132/132 [==============================] - 59s 444ms/step - loss: 0.1217 - acc: 0.9499 - val_loss: 0.4682 - val_acc: 0.8530\n",
            "Epoch 19/25\n",
            "132/132 [==============================] - 59s 444ms/step - loss: 0.0948 - acc: 0.9587 - val_loss: 0.5499 - val_acc: 0.8045\n",
            "Epoch 20/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.1059 - acc: 0.9632 - val_loss: 0.5654 - val_acc: 0.8227\n",
            "Epoch 21/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.0704 - acc: 0.9754 - val_loss: 0.5963 - val_acc: 0.8485\n",
            "Epoch 22/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.0711 - acc: 0.9746 - val_loss: 0.5538 - val_acc: 0.8258\n",
            "Epoch 23/25\n",
            "132/132 [==============================] - 59s 444ms/step - loss: 0.0598 - acc: 0.9788 - val_loss: 0.6017 - val_acc: 0.8455\n",
            "Epoch 24/25\n",
            "132/132 [==============================] - 58s 443ms/step - loss: 0.0624 - acc: 0.9795 - val_loss: 0.5504 - val_acc: 0.8409\n",
            "Epoch 25/25\n",
            "132/132 [==============================] - 58s 442ms/step - loss: 0.0624 - acc: 0.9784 - val_loss: 0.6533 - val_acc: 0.8439\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Dxi12Pn-fpyk"
      },
      "source": [
        "model.save_weights('model-cancer.h5')"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39v6vv34frKb",
        "outputId": "85ce7f8e-3251-4204-8624-1b72cede79c5"
      },
      "source": [
        "model.get_weights()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[[[ 7.70905316e-02,  1.29209965e-01,  2.23758742e-01,\n",
              "           -2.01857295e-02,  2.16384262e-01,  1.02150448e-01,\n",
              "            6.63038343e-02,  9.42493379e-02,  1.07407019e-01,\n",
              "           -7.33551458e-02, -1.45518273e-01,  1.34346575e-01,\n",
              "            1.53587073e-01,  1.64419845e-01, -6.86876401e-02,\n",
              "           -1.86350361e-01],\n",
              "          [ 1.59484893e-01,  1.17437258e-01,  3.31883505e-02,\n",
              "           -9.42189470e-02, -9.83983651e-02, -1.14258014e-01,\n",
              "           -1.16820656e-01, -1.96005553e-01,  1.07260302e-01,\n",
              "            1.05681019e-02,  2.44445652e-02,  2.12877318e-01,\n",
              "           -5.32258414e-02, -1.63244382e-01,  2.73938850e-02,\n",
              "            2.08795935e-01],\n",
              "          [ 1.47330901e-02, -1.60998985e-01, -9.51431841e-02,\n",
              "           -8.97213966e-02,  7.72509053e-02,  9.39257592e-02,\n",
              "            1.29643008e-01,  1.53477311e-01,  4.12237123e-02,\n",
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              "            6.69955015e-02, -1.00065053e-01, -1.95716113e-01,\n",
              "            7.58933872e-02]],\n",
              " \n",
              "         [[ 5.07656373e-02, -4.03836928e-02,  1.43630460e-01,\n",
              "            3.07507589e-02,  1.76788628e-01,  3.02839633e-02,\n",
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              "           -1.69129714e-01, -7.20820129e-02, -8.10439587e-02,\n",
              "            1.69516161e-01, -3.84632386e-02, -4.17824872e-02,\n",
              "            5.78717589e-02],\n",
              "          [ 1.96281955e-01, -1.86808705e-02, -1.56988472e-01,\n",
              "           -1.46635041e-01, -5.01327589e-02, -2.03614488e-01,\n",
              "           -2.02936158e-01, -7.80572519e-02,  8.70272219e-02,\n",
              "            1.65315852e-01, -4.27696742e-02, -1.68104023e-01,\n",
              "            9.23471600e-02,  5.11977402e-03,  6.85431063e-02,\n",
              "            2.30936050e-01],\n",
              "          [-6.84958398e-02,  1.38207331e-01, -4.72923331e-02,\n",
              "           -1.57304153e-01,  2.66016312e-02,  8.77001733e-02,\n",
              "           -3.64425741e-02,  9.07172263e-02,  3.26854102e-02,\n",
              "           -1.49871990e-01,  1.39574260e-02, -2.06515402e-01,\n",
              "           -1.84262842e-01, -1.95011646e-02, -4.18725684e-02,\n",
              "           -1.70108184e-01]],\n",
              " \n",
              "         [[-1.18610300e-01, -2.89985817e-02,  1.50954798e-01,\n",
              "           -4.76869792e-02, -4.01009135e-02, -6.21019416e-02,\n",
              "            2.44193748e-02, -2.23527721e-04,  1.83081433e-01,\n",
              "            1.01495884e-01,  1.59505755e-01, -1.77136704e-01,\n",
              "           -9.15030986e-02,  1.58641905e-01, -1.53775781e-01,\n",
              "           -2.17117473e-01],\n",
              "          [ 2.02724516e-01,  8.03152844e-02,  2.04047095e-02,\n",
              "            9.47212707e-03,  1.35908052e-01, -2.37808838e-01,\n",
              "            1.58038139e-01, -1.23129293e-01,  6.54886514e-02,\n",
              "            9.89792868e-03,  1.99462086e-01,  1.10116624e-03,\n",
              "           -9.97405723e-02, -3.34342979e-02, -1.12689078e-01,\n",
              "           -1.22699467e-02],\n",
              "          [-2.58625187e-02,  2.02037096e-01,  2.88745090e-02,\n",
              "            1.09187104e-01,  3.64551954e-02,  4.59876843e-02,\n",
              "            1.08571604e-01,  1.65037632e-01, -3.59940976e-02,\n",
              "            2.93823443e-02, -1.32623553e-01, -1.87451094e-01,\n",
              "            8.56619924e-02, -9.08644870e-02,  8.65333155e-02,\n",
              "            1.35866418e-01]]],\n",
              " \n",
              " \n",
              "        [[[-2.36336842e-01, -1.87710389e-01,  1.05649769e-01,\n",
              "            1.61476005e-02,  1.53285593e-01,  2.51284316e-02,\n",
              "           -1.32452205e-01,  8.12648684e-02, -1.81014106e-01,\n",
              "           -8.53018984e-02, -5.88438357e-04, -3.64998728e-02,\n",
              "            1.55241728e-01,  1.63451359e-01, -1.05071157e-01,\n",
              "           -1.74518228e-01],\n",
              "          [ 4.87592909e-03,  8.08195248e-02,  1.58874214e-01,\n",
              "           -3.64551693e-03, -1.02729402e-01,  1.13413692e-01,\n",
              "           -1.15063228e-01, -1.47429079e-01, -7.49740843e-03,\n",
              "           -5.93440756e-02,  4.18353302e-04,  2.17856541e-01,\n",
              "           -1.68834671e-01,  1.20901436e-01,  5.76794967e-02,\n",
              "            1.15836635e-01],\n",
              "          [-1.52761519e-01, -8.39049369e-02,  1.70005113e-01,\n",
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              "           -5.96769899e-02,  3.18396790e-03,  5.54463938e-02,\n",
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              "           -1.46351710e-01, -9.59043503e-02, -1.92201778e-01,\n",
              "            1.30266592e-01]],\n",
              " \n",
              "         [[-1.39221117e-01,  3.72225605e-02,  8.12759399e-02,\n",
              "           -1.82084829e-01,  1.73028037e-01,  3.36813405e-02,\n",
              "            4.93100807e-02, -1.08023398e-02,  3.62883396e-02,\n",
              "           -9.30593908e-02,  8.87340978e-02, -1.23122692e-04,\n",
              "            8.22097361e-02, -5.49568310e-02,  1.39903843e-01,\n",
              "           -1.86229974e-01],\n",
              "          [ 6.07077852e-02, -8.09575766e-02, -3.91170681e-02,\n",
              "           -1.29340634e-01, -5.35488420e-04,  1.14230672e-02,\n",
              "            1.44860759e-01, -3.83455195e-02,  1.51136547e-01,\n",
              "           -1.34503797e-01, -1.50542155e-01,  2.41235401e-02,\n",
              "           -1.38518855e-01,  1.31758288e-01,  1.17835097e-01,\n",
              "            1.32239431e-01],\n",
              "          [ 1.06839411e-01,  1.30176947e-01, -9.24240425e-02,\n",
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              "           -1.56181097e-01,  9.30962712e-03,  4.69064564e-02,\n",
              "            7.23407045e-02,  2.30514854e-02,  8.09104294e-02,\n",
              "            1.06241569e-01,  1.02233179e-01,  1.63867772e-01,\n",
              "            1.60425693e-01]],\n",
              " \n",
              "         [[ 9.46565568e-02, -2.02031150e-01, -1.77751675e-01,\n",
              "           -8.63075778e-02, -9.61150229e-02, -6.67818487e-02,\n",
              "            2.12214485e-01, -9.05390829e-02,  1.69547394e-01,\n",
              "            1.96033865e-01,  2.00465143e-01,  1.47571921e-01,\n",
              "            3.32169384e-02,  3.11546437e-02,  6.37465417e-02,\n",
              "           -3.09193105e-01],\n",
              "          [ 9.80995297e-02,  7.87665099e-02, -2.09384874e-01,\n",
              "            1.73041016e-01, -1.78558424e-01, -2.59932503e-02,\n",
              "            3.90192643e-02,  3.67086120e-02, -1.27728790e-01,\n",
              "           -7.19260648e-02,  1.86090663e-01,  2.49781236e-02,\n",
              "           -1.59857377e-01,  5.38941137e-02, -1.90560631e-02,\n",
              "           -1.42768010e-01],\n",
              "          [ 1.56511232e-01,  1.16066048e-02,  6.69671223e-03,\n",
              "            8.66630077e-02,  7.18315989e-02, -9.58260335e-03,\n",
              "            1.64732456e-01, -3.35732140e-02,  1.20983884e-01,\n",
              "           -1.62642691e-02,  1.17426224e-01, -1.50176391e-01,\n",
              "           -1.05324373e-01, -1.68449908e-01,  4.18573618e-02,\n",
              "           -5.97085506e-02]]],\n",
              " \n",
              " \n",
              "        [[[-1.45940512e-01, -2.09101930e-01,  1.34232556e-02,\n",
              "            2.14811400e-01,  2.49579996e-01,  1.66601501e-02,\n",
              "            1.50430903e-01,  2.06786364e-01, -2.96223983e-02,\n",
              "           -4.15317416e-02, -4.20642234e-02,  3.39541808e-02,\n",
              "            1.69595420e-01,  3.63688320e-02, -1.70484900e-01,\n",
              "           -2.31182724e-01],\n",
              "          [ 1.18535617e-02, -1.28926322e-01, -8.69172886e-02,\n",
              "           -1.64895635e-02, -8.46305490e-03, -4.07566987e-02,\n",
              "            1.03412360e-01, -1.48807809e-01, -8.41116160e-02,\n",
              "           -4.35623117e-02,  1.21680796e-01,  4.06981036e-02,\n",
              "           -1.87692747e-01,  8.34697392e-03,  2.01921418e-01,\n",
              "           -5.54724522e-02],\n",
              "          [-5.30645549e-02, -1.69531062e-01, -1.03855953e-01,\n",
              "           -5.45171276e-02, -1.04226783e-01,  1.43092334e-01,\n",
              "            1.02183886e-01, -1.12274289e-01, -2.51381516e-01,\n",
              "           -6.46465793e-02, -1.63934901e-02,  1.67482629e-01,\n",
              "            7.80914386e-04,  1.47231296e-01,  1.32452190e-01,\n",
              "            3.86076011e-02]],\n",
              " \n",
              "         [[-2.08731622e-01,  3.85584086e-02, -1.06189467e-01,\n",
              "           -4.04512845e-02,  1.45514429e-01, -1.01124786e-01,\n",
              "            1.57181192e-02,  1.48301631e-01,  1.98648162e-02,\n",
              "           -7.09424317e-02,  1.50964186e-01,  2.07873747e-01,\n",
              "           -1.49777919e-01,  2.31583834e-01,  5.53397126e-02,\n",
              "           -2.16541067e-01],\n",
              "          [ 1.41264990e-01,  4.32306044e-02, -1.40001038e-02,\n",
              "            1.27865449e-01, -9.97749642e-02, -2.09273323e-02,\n",
              "            1.30842790e-01,  1.30333871e-01,  1.37101248e-01,\n",
              "           -2.02555191e-02,  1.80145249e-01, -6.61868602e-02,\n",
              "            1.75545588e-02, -7.25141019e-02,  1.90076619e-01,\n",
              "            1.27461970e-01],\n",
              "          [ 1.33287042e-01, -1.32715523e-01, -5.71940504e-02,\n",
              "           -1.36097386e-01, -2.78998092e-02,  1.24870427e-01,\n",
              "           -8.51424485e-02,  1.46711931e-01, -6.33024052e-02,\n",
              "           -1.87197566e-01, -4.64038067e-02, -1.49556007e-02,\n",
              "           -2.16945082e-01,  6.41438738e-02, -2.81534791e-02,\n",
              "            8.84755030e-02]],\n",
              " \n",
              "         [[ 3.43637057e-02, -5.76840192e-02, -2.30870903e-01,\n",
              "            7.72024393e-02,  1.06434025e-01, -1.61699131e-01,\n",
              "           -1.11266427e-01,  7.53656924e-02, -4.47009836e-04,\n",
              "            1.19308643e-01, -6.35414794e-02, -1.64609000e-01,\n",
              "            1.00426205e-01,  1.02917738e-01, -1.55270413e-01,\n",
              "           -2.77129948e-01],\n",
              "          [ 1.97375402e-01, -5.58515452e-02,  2.88727097e-02,\n",
              "            1.63812771e-01, -5.21700494e-02, -1.07447309e-02,\n",
              "           -2.70513669e-02,  7.07343072e-02,  1.37997657e-01,\n",
              "            7.06263930e-02, -7.94801861e-02,  9.97934863e-02,\n",
              "           -2.04459473e-01,  1.66646644e-01,  4.08285670e-02,\n",
              "           -4.06031422e-02],\n",
              "          [-1.18920557e-01, -1.09061845e-01,  3.38302180e-02,\n",
              "            1.85007885e-01, -1.41696170e-01,  1.06092460e-01,\n",
              "           -5.52089885e-02, -5.71416952e-02,  8.76545161e-02,\n",
              "            1.93535715e-01, -1.37711331e-01,  2.01875009e-02,\n",
              "            7.49781951e-02, -1.53934181e-01, -7.79033601e-02,\n",
              "           -1.12163067e-01]]]], dtype=float32),\n",
              " array([ 0.00696344, -0.00219355,  0.01270607,  0.06176405, -0.07696187,\n",
              "        -0.03564663, -0.14430922, -0.09945951, -0.14950508,  0.00843341,\n",
              "         0.02798396,  0.03749874,  0.05311888, -0.02389732,  0.05412957,\n",
              "         0.05325996], dtype=float32),\n",
              " array([[[[-8.48317612e-03, -2.32997034e-02,  8.31929445e-02, ...,\n",
              "           -7.05570057e-02, -2.28161998e-02, -7.00512752e-02],\n",
              "          [-8.87948647e-02,  5.38850203e-02,  2.19972908e-01, ...,\n",
              "            2.50300933e-02, -6.37899190e-02, -1.27613982e-02],\n",
              "          [-6.17837757e-02,  1.73672467e-01,  1.24372356e-01, ...,\n",
              "            9.41122249e-02,  2.49954015e-01,  1.20073698e-01],\n",
              "          ...,\n",
              "          [ 6.18314324e-03,  3.81791219e-02, -1.26135692e-01, ...,\n",
              "            1.38010412e-01, -5.85228316e-02,  7.00092455e-03],\n",
              "          [ 4.07644100e-02, -4.98554446e-02, -5.04226349e-02, ...,\n",
              "            7.59883821e-02, -1.00156583e-01,  1.04137607e-01],\n",
              "          [-2.99872220e-01,  1.52194589e-01,  2.18061969e-01, ...,\n",
              "           -8.14339072e-02,  3.00714433e-01, -6.85951561e-02]],\n",
              " \n",
              "         [[-7.79487342e-02, -1.56944394e-01,  3.99058759e-02, ...,\n",
              "           -2.39183288e-02,  5.43096699e-02,  1.49902441e-02],\n",
              "          [-1.07628249e-01, -8.73182807e-03,  3.19526047e-02, ...,\n",
              "           -6.58669174e-02,  1.36208758e-01, -1.26055822e-01],\n",
              "          [ 8.20873454e-02,  1.34570539e-01,  2.20993802e-01, ...,\n",
              "            8.95185620e-02,  2.76284546e-01,  5.87259941e-02],\n",
              "          ...,\n",
              "          [ 1.81325793e-01, -9.18492824e-02, -1.55537635e-01, ...,\n",
              "            9.78247672e-02, -1.15083247e-01,  1.39875755e-01],\n",
              "          [ 4.79826964e-02,  5.97431965e-04,  2.06500851e-03, ...,\n",
              "            1.40230343e-01, -8.33644047e-02,  1.91886127e-01],\n",
              "          [ 9.74404742e-04, -3.29911932e-02,  2.13616922e-01, ...,\n",
              "            6.13637790e-02,  1.94612786e-01,  2.58419484e-01]],\n",
              " \n",
              "         [[-1.61723480e-01, -7.41386414e-02, -2.46682949e-02, ...,\n",
              "           -4.73624468e-02, -4.37388904e-02, -1.39326110e-01],\n",
              "          [-2.25292370e-02,  4.40722890e-02,  3.75653982e-01, ...,\n",
              "            2.39163619e-02,  1.70481637e-01, -2.37261541e-02],\n",
              "          [ 6.57130852e-02,  6.66270256e-02,  1.41877711e-01, ...,\n",
              "            9.60196406e-02, -1.09185845e-01, -4.56185006e-02],\n",
              "          ...,\n",
              "          [ 3.97084700e-03,  1.33577744e-02,  3.72597910e-02, ...,\n",
              "            4.38084602e-02, -5.08478060e-02,  7.39829615e-02],\n",
              "          [-1.71937791e-04, -1.18371770e-01,  5.88383153e-02, ...,\n",
              "            5.46490848e-02, -8.63009170e-02, -1.12897582e-01],\n",
              "          [-7.04738544e-03, -1.00614384e-01,  6.08650874e-03, ...,\n",
              "            8.49543884e-02,  1.83432952e-01, -8.73064846e-02]]],\n",
              " \n",
              " \n",
              "        [[[ 4.65487242e-02, -8.20358396e-02, -7.59635493e-02, ...,\n",
              "           -9.80595723e-02,  9.84079838e-02,  1.23523131e-01],\n",
              "          [ 9.24783647e-02, -1.41544521e-01, -8.28353092e-02, ...,\n",
              "            8.54310319e-02,  4.70387097e-03, -3.33648585e-02],\n",
              "          [ 5.89032769e-02, -2.34286673e-02,  2.12029628e-02, ...,\n",
              "            1.38587698e-01,  6.68895319e-02, -2.89127547e-02],\n",
              "          ...,\n",
              "          [-3.41333598e-02, -5.49446717e-02, -1.53436422e-01, ...,\n",
              "           -1.08232517e-02, -9.35207382e-02,  1.01782188e-01],\n",
              "          [-2.54593845e-02, -1.66036114e-01,  4.71492670e-02, ...,\n",
              "           -1.09984301e-01,  1.00019425e-01,  9.77517739e-02],\n",
              "          [-2.32961953e-01, -5.36112711e-02,  3.08041275e-01, ...,\n",
              "           -3.10750782e-01,  3.50006878e-01, -9.68581662e-02]],\n",
              " \n",
              "         [[ 3.34375612e-02, -1.06360503e-01, -1.38751373e-01, ...,\n",
              "            5.13368063e-02, -5.70448451e-02,  4.47186492e-02],\n",
              "          [ 8.43688250e-02, -1.30152293e-02,  6.18372671e-03, ...,\n",
              "           -3.55129205e-02,  1.11152090e-01, -1.33990064e-01],\n",
              "          [ 9.69896838e-03,  2.85800677e-02, -8.67804587e-02, ...,\n",
              "            1.84736952e-01,  7.88862631e-02, -9.24563408e-03],\n",
              "          ...,\n",
              "          [ 4.98447008e-02,  7.07458779e-02, -1.25143275e-01, ...,\n",
              "            3.09291366e-03, -3.96276526e-02, -7.87804499e-02],\n",
              "          [ 6.37547225e-02,  2.90725995e-02, -2.46758349e-02, ...,\n",
              "           -4.62571867e-02, -1.77441984e-02, -4.17283103e-02],\n",
              "          [ 1.47589400e-01,  4.01931331e-02,  2.35737786e-02, ...,\n",
              "            1.73845008e-01,  2.22432777e-01,  2.36014232e-01]],\n",
              " \n",
              "         [[-1.75550714e-01, -1.27803043e-01,  8.16190690e-02, ...,\n",
              "           -7.45366290e-02,  1.03448778e-01,  5.50611364e-03],\n",
              "          [-8.12630802e-02,  1.19710706e-01,  1.89873785e-01, ...,\n",
              "            6.80383220e-02,  3.99534740e-02, -1.38795341e-03],\n",
              "          [ 2.49572992e-02, -9.90499854e-02,  7.95435831e-02, ...,\n",
              "            1.61090493e-01, -1.76376980e-02, -6.86166361e-02],\n",
              "          ...,\n",
              "          [ 9.61649343e-02, -9.38765407e-02, -3.89537252e-02, ...,\n",
              "           -8.36041383e-03,  1.40196815e-01, -1.00895040e-01],\n",
              "          [-8.46016407e-02, -8.42873007e-02,  1.79594383e-01, ...,\n",
              "           -9.68111083e-02,  1.48378059e-01, -1.17504999e-01],\n",
              "          [-4.87394221e-02,  3.33929472e-02, -8.12138468e-02, ...,\n",
              "            8.80596042e-02,  1.90115675e-01,  4.43591066e-02]]],\n",
              " \n",
              " \n",
              "        [[[-8.63028392e-02, -2.43435577e-01, -1.70424387e-01, ...,\n",
              "           -1.07502267e-01,  1.09150879e-01, -1.52198235e-02],\n",
              "          [-6.85561635e-03,  1.87913934e-03, -3.90284993e-02, ...,\n",
              "            7.90705159e-02,  3.69404107e-02, -3.92584801e-02],\n",
              "          [-7.85833225e-02,  3.05424333e-02,  9.57693532e-03, ...,\n",
              "           -1.77175440e-02, -1.46174833e-01, -1.13150142e-02],\n",
              "          ...,\n",
              "          [ 1.52383253e-01,  7.04125464e-02, -1.13418564e-01, ...,\n",
              "            7.14448020e-02, -9.45951343e-02, -9.18971524e-02],\n",
              "          [-7.98021033e-02, -7.92719051e-02,  5.93783967e-02, ...,\n",
              "           -8.88704583e-02, -2.32488532e-02, -7.45980255e-03],\n",
              "          [-2.52832919e-01,  2.80296132e-02,  2.42729604e-01, ...,\n",
              "            5.00099100e-02,  1.36407241e-01,  1.93346199e-02]],\n",
              " \n",
              "         [[-3.92540954e-02, -1.42389471e-02, -2.80109290e-02, ...,\n",
              "           -7.17941895e-02,  9.25804079e-02,  2.10924633e-03],\n",
              "          [-8.05192813e-02,  2.12060008e-02, -2.40796618e-02, ...,\n",
              "            3.59803289e-02,  9.20438021e-02,  8.78769755e-02],\n",
              "          [ 1.61012664e-01, -1.18815139e-01, -3.21592577e-02, ...,\n",
              "            6.32900000e-02, -2.28187650e-01,  3.81135382e-03],\n",
              "          ...,\n",
              "          [ 2.04111934e-01,  1.23803310e-01, -7.78753161e-02, ...,\n",
              "            1.74690872e-01, -6.33241758e-02,  6.18048944e-02],\n",
              "          [-1.07387118e-01, -1.40647367e-01, -1.31900996e-01, ...,\n",
              "           -9.71071944e-02, -4.99642193e-02, -3.12577933e-02],\n",
              "          [-3.50877945e-03, -1.91019788e-01,  4.03487422e-02, ...,\n",
              "            2.41520748e-01,  2.05615819e-01,  5.03806323e-02]],\n",
              " \n",
              "         [[-7.68006444e-02, -6.10354654e-02, -7.63663426e-02, ...,\n",
              "           -1.20513169e-02,  2.82581896e-02,  8.79162923e-02],\n",
              "          [-1.17460862e-01, -3.22214067e-02,  1.35188490e-01, ...,\n",
              "           -6.32889569e-02,  9.01836623e-03, -1.03250012e-01],\n",
              "          [ 1.35601342e-01,  8.71423110e-02,  9.12365764e-02, ...,\n",
              "            7.26991072e-02, -1.69379920e-01, -1.80476215e-02],\n",
              "          ...,\n",
              "          [ 1.47448212e-01, -5.75119592e-02,  1.12696417e-01, ...,\n",
              "            1.26470411e-02, -3.74142677e-02, -1.55084223e-01],\n",
              "          [-1.52706444e-01, -1.60231367e-01,  2.08376601e-01, ...,\n",
              "           -5.68053275e-02,  4.68361191e-02,  7.03097284e-02],\n",
              "          [-6.33252645e-03,  4.88154888e-02,  3.66912000e-02, ...,\n",
              "            6.91316202e-02,  2.68808365e-01,  4.44949195e-02]]]],\n",
              "       dtype=float32),\n",
              " array([-0.08081427, -0.02093383, -0.03061242, -0.0407122 , -0.0140553 ,\n",
              "        -0.03268505, -0.0111749 ,  0.01731139, -0.07736085,  0.08449597,\n",
              "        -0.00486363, -0.00343953,  0.05861153, -0.05779726, -0.0314063 ,\n",
              "        -0.01634272, -0.00054584, -0.0369872 , -0.0332587 , -0.02274591,\n",
              "        -0.00326122, -0.00863391, -0.02900547,  0.06462129,  0.04724913,\n",
              "        -0.02364713, -0.03465812,  0.04652352, -0.03951702, -0.03064288,\n",
              "        -0.0062973 , -0.00071389], dtype=float32),\n",
              " array([[[[-1.02173656e-01, -1.05932780e-01,  1.25701964e-01, ...,\n",
              "           -1.15652038e-02, -6.93928525e-02,  4.60828990e-02],\n",
              "          [-4.68279906e-02,  2.75928508e-02,  3.25649581e-03, ...,\n",
              "            2.88841221e-02,  2.77817324e-02,  8.53063986e-02],\n",
              "          [-6.24305196e-03, -1.36206210e-01,  7.41477162e-02, ...,\n",
              "           -6.35697097e-02, -3.56067419e-02,  8.51603597e-02],\n",
              "          ...,\n",
              "          [-7.97186643e-02,  2.29003206e-02,  1.56093240e-01, ...,\n",
              "           -2.20952388e-02, -1.13275483e-01, -7.46149868e-02],\n",
              "          [-4.37108278e-02,  2.61017531e-02,  5.18896095e-02, ...,\n",
              "           -2.58709863e-02, -3.77509668e-02, -5.64288124e-02],\n",
              "          [ 5.65714575e-03,  5.32970987e-02, -5.84829822e-02, ...,\n",
              "           -6.85183555e-02, -1.83630516e-04, -4.38269638e-02]],\n",
              " \n",
              "         [[-1.37003288e-01, -5.77383339e-02, -1.51780779e-02, ...,\n",
              "           -9.65913236e-02, -3.15689892e-02,  6.37333021e-02],\n",
              "          [ 1.71814766e-02,  6.56178519e-02,  1.35063708e-01, ...,\n",
              "            1.51929613e-02,  1.87487639e-02,  4.34896909e-02],\n",
              "          [-2.88488232e-02, -4.45602909e-02,  6.80400282e-02, ...,\n",
              "           -1.18123703e-01,  1.40998796e-01, -1.13642730e-01],\n",
              "          ...,\n",
              "          [-1.92338899e-02, -1.58250425e-02, -5.91992699e-02, ...,\n",
              "           -9.56003293e-02,  2.18185801e-02, -7.54636228e-02],\n",
              "          [-5.13674840e-02, -1.31327257e-01,  1.47177279e-01, ...,\n",
              "           -6.94454089e-02,  5.36267571e-02, -9.94276032e-02],\n",
              "          [ 3.33043076e-02, -7.08682761e-02, -4.70418073e-02, ...,\n",
              "           -6.15387484e-02, -2.10116562e-02,  1.34471394e-02]],\n",
              " \n",
              "         [[ 2.37676222e-02, -4.02014330e-03, -8.07991326e-02, ...,\n",
              "           -5.28470203e-02,  1.80408894e-03,  4.48193625e-02],\n",
              "          [-3.53758689e-03, -9.32234153e-02,  8.56838301e-02, ...,\n",
              "            1.39869126e-02,  1.28792203e-03, -9.82815400e-02],\n",
              "          [-6.44589141e-02, -1.03201926e-01,  1.38826981e-01, ...,\n",
              "            2.28077881e-02, -8.64811763e-02,  8.39472339e-02],\n",
              "          ...,\n",
              "          [-9.27323326e-02,  2.01263223e-02, -4.44331951e-02, ...,\n",
              "           -4.83287387e-02, -4.45438176e-02, -2.40227543e-02],\n",
              "          [-2.23398618e-02, -8.62640236e-03,  1.67053044e-02, ...,\n",
              "           -9.34330001e-02,  9.72130150e-02, -6.62740991e-02],\n",
              "          [ 5.42819910e-02, -9.91681591e-02, -3.79649140e-02, ...,\n",
              "           -6.01812638e-02,  7.12231770e-02, -3.01946457e-02]]],\n",
              " \n",
              " \n",
              "        [[[-9.85980332e-02, -1.07739605e-01,  2.00775508e-02, ...,\n",
              "            1.89051777e-02, -1.18242204e-01,  4.52206470e-02],\n",
              "          [-7.64242038e-02,  7.80586153e-02, -4.98245144e-03, ...,\n",
              "            4.48320583e-02, -6.46225289e-02,  2.33943462e-02],\n",
              "          [ 3.17993835e-02, -1.54756838e-02, -2.14800853e-02, ...,\n",
              "           -1.48328478e-02, -5.17900707e-03,  2.08376627e-02],\n",
              "          ...,\n",
              "          [-7.92724825e-03, -1.11503512e-01, -2.89091971e-02, ...,\n",
              "           -3.33392695e-02, -5.11946231e-02, -4.20144983e-02],\n",
              "          [-1.90758845e-03,  3.12871449e-02, -1.05020292e-02, ...,\n",
              "            3.62761654e-02, -1.31355133e-02, -2.38544252e-02],\n",
              "          [ 6.39815778e-02,  2.49352790e-02,  6.36659861e-02, ...,\n",
              "           -9.68605727e-02, -1.98008865e-02, -7.10029155e-02]],\n",
              " \n",
              "         [[-2.08141319e-02, -6.26940504e-02, -4.00530957e-02, ...,\n",
              "           -4.07039560e-02,  1.87976658e-02,  4.84433137e-02],\n",
              "          [-4.78069410e-02, -2.65320148e-02,  9.92198139e-02, ...,\n",
              "            8.51299241e-03, -9.39421877e-02, -4.71090199e-03],\n",
              "          [-1.78201851e-02, -1.08278193e-01,  5.59381060e-02, ...,\n",
              "           -1.04009680e-01, -9.85066891e-02,  8.20989311e-02],\n",
              "          ...,\n",
              "          [-9.16776434e-02, -1.32406747e-03,  7.61817396e-02, ...,\n",
              "           -4.55091298e-02,  1.48788793e-02,  1.76507030e-02],\n",
              "          [-4.16541584e-02, -9.71020386e-02,  9.78303626e-02, ...,\n",
              "           -8.81493092e-02,  6.33389223e-03, -7.84192681e-02],\n",
              "          [-2.98757739e-02, -6.81647733e-02, -6.42239302e-02, ...,\n",
              "            1.83026604e-02, -1.08878501e-02, -2.64553446e-02]],\n",
              " \n",
              "         [[-1.60742160e-02, -4.76193726e-02,  2.15877686e-02, ...,\n",
              "           -1.43895410e-02, -1.00533620e-01,  4.10999171e-02],\n",
              "          [ 1.42753765e-01,  3.92486155e-02,  6.85541183e-02, ...,\n",
              "           -6.54261839e-03, -7.76986554e-02, -7.06257820e-02],\n",
              "          [-2.05103867e-02, -7.22305328e-02,  1.39848545e-01, ...,\n",
              "           -8.43305364e-02, -4.33039889e-02, -9.78441603e-05],\n",
              "          ...,\n",
              "          [-4.60756607e-02, -8.47455934e-02, -7.32857361e-02, ...,\n",
              "           -6.74121231e-02, -5.78310490e-02, -1.02424391e-01],\n",
              "          [-9.87827256e-02, -1.27257481e-01, -4.87913340e-02, ...,\n",
              "           -3.97571623e-02,  5.95738962e-02, -1.61004812e-02],\n",
              "          [ 1.76048912e-02, -8.61415714e-02,  3.34090032e-02, ...,\n",
              "           -1.41785480e-02,  1.88168930e-03, -3.96700092e-02]]],\n",
              " \n",
              " \n",
              "        [[[ 7.90921040e-03, -6.91732764e-02, -6.93351030e-02, ...,\n",
              "           -6.60405532e-02, -6.00982495e-02, -6.30733296e-02],\n",
              "          [-3.27416174e-02, -8.73694494e-02, -6.80040277e-04, ...,\n",
              "            6.24819249e-02,  5.98129407e-02,  6.10874407e-02],\n",
              "          [-2.50774994e-03, -1.22022793e-01, -6.85117766e-02, ...,\n",
              "            2.06093397e-03, -1.09360337e-01, -2.17016824e-02],\n",
              "          ...,\n",
              "          [-9.74907950e-02, -6.00143671e-02,  6.08253712e-03, ...,\n",
              "           -4.89548706e-02, -3.25822495e-02,  5.70080653e-02],\n",
              "          [ 1.78599134e-02,  6.21398957e-03,  5.94765618e-02, ...,\n",
              "           -1.04074709e-01,  6.76736236e-02, -1.20351493e-01],\n",
              "          [-7.90608004e-02, -1.26523208e-02, -5.45807071e-02, ...,\n",
              "           -1.13685995e-01, -7.95314312e-02,  3.10590230e-02]],\n",
              " \n",
              "         [[-2.17854884e-02,  4.32304479e-02,  1.56877059e-02, ...,\n",
              "            3.86713445e-02, -2.11656913e-02, -5.76585643e-02],\n",
              "          [-2.43602321e-02,  5.77275418e-02,  1.15491875e-01, ...,\n",
              "            9.35502574e-02, -5.25422134e-02, -7.17356876e-02],\n",
              "          [-6.74115494e-02, -9.93892848e-02,  2.51064673e-02, ...,\n",
              "           -2.31501117e-01, -7.28328619e-03, -9.54174027e-02],\n",
              "          ...,\n",
              "          [-6.84202611e-02, -4.06259000e-02, -2.70963423e-02, ...,\n",
              "           -3.95439379e-02,  7.78174326e-02, -3.53365764e-02],\n",
              "          [-9.53167975e-02, -1.20268650e-01,  1.30689472e-01, ...,\n",
              "            1.89360641e-02,  3.32573950e-02, -1.18432781e-02],\n",
              "          [ 8.92973132e-03, -3.69338393e-02,  5.55186160e-02, ...,\n",
              "           -6.13273121e-02, -1.38118668e-02, -6.65582865e-02]],\n",
              " \n",
              "         [[-3.47128399e-02, -3.11935972e-02, -2.73063779e-02, ...,\n",
              "           -7.79530630e-02,  3.00708553e-03, -1.79791283e-02],\n",
              "          [ 1.01745769e-01, -1.57807879e-02,  1.17764771e-01, ...,\n",
              "           -1.69447977e-02, -8.54048356e-02,  3.36551070e-02],\n",
              "          [ 7.02678561e-02, -4.11149301e-02,  1.62345022e-01, ...,\n",
              "           -1.09535597e-01,  6.39501438e-02, -4.44393717e-02],\n",
              "          ...,\n",
              "          [-8.52312893e-02,  4.09108996e-02, -8.33350047e-02, ...,\n",
              "           -1.12313360e-01,  7.37804249e-02, -2.93652192e-02],\n",
              "          [-3.48653570e-02, -4.94027659e-02, -6.53742626e-02, ...,\n",
              "           -2.34433915e-02,  9.63072851e-02, -8.54944587e-02],\n",
              "          [-8.61514434e-02, -8.12493041e-02,  4.44241278e-02, ...,\n",
              "           -5.78211471e-02,  1.29756983e-02,  4.96266335e-02]]]],\n",
              "       dtype=float32),\n",
              " array([-0.03888318, -0.0348325 , -0.04182116, -0.00593415,  0.02659478,\n",
              "        -0.05121908, -0.03233659, -0.03042451, -0.02326152,  0.08960776,\n",
              "        -0.01750954,  0.03858132, -0.04548262,  0.01577677, -0.06463865,\n",
              "        -0.05825369,  0.02114473, -0.0390972 , -0.02634859, -0.04070187,\n",
              "         0.01046749, -0.01235873, -0.0491981 ,  0.06095937, -0.05523831,\n",
              "        -0.05211185, -0.00779073, -0.02298949, -0.01079017, -0.04091796,\n",
              "        -0.01966484, -0.02002927, -0.05873943, -0.02269845, -0.01779303,\n",
              "         0.00692049, -0.03506014, -0.0693654 , -0.0787046 ,  0.03829757,\n",
              "        -0.05494148, -0.00742971,  0.0027284 , -0.08855398, -0.03861311,\n",
              "        -0.02397369, -0.00949701, -0.02741031,  0.07200556, -0.0621176 ,\n",
              "        -0.01709619, -0.02427014,  0.00559815, -0.07666358, -0.06303186,\n",
              "        -0.03177454, -0.05126363, -0.04558795, -0.02381243,  0.0221574 ,\n",
              "        -0.03165986, -0.0355063 , -0.03347143, -0.04310358], dtype=float32),\n",
              " array([[ 0.0287059 , -0.01578161, -0.00748343, ..., -0.01905515,\n",
              "         -0.00200202,  0.01225083],\n",
              "        [ 0.02124998,  0.01191788, -0.0077939 , ..., -0.01266738,\n",
              "         -0.01553666, -0.00663071],\n",
              "        [ 0.20392972, -0.10794146,  0.01611864, ..., -0.1348639 ,\n",
              "         -0.03692361,  0.009801  ],\n",
              "        ...,\n",
              "        [ 0.01134088, -0.00163209,  0.00576181, ..., -0.0251031 ,\n",
              "         -0.03188619, -0.00047988],\n",
              "        [ 0.08894749, -0.08358321, -0.00312748, ..., -0.08071917,\n",
              "         -0.02609809, -0.00500113],\n",
              "        [ 0.01345218, -0.00347972, -0.00326143, ..., -0.00185432,\n",
              "         -0.01014252,  0.00344492]], dtype=float32),\n",
              " array([-1.66473456e-03,  4.86080199e-02, -9.43757314e-03, -2.16129213e-03,\n",
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              "        -9.58344340e-03, -5.71819302e-03, -1.32126035e-02, -3.27130817e-02,\n",
              "        -1.00434814e-02,  6.45650507e-05, -7.18192458e-02, -9.95208230e-03,\n",
              "        -8.54357239e-03, -1.71650629e-02,  1.03562605e-02,  5.53064235e-02,\n",
              "        -1.59263406e-02, -1.75060406e-02,  4.75508608e-02, -7.82867149e-03,\n",
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              "        -2.50704177e-02, -6.00359915e-03,  1.38089666e-03, -3.36823403e-03,\n",
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              "         3.11095249e-02, -9.51769575e-03, -4.31386335e-03, -9.81671177e-03,\n",
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              "        -1.37747480e-02, -1.14030717e-02, -2.13601515e-02,  9.35398228e-03,\n",
              "        -1.71780903e-02, -1.16899498e-02,  3.30226645e-02, -1.78097654e-02,\n",
              "         7.32126599e-03, -5.55854756e-03, -9.26049799e-03, -3.01654935e-02,\n",
              "         4.58003432e-02, -3.05284616e-02,  4.12745913e-03, -6.00018026e-03,\n",
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              "        -2.47734804e-02, -5.10992296e-03, -8.41449201e-03, -1.72067508e-02,\n",
              "         5.90012372e-02, -8.23897496e-03,  8.23961869e-02, -1.07669691e-02,\n",
              "         6.55713901e-02, -3.86623340e-03, -2.38110162e-02, -5.33480057e-03,\n",
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              "        -4.60113771e-02,  6.11626729e-02, -8.57533421e-03, -2.91304966e-03,\n",
              "         1.61830522e-02, -1.48341954e-02, -5.55839855e-03, -5.70456311e-03,\n",
              "         2.84416862e-02, -3.39976349e-03, -7.60926306e-03, -5.85823366e-03,\n",
              "         4.92620133e-02, -5.11223124e-03,  7.74715692e-02, -9.51665267e-03,\n",
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              "        -1.64511781e-02, -3.31666432e-02, -5.98452520e-03, -1.00527285e-02,\n",
              "         3.96162160e-02, -3.42129171e-02,  5.99826016e-02, -5.33280708e-03,\n",
              "         1.05966628e-02,  5.67638837e-02,  2.43452545e-02,  1.88386701e-02,\n",
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              "        -5.33588417e-03,  3.36677046e-03, -2.96440516e-02, -3.67876366e-02,\n",
              "        -5.34317084e-03,  5.05393408e-02,  6.13258081e-03, -9.34353471e-03],\n",
              "       dtype=float32),\n",
              " array([[-2.34015390e-01],\n",
              "        [ 2.13639468e-01],\n",
              "        [-1.74153060e-01],\n",
              "        [ 1.03863142e-01],\n",
              "        [-1.28190324e-01],\n",
              "        [ 2.00601682e-01],\n",
              "        [ 1.80107746e-02],\n",
              "        [ 1.14656310e-03],\n",
              "        [-4.35739122e-02],\n",
              "        [-7.32844323e-02],\n",
              "        [-1.90919369e-01],\n",
              "        [-1.62957266e-01],\n",
              "        [-7.15123340e-02],\n",
              "        [-2.43836731e-01],\n",
              "        [ 3.71779664e-04],\n",
              "        [ 1.34197408e-02],\n",
              "        [ 9.00978819e-02],\n",
              "        [-1.86431304e-01],\n",
              "        [ 2.36483186e-01],\n",
              "        [ 1.73057139e-01],\n",
              "        [ 1.39977589e-01],\n",
              "        [ 1.43703774e-01],\n",
              "        [ 1.41870812e-01],\n",
              "        [-2.65791919e-03],\n",
              "        [-6.43267781e-02],\n",
              "        [-1.14472866e-01],\n",
              "        [-1.88364595e-01],\n",
              "        [-3.72701660e-02],\n",
              "        [ 9.70233157e-02],\n",
              "        [-5.46839461e-02],\n",
              "        [ 2.03625470e-01],\n",
              "        [ 3.23678553e-02],\n",
              "        [ 2.11005792e-01],\n",
              "        [-1.98380789e-03],\n",
              "        [ 1.41664356e-01],\n",
              "        [ 1.52152628e-01],\n",
              "        [-2.30434135e-01],\n",
              "        [ 1.53680623e-01],\n",
              "        [-8.31119493e-02],\n",
              "        [-1.26638502e-01],\n",
              "        [-2.92258590e-01],\n",
              "        [-4.37800512e-02],\n",
              "        [ 1.05896927e-01],\n",
              "        [-1.83650509e-01],\n",
              "        [ 1.88231722e-01],\n",
              "        [-1.29255459e-01],\n",
              "        [ 7.65846996e-03],\n",
              "        [ 1.62537973e-02],\n",
              "        [ 1.21042237e-01],\n",
              "        [ 1.97899655e-01],\n",
              "        [-3.18936914e-01],\n",
              "        [-3.40113550e-01],\n",
              "        [ 3.33279022e-04],\n",
              "        [-1.49253696e-01],\n",
              "        [-2.12617874e-01],\n",
              "        [ 1.56216502e-01],\n",
              "        [-1.50406003e-01],\n",
              "        [ 1.46948397e-01],\n",
              "        [-3.99138540e-01],\n",
              "        [ 8.12676996e-02],\n",
              "        [ 7.98591077e-02],\n",
              "        [ 4.88433950e-02],\n",
              "        [-1.71739370e-01],\n",
              "        [-1.46705464e-01],\n",
              "        [ 1.22896545e-01],\n",
              "        [-3.03858787e-01],\n",
              "        [-1.60499096e-01],\n",
              "        [ 1.48836598e-01],\n",
              "        [ 4.96036410e-02],\n",
              "        [-8.22257698e-02],\n",
              "        [-5.11930175e-02],\n",
              "        [-8.65702182e-02],\n",
              "        [ 1.75896183e-01],\n",
              "        [-1.66726232e-01],\n",
              "        [ 3.51705343e-01],\n",
              "        [ 2.05002472e-01],\n",
              "        [ 2.63716310e-01],\n",
              "        [ 5.53816147e-02],\n",
              "        [ 1.18777610e-01],\n",
              "        [-1.31388023e-01],\n",
              "        [ 9.66990250e-04],\n",
              "        [-3.01270336e-01],\n",
              "        [ 1.20474987e-01],\n",
              "        [ 2.21414346e-04],\n",
              "        [ 8.28699488e-03],\n",
              "        [ 2.34230846e-01],\n",
              "        [-1.26404315e-01],\n",
              "        [ 5.99099658e-02],\n",
              "        [ 1.29790753e-01],\n",
              "        [ 1.73482850e-01],\n",
              "        [ 7.63629675e-02],\n",
              "        [ 2.05877632e-01],\n",
              "        [-2.85699695e-01],\n",
              "        [ 8.97628255e-03],\n",
              "        [-1.21848568e-01],\n",
              "        [-6.96457399e-04],\n",
              "        [-2.84460485e-01],\n",
              "        [-2.00263441e-01],\n",
              "        [ 2.35675693e-01],\n",
              "        [-4.68216687e-02],\n",
              "        [-7.09647499e-03],\n",
              "        [ 1.65229976e-01],\n",
              "        [ 7.63337314e-02],\n",
              "        [ 3.15428585e-01],\n",
              "        [-4.15270701e-02],\n",
              "        [ 4.55832891e-02],\n",
              "        [ 7.28524872e-04],\n",
              "        [-9.17489976e-02],\n",
              "        [-3.09784532e-01],\n",
              "        [ 6.21367618e-02],\n",
              "        [ 2.12691277e-01],\n",
              "        [ 1.10296480e-01],\n",
              "        [-1.53493598e-01],\n",
              "        [ 3.32190931e-01],\n",
              "        [-9.53894258e-02],\n",
              "        [-1.44541085e-01],\n",
              "        [ 1.71094555e-02],\n",
              "        [-1.60258889e-01],\n",
              "        [ 7.02550858e-02],\n",
              "        [-1.93525836e-01],\n",
              "        [-6.83003142e-02],\n",
              "        [ 2.85103977e-01],\n",
              "        [ 4.60779928e-02],\n",
              "        [ 6.12037741e-02],\n",
              "        [-1.96535051e-01],\n",
              "        [ 1.49010122e-01],\n",
              "        [ 1.73659816e-01],\n",
              "        [-1.61730170e-01]], dtype=float32),\n",
              " array([-0.01155756], dtype=float32)]"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZC7Bzyy1fvL4"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from keras.models import load_model\n",
        "from sklearn.metrics import classification_report, confusion_matrix"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "YaIsdtxrfLja",
        "outputId": "0adfdddd-004d-4177-fa08-4bd0bafa5eb6"
      },
      "source": [
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(np.arange(0, 25), H.history[\"loss\"], label=\"train_loss\")\n",
        "plt.plot(np.arange(0, 25), H.history[\"val_loss\"], label=\"val_loss\")\n",
        "plt.title(\"Loss Plot\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.savefig('loss-plot-cancer.png')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "dnL4TYXgfNKz",
        "outputId": "3e990373-a94f-4589-b31e-c62ed19b4467"
      },
      "source": [
        "plt.style.use(\"ggplot\")\n",
        "plt.figure()\n",
        "plt.plot(np.arange(0, 25), H.history[\"acc\"], label=\"train_acc\")\n",
        "plt.plot(np.arange(0, 25), H.history[\"val_acc\"], label=\"val_acc\")\n",
        "plt.title(\"Accuracy Plot\")\n",
        "plt.xlabel(\"Epoch #\")\n",
        "plt.ylabel(\"Acc\")\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "plt.savefig('accuracy-plot-cancer.png')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDcv-i-efPXl",
        "outputId": "0de26b3a-6a97-45d2-e9c0-1f8b2502436d"
      },
      "source": [
        "from sklearn.metrics import classification_report\n",
        "\n",
        "pred = model.predict(x_val)\n",
        "labels = (pred > 0.5).astype(np.int)\n",
        "\n",
        "print(classification_report(y_val, labels))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.88      0.86       360\n",
            "           1       0.84      0.81      0.82       300\n",
            "\n",
            "    accuracy                           0.84       660\n",
            "   macro avg       0.84      0.84      0.84       660\n",
            "weighted avg       0.84      0.84      0.84       660\n",
            "\n"
          ]
        }
      ]
    }
  ]
}