ANN.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "ANN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "498kdGpUHfXs"
      },
      "source": [
        "import numpy as np\n",
        "from random import randint\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjBGtbcOHios"
      },
      "source": [
        "train_samples = []\n",
        "train_labels = []\n",
        "\n",
        "for i in range(50):\n",
        "    random_younger = randint(13,64)\n",
        "    train_samples.append(random_younger)\n",
        "    train_labels.append(1)\n",
        "    \n",
        "    random_older = randint(65,100)\n",
        "    train_samples.append(random_older)\n",
        "    train_labels.append(0)\n",
        "    \n",
        "for i in range(1000):\n",
        "    random_younger = randint(13,64)\n",
        "    train_samples.append(random_younger)\n",
        "    train_labels.append(0)\n",
        "    \n",
        "    random_older = randint(65,100)\n",
        "    train_samples.append(random_older)\n",
        "    train_labels.append(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9rejc4k9HqAL"
      },
      "source": [
        "train_labels = np.array(train_labels)\n",
        "train_samples = np.array(train_samples)\n",
        "train_labels, train_samples = shuffle(train_labels, train_samples)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HSAYPcJ3HvsL"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0,1))\n",
        "scaled_train_samples = scaler.fit_transform(train_samples.reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kiTMeFjeH227"
      },
      "source": [
        "Keras Sequential Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDJa36HoH57B"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Activation, Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.metrics import categorical_crossentropy"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMhECx90IP_u",
        "outputId": "576b28f7-70b6-4a8c-fa2f-9c0033465f2f"
      },
      "source": [
        "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
        "print(\"Num GPUs Available: \", len(physical_devices))\n",
        "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Num GPUs Available:  1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C8Ck0cfMIkx7"
      },
      "source": [
        "model = Sequential([\n",
        "      Dense(units=16, input_shape=(1,), activation='relu'),\n",
        "      Dense(units=32, activation='relu'),\n",
        "      Dense(units=2, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uFdFVZr9JYUC",
        "outputId": "8c0012d5-5c9c-4ca3-e29e-d6c01f56e0f7"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 16)                32        \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 32)                544       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 66        \n",
            "=================================================================\n",
            "Total params: 642\n",
            "Trainable params: 642\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u6lV1-ZGKFk4"
      },
      "source": [
        "Train the Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yd2cTfFhKHpo"
      },
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.0001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sB1ut6x-KUGD",
        "outputId": "6621d30c-86ec-40bc-9bea-18c25d826bef"
      },
      "source": [
        "model.fit(x=scaled_train_samples, y=train_labels, validation_split=0.1, batch_size=10, epochs=30, shuffle=True, verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "189/189 - 0s - loss: 0.6592 - accuracy: 0.5291 - val_loss: 0.6451 - val_accuracy: 0.6238\n",
            "Epoch 2/30\n",
            "189/189 - 0s - loss: 0.6379 - accuracy: 0.6280 - val_loss: 0.6193 - val_accuracy: 0.7238\n",
            "Epoch 3/30\n",
            "189/189 - 0s - loss: 0.6098 - accuracy: 0.7063 - val_loss: 0.5921 - val_accuracy: 0.7524\n",
            "Epoch 4/30\n",
            "189/189 - 0s - loss: 0.5841 - accuracy: 0.7370 - val_loss: 0.5682 - val_accuracy: 0.7810\n",
            "Epoch 5/30\n",
            "189/189 - 0s - loss: 0.5593 - accuracy: 0.7693 - val_loss: 0.5456 - val_accuracy: 0.7952\n",
            "Epoch 6/30\n",
            "189/189 - 0s - loss: 0.5347 - accuracy: 0.7915 - val_loss: 0.5226 - val_accuracy: 0.8000\n",
            "Epoch 7/30\n",
            "189/189 - 0s - loss: 0.5111 - accuracy: 0.8074 - val_loss: 0.5019 - val_accuracy: 0.8286\n",
            "Epoch 8/30\n",
            "189/189 - 0s - loss: 0.4886 - accuracy: 0.8280 - val_loss: 0.4823 - val_accuracy: 0.8333\n",
            "Epoch 9/30\n",
            "189/189 - 0s - loss: 0.4676 - accuracy: 0.8439 - val_loss: 0.4646 - val_accuracy: 0.8429\n",
            "Epoch 10/30\n",
            "189/189 - 0s - loss: 0.4484 - accuracy: 0.8513 - val_loss: 0.4489 - val_accuracy: 0.8524\n",
            "Epoch 11/30\n",
            "189/189 - 0s - loss: 0.4311 - accuracy: 0.8677 - val_loss: 0.4344 - val_accuracy: 0.8619\n",
            "Epoch 12/30\n",
            "189/189 - 0s - loss: 0.4158 - accuracy: 0.8709 - val_loss: 0.4222 - val_accuracy: 0.8619\n",
            "Epoch 13/30\n",
            "189/189 - 0s - loss: 0.4024 - accuracy: 0.8714 - val_loss: 0.4116 - val_accuracy: 0.8524\n",
            "Epoch 14/30\n",
            "189/189 - 0s - loss: 0.3907 - accuracy: 0.8735 - val_loss: 0.4030 - val_accuracy: 0.8429\n",
            "Epoch 15/30\n",
            "189/189 - 0s - loss: 0.3808 - accuracy: 0.8688 - val_loss: 0.3954 - val_accuracy: 0.8429\n",
            "Epoch 16/30\n",
            "189/189 - 0s - loss: 0.3723 - accuracy: 0.8672 - val_loss: 0.3893 - val_accuracy: 0.8429\n",
            "Epoch 17/30\n",
            "189/189 - 0s - loss: 0.3647 - accuracy: 0.8698 - val_loss: 0.3834 - val_accuracy: 0.8429\n",
            "Epoch 18/30\n",
            "189/189 - 0s - loss: 0.3587 - accuracy: 0.8677 - val_loss: 0.3796 - val_accuracy: 0.8429\n",
            "Epoch 19/30\n",
            "189/189 - 0s - loss: 0.3538 - accuracy: 0.8677 - val_loss: 0.3761 - val_accuracy: 0.8429\n",
            "Epoch 20/30\n",
            "189/189 - 0s - loss: 0.3494 - accuracy: 0.8693 - val_loss: 0.3728 - val_accuracy: 0.8429\n",
            "Epoch 21/30\n",
            "189/189 - 0s - loss: 0.3459 - accuracy: 0.8672 - val_loss: 0.3706 - val_accuracy: 0.8429\n",
            "Epoch 22/30\n",
            "189/189 - 0s - loss: 0.3428 - accuracy: 0.8704 - val_loss: 0.3686 - val_accuracy: 0.8333\n",
            "Epoch 23/30\n",
            "189/189 - 0s - loss: 0.3404 - accuracy: 0.8709 - val_loss: 0.3670 - val_accuracy: 0.8429\n",
            "Epoch 24/30\n",
            "189/189 - 0s - loss: 0.3378 - accuracy: 0.8698 - val_loss: 0.3663 - val_accuracy: 0.8333\n",
            "Epoch 25/30\n",
            "189/189 - 0s - loss: 0.3361 - accuracy: 0.8720 - val_loss: 0.3648 - val_accuracy: 0.8333\n",
            "Epoch 26/30\n",
            "189/189 - 0s - loss: 0.3344 - accuracy: 0.8720 - val_loss: 0.3638 - val_accuracy: 0.8333\n",
            "Epoch 27/30\n",
            "189/189 - 0s - loss: 0.3329 - accuracy: 0.8720 - val_loss: 0.3630 - val_accuracy: 0.8333\n",
            "Epoch 28/30\n",
            "189/189 - 0s - loss: 0.3317 - accuracy: 0.8720 - val_loss: 0.3622 - val_accuracy: 0.8333\n",
            "Epoch 29/30\n",
            "189/189 - 0s - loss: 0.3304 - accuracy: 0.8683 - val_loss: 0.3621 - val_accuracy: 0.8286\n",
            "Epoch 30/30\n",
            "189/189 - 0s - loss: 0.3296 - accuracy: 0.8730 - val_loss: 0.3616 - val_accuracy: 0.8286\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f73fd72f438>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdfrLnZANksO"
      },
      "source": [
        "Proprocess Test Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZ8hk0unKdFR"
      },
      "source": [
        "test_labels = []\n",
        "test_samples = []\n",
        "\n",
        "for i in range(10):\n",
        "    random_younger = randint(13,64)\n",
        "    test_samples.append(random_younger)\n",
        "    test_labels.append(1)\n",
        "    \n",
        "    random_older = randint(65,100)\n",
        "    test_samples.append(random_older)\n",
        "    test_labels.append(0)\n",
        "    \n",
        "for i in range(200):\n",
        "    random_younger = randint(13,64)\n",
        "    test_samples.append(random_younger)\n",
        "    test_labels.append(0)\n",
        "    \n",
        "    random_older = randint(65,100)\n",
        "    test_samples.append(random_older)\n",
        "    test_labels.append(1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gE34NTbuN7No"
      },
      "source": [
        "test_labels = np.array(test_labels)\n",
        "test_samples = np.array(test_samples)\n",
        "test_labels, test_samples = shuffle(test_labels, test_samples)\n",
        "scaled_test_samples = scaler.fit_transform(test_samples.reshape(-1,1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2AhN_K_OcmZ"
      },
      "source": [
        "Predict"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lHi-oRi5OaEY"
      },
      "source": [
        "predictions = model.predict(x=scaled_test_samples, batch_size=10, verbose=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JbeLW245Oioj",
        "outputId": "6f880d24-8272-4084-bf9d-073e20491851"
      },
      "source": [
        "predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "       [0.2791198 , 0.72088015],\n",
              "       [0.14400229, 0.8559977 ],\n",
              "       [0.303699  , 0.69630104],\n",
              "       [0.8908655 , 0.10913452],\n",
              "       [0.74566823, 0.2543318 ],\n",
              "       [0.21314152, 0.7868585 ],\n",
              "       [0.41255853, 0.5874415 ],\n",
              "       [0.9383728 , 0.06162723]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wMrjcEAUOjoB"
      },
      "source": [
        "rounded_predictions = np.argmax(predictions, axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kP3drvt7Oq9O",
        "outputId": "13c964b9-25ec-4195-84fc-0e7a79252563"
      },
      "source": [
        "rounded_predictions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
              "       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,\n",
              "       1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1,\n",
              "       0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1,\n",
              "       0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1,\n",
              "       1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1,\n",
              "       1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1,\n",
              "       1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,\n",
              "       0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0,\n",
              "       0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,\n",
              "       1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0,\n",
              "       1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,\n",
              "       0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1,\n",
              "       1, 0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPG10D-lOtfT"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "import itertools\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wN1gX0pbPkMU"
      },
      "source": [
        "cm = confusion_matrix(y_true=test_labels, y_pred=rounded_predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CupPHky2PpzZ"
      },
      "source": [
        "def plot_confusion_matrix(cm, classes, normalize=False, title=\"Confusion matrix\", cmap=plt.cm.Blues):\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  if normalize:\n",
        "    cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "    print(\"Normalized Confusion Matrix\")\n",
        "  else:\n",
        "    print(\"Confusion Matrix without Normalization\")\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  thresh = cm.max() / 2.0\n",
        "\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "    plt.text(j, i, cm[i,j], horizontalalignment=\"center\", color=\"white\" if cm[i,j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.xlabel('Predicted label')\n",
        "  plt.ylabel('True label')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "5V-kkBdERJlF",
        "outputId": "2f9cbc7a-164d-4c4b-b5c2-99d21b22762f"
      },
      "source": [
        "cm_plot_labels = ['no_side_effects', 'had_side_effects']\n",
        "plot_confusion_matrix(cm=cm, classes=cm_plot_labels)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Confusion Matrix without Normalization\n",
            "[[186  24]\n",
            " [ 10 200]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3tj3x0vSR7Wc"
      },
      "source": [
        "Save a Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YTDM8w-yRUFg"
      },
      "source": [
        "import os.path\n",
        "if os.path.isfile('medical_trial_model.h5') is False:\n",
        "  model.save('medical_trial_model.h5')\n",
        "  print(\"Model Saved!\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A0LX5ftjSKgb"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "new_model = load_model('medical_trial_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCr8TNTNSuLs",
        "outputId": "49967365-1d04-46e9-9d58-708738990b6f"
      },
      "source": [
        "new_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 16)                32        \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 32)                544       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 66        \n",
            "=================================================================\n",
            "Total params: 642\n",
            "Trainable params: 642\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g33-p5_hSvvm",
        "outputId": "cf5feaef-5dfa-4e17-d962-6205d624f8df"
      },
      "source": [
        "new_model.get_weights()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 0.658495  , -0.57884365,  0.5766728 , -0.4186311 , -0.45432907,\n",
              "         -0.01517761,  0.08068924, -0.07213491,  0.3457575 , -0.28450316,\n",
              "          0.2807092 , -0.38630718, -0.57006836, -0.17407686,  0.4774697 ,\n",
              "          0.48209918]], dtype=float32),\n",
              " array([-0.11281551,  0.        , -0.12681632,  0.        ,  0.        ,\n",
              "         0.        ,  0.24590242,  0.        , -0.09091264,  0.        ,\n",
              "        -0.08369508,  0.        ,  0.        ,  0.        , -0.09601183,\n",
              "        -0.1221495 ], dtype=float32),\n",
              " array([[-0.04046595,  0.39698634,  0.35735756, -0.24532634, -0.16593158,\n",
              "          0.30845317,  0.34917107, -0.27576414, -0.20099992, -0.22190341,\n",
              "         -0.3663994 , -0.23690987,  0.38386333,  0.07708398,  0.41189143,\n",
              "          0.23054412,  0.12123533, -0.3438299 ,  0.20011172,  0.09990514,\n",
              "          0.5223809 , -0.18318588, -0.08146498, -0.25972894,  0.28303325,\n",
              "          0.27940306,  0.30553862, -0.10511122, -0.07770795, -0.33227167,\n",
              "         -0.4902872 , -0.46147928],\n",
              "        [-0.34797686, -0.1132426 ,  0.17768958, -0.09226096,  0.29517606,\n",
              "         -0.09042808,  0.33483312, -0.17276102,  0.3497959 , -0.23953933,\n",
              "          0.33694056,  0.0773586 ,  0.02620226,  0.18867418,  0.20620385,\n",
              "         -0.2884768 ,  0.05350146,  0.30366752, -0.1547832 ,  0.19592997,\n",
              "          0.06320545,  0.21034196, -0.08939886, -0.32676974,  0.00865975,\n",
              "         -0.09168845, -0.24840331,  0.30947503, -0.17586547,  0.34417173,\n",
              "          0.28961846, -0.2506612 ],\n",
              "        [ 0.15567377,  0.12032462,  0.3953957 ,  0.07201043, -0.13824314,\n",
              "          0.31756276,  0.09969306, -0.20725605, -0.04172918, -0.07486048,\n",
              "          0.1910862 ,  0.08708161,  0.25544053, -0.02775326,  0.4867397 ,\n",
              "          0.18782973,  0.02260839, -0.267023  , -0.11308531,  0.16130458,\n",
              "         -0.09040979, -0.29241621,  0.15116581, -0.02737969,  0.39734802,\n",
              "          0.19888921,  0.5302482 ,  0.4110539 ,  0.42901978, -0.16151701,\n",
              "         -0.12757787,  0.1363815 ],\n",
              "        [-0.20735414,  0.13031495, -0.25047255,  0.35068074,  0.11994809,\n",
              "         -0.03523275, -0.2934749 , -0.18937987, -0.17014253,  0.23242936,\n",
              "         -0.07418275, -0.17323636,  0.22483352,  0.2640802 , -0.32924443,\n",
              "         -0.19758257, -0.23980401, -0.32546353,  0.31247082,  0.2825288 ,\n",
              "          0.06651977, -0.18703178,  0.27178916,  0.05353677,  0.08696708,\n",
              "          0.22926757,  0.28540656,  0.3352578 ,  0.00814447,  0.18537727,\n",
              "         -0.25884402, -0.01309419],\n",
              "        [-0.06626523, -0.09508312, -0.19373287, -0.35250425,  0.30930904,\n",
              "          0.32214895,  0.20599696,  0.31749633, -0.08782029, -0.09803072,\n",
              "         -0.07029048, -0.2197019 ,  0.25401923,  0.33383355,  0.16632149,\n",
              "         -0.05994394,  0.09506971, -0.18081512,  0.07213727,  0.28454837,\n",
              "          0.17725286,  0.34252653, -0.01577649,  0.2825164 ,  0.3509017 ,\n",
              "         -0.00406969,  0.3242428 ,  0.11974517,  0.33259383,  0.30430827,\n",
              "         -0.12044364, -0.17356358],\n",
              "        [-0.2609452 , -0.12550674, -0.1630697 ,  0.32005194, -0.04037187,\n",
              "         -0.15250163, -0.14918248,  0.0347327 ,  0.11772549, -0.13165815,\n",
              "          0.1732088 , -0.01722753, -0.01815981,  0.2052966 ,  0.22626486,\n",
              "          0.07266378,  0.19300422,  0.08886158, -0.25538242,  0.02689973,\n",
              "          0.12582394,  0.29279295,  0.13967094, -0.2022854 ,  0.2943798 ,\n",
              "          0.04283139, -0.18062428,  0.07329768, -0.16765931,  0.22050574,\n",
              "          0.28700754, -0.19965047],\n",
              "        [-0.17368522,  0.1454311 ,  0.3006205 , -0.1841202 ,  0.29431358,\n",
              "          0.34547067, -0.06167013,  0.40142518, -0.3038616 , -0.24697387,\n",
              "          0.19662969, -0.11791289, -0.2879472 , -0.30049092,  0.16836302,\n",
              "          0.2826018 ,  0.22564936,  0.49596307,  0.31329   ,  0.24556366,\n",
              "         -0.2581665 ,  0.46604928, -0.02517059, -0.2982596 , -0.2825519 ,\n",
              "          0.14216174, -0.11731535, -0.11743268,  0.23769797, -0.34103325,\n",
              "          0.24016103,  0.48517787],\n",
              "        [ 0.09692174,  0.05632892, -0.18795462,  0.34529993,  0.24835399,\n",
              "         -0.08892554, -0.21103719, -0.07488364, -0.15745994, -0.27215627,\n",
              "         -0.11980174, -0.27758124, -0.14879313, -0.1618342 , -0.13248247,\n",
              "          0.3055599 ,  0.30502263,  0.18489662,  0.19572988,  0.08704421,\n",
              "         -0.29308522, -0.28836602, -0.00580445,  0.16528687, -0.20824884,\n",
              "          0.25087407, -0.2555043 ,  0.22318187,  0.16149357,  0.00703195,\n",
              "          0.03335208,  0.04359654],\n",
              "        [-0.33226106,  0.308097  , -0.09379178, -0.01074567, -0.4051546 ,\n",
              "          0.31537977,  0.292168  , -0.46879536,  0.31918788, -0.1390105 ,\n",
              "         -0.09688498, -0.15747108,  0.4686735 , -0.07247183, -0.04602106,\n",
              "          0.30081233, -0.00273483, -0.23593298, -0.12657419, -0.06548882,\n",
              "          0.4688956 ,  0.19405392, -0.16274348, -0.15788782,  0.1357369 ,\n",
              "          0.1436634 , -0.09974191,  0.41971415,  0.35135117, -0.2733863 ,\n",
              "         -0.47474688, -0.39926234],\n",
              "        [ 0.34701726, -0.30210632, -0.00362396, -0.07899973, -0.15031429,\n",
              "         -0.32886553, -0.16023153,  0.06328306,  0.14320436, -0.02627721,\n",
              "          0.2564291 ,  0.06611693,  0.06985664,  0.2835935 ,  0.1246295 ,\n",
              "          0.09223804,  0.26522884,  0.19028905,  0.25102088, -0.23982905,\n",
              "          0.08907425,  0.06484479,  0.15515992,  0.32626203,  0.16856393,\n",
              "          0.09577999,  0.17887774, -0.2874248 , -0.24384555, -0.24595544,\n",
              "         -0.14452045, -0.30773124],\n",
              "        [-0.24269494, -0.14019975,  0.3566562 , -0.04599521, -0.12268206,\n",
              "          0.2596226 ,  0.02715606,  0.12254287,  0.23144868,  0.14523298,\n",
              "          0.01244773, -0.12645951, -0.11201978,  0.04461303,  0.02302789,\n",
              "          0.55189383, -0.00591495,  0.01252477, -0.45750442, -0.39671108,\n",
              "         -0.00896358, -0.35320064,  0.1136269 ,  0.2684293 ,  0.38296333,\n",
              "          0.42961222,  0.3043026 ,  0.19982685,  0.54163116, -0.28003657,\n",
              "         -0.32400072,  0.0977748 ],\n",
              "        [-0.11332588,  0.2907078 ,  0.00102359, -0.30543324, -0.28403968,\n",
              "          0.32377622,  0.14913437,  0.29942563, -0.137913  ,  0.1970754 ,\n",
              "         -0.00065833, -0.29071462,  0.120323  ,  0.23191002,  0.31333533,\n",
              "          0.07492015,  0.03037575,  0.17895326,  0.15397093, -0.21847771,\n",
              "          0.09177956, -0.15526983, -0.25977546,  0.04939494, -0.11246203,\n",
              "          0.08074552,  0.18192813,  0.23416874,  0.1722599 ,  0.32703063,\n",
              "         -0.29633594, -0.2094089 ],\n",
              "        [-0.31126812, -0.25713015,  0.3335065 ,  0.17071709, -0.21077909,\n",
              "          0.33296356,  0.10936138,  0.14346787, -0.11550368, -0.19533175,\n",
              "         -0.35059392,  0.229318  , -0.02336192,  0.1894789 , -0.2788182 ,\n",
              "          0.21711412, -0.16107632,  0.32899073,  0.21306798, -0.14485586,\n",
              "         -0.2818262 ,  0.26060465,  0.19522902, -0.32194948,  0.16980502,\n",
              "         -0.04248714, -0.31765294,  0.29567656, -0.20497605,  0.26756504,\n",
              "         -0.02623904, -0.3419292 ],\n",
              "        [ 0.17102405, -0.3119642 ,  0.20374581, -0.20451455, -0.14965224,\n",
              "         -0.3328491 ,  0.00354952,  0.02959576,  0.31163445,  0.31685898,\n",
              "         -0.32121056, -0.22434026,  0.04026407,  0.26430187,  0.1851022 ,\n",
              "          0.22570309, -0.16009867, -0.18183869,  0.26836106, -0.15443668,\n",
              "          0.12386817,  0.30573854,  0.00201672,  0.0651249 , -0.30062512,\n",
              "         -0.25716615, -0.00524643, -0.23703665,  0.21307305, -0.14858188,\n",
              "         -0.25578475,  0.22742793],\n",
              "        [-0.1483317 ,  0.14721203,  0.4912148 ,  0.07436922, -0.04744105,\n",
              "          0.00911907,  0.18752165,  0.11700094,  0.16218683,  0.17792806,\n",
              "         -0.05964681, -0.17064306,  0.4263924 , -0.03614396,  0.41853935,\n",
              "          0.31503212,  0.4482814 , -0.3453519 , -0.25104612, -0.3253075 ,\n",
              "          0.08733282, -0.13794996, -0.3002415 , -0.10783574,  0.08988624,\n",
              "          0.09298407,  0.34181204,  0.38222528, -0.00260878,  0.21225151,\n",
              "          0.16648783, -0.16510983],\n",
              "        [-0.19936757,  0.38726598,  0.1021769 ,  0.15704319, -0.40570256,\n",
              "          0.37700844,  0.40077102, -0.3376622 , -0.2932132 ,  0.10129395,\n",
              "         -0.25711015, -0.24411377,  0.03492215, -0.14945981,  0.21605758,\n",
              "          0.4892901 ,  0.3847181 ,  0.1082013 , -0.1845474 ,  0.09856184,\n",
              "          0.19648042, -0.40160576, -0.02549234, -0.21453883,  0.21014732,\n",
              "         -0.092719  ,  0.06608604,  0.0299101 ,  0.5150879 ,  0.02539456,\n",
              "          0.14550845, -0.40845788]], dtype=float32),\n",
              " array([ 0.        , -0.08041231, -0.10700884,  0.        ,  0.24941005,\n",
              "        -0.10878263, -0.0574243 ,  0.21005785, -0.00053581,  0.        ,\n",
              "        -0.05093345,  0.        , -0.033065  ,  0.        , -0.09298388,\n",
              "        -0.10130252, -0.08971432,  0.23071782,  0.17285737,  0.15361765,\n",
              "        -0.01944824,  0.22750385,  0.        ,  0.        , -0.02053736,\n",
              "        -0.07503386, -0.06002638, -0.02669753, -0.0789165 ,  0.        ,\n",
              "         0.21521178,  0.25062808], dtype=float32),\n",
              " array([[-2.0854488e-01, -2.2494961e-01],\n",
              "        [-5.9550357e-01,  2.9960456e-01],\n",
              "        [-3.4533989e-01,  2.5051191e-01],\n",
              "        [ 3.3412638e-01, -3.5131872e-02],\n",
              "        [ 4.6616703e-01, -3.5955855e-01],\n",
              "        [-5.6310195e-01,  5.5079009e-02],\n",
              "        [-3.6086729e-01,  3.3910048e-01],\n",
              "        [ 8.1920475e-01, -5.4306805e-01],\n",
              "        [-1.0004456e-01, -6.0701169e-02],\n",
              "        [ 4.0722540e-01, -2.0170103e-01],\n",
              "        [-2.8225639e-01,  2.1832278e-01],\n",
              "        [-3.0240321e-01,  1.2400746e-04],\n",
              "        [-3.9892584e-01,  2.5842905e-01],\n",
              "        [-1.4346963e-01, -2.7779937e-02],\n",
              "        [-1.8145864e-01,  4.5607266e-01],\n",
              "        [-5.0627959e-01,  4.8804354e-02],\n",
              "        [-3.0192873e-01,  3.4604207e-01],\n",
              "        [ 4.6049118e-01, -6.4139301e-01],\n",
              "        [ 2.5810263e-01, -2.1050943e-01],\n",
              "        [ 3.0295473e-01, -2.9577070e-01],\n",
              "        [-5.4800391e-01,  3.4798849e-01],\n",
              "        [ 8.3279979e-01, -6.3892329e-01],\n",
              "        [-3.5240918e-02, -3.5360217e-01],\n",
              "        [-1.1900997e-01, -3.9020324e-01],\n",
              "        [-5.6501085e-01,  4.8395801e-01],\n",
              "        [-4.9118057e-01,  6.0751367e-01],\n",
              "        [ 7.5861633e-02,  5.7832849e-01],\n",
              "        [-4.8293221e-01, -1.1099409e-01],\n",
              "        [-5.6380254e-01,  2.4943946e-02],\n",
              "        [ 2.1911591e-02, -2.2270282e-01],\n",
              "        [ 8.4695935e-01, -1.4659539e-01],\n",
              "        [ 4.8998317e-01, -3.4663731e-01]], dtype=float32),\n",
              " array([ 0.15320268, -0.15320267], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eAF5y-VqSywC",
        "outputId": "23d44e1a-0ca1-402f-a718-26ee01e93d4c"
      },
      "source": [
        "new_model.optimizer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.optimizer_v2.adam.Adam at 0x7f73f6b2b8d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXJZ8lkJS51J"
      },
      "source": [
        "Model to JSON"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUgRqg-_S4OG"
      },
      "source": [
        "json_string = model.to_json()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "id": "omgjd_CyS_pH",
        "outputId": "91e9dde5-506e-4efc-c180-5c72f32a8630"
      },
      "source": [
        "json_string"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_1\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 1], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"dense_3_input\"}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_3\", \"trainable\": true, \"batch_input_shape\": [null, 1], \"dtype\": \"float32\", \"units\": 16, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_4\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 32, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_5\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 2, \"activation\": \"softmax\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zqg8Ok5WTAht"
      },
      "source": [
        "from tensorflow.keras.models import model_from_json\n",
        "model_architecture = model_from_json(json_string)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52-ZrsHhTPVg",
        "outputId": "5bd2e01f-9510-42f6-df76-4b2507ebd8ae"
      },
      "source": [
        "model_architecture.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 16)                32        \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 32)                544       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 66        \n",
            "=================================================================\n",
            "Total params: 642\n",
            "Trainable params: 642\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zqA29gmMTZ81"
      },
      "source": [
        "Save Weights of a model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0IzGGpxTR4G"
      },
      "source": [
        "import os.path\n",
        "if os.path.isfile('my_model_weights.h5') is False:\n",
        "  model.save_weights('my_model_weights.h5')\n",
        "  print(\"Model Saved!\")\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rWucDfrBTuH6"
      },
      "source": [
        "model2 = Sequential([\n",
        "      Dense(units=16, input_shape=(1,), activation='relu'),\n",
        "      Dense(units=32, activation='relu'),\n",
        "      Dense(units=2, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iGQ2RBOzT28e"
      },
      "source": [
        "model2.load_weights('my_model_weights.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GY45qLiDT8Ia",
        "outputId": "544dec4e-cb8f-45b9-ec95-f71e26012560"
      },
      "source": [
        "model2.get_weights()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([[ 0.658495  , -0.57884365,  0.5766728 , -0.4186311 , -0.45432907,\n",
              "         -0.01517761,  0.08068924, -0.07213491,  0.3457575 , -0.28450316,\n",
              "          0.2807092 , -0.38630718, -0.57006836, -0.17407686,  0.4774697 ,\n",
              "          0.48209918]], dtype=float32),\n",
              " array([-0.11281551,  0.        , -0.12681632,  0.        ,  0.        ,\n",
              "         0.        ,  0.24590242,  0.        , -0.09091264,  0.        ,\n",
              "        -0.08369508,  0.        ,  0.        ,  0.        , -0.09601183,\n",
              "        -0.1221495 ], dtype=float32),\n",
              " array([[-0.04046595,  0.39698634,  0.35735756, -0.24532634, -0.16593158,\n",
              "          0.30845317,  0.34917107, -0.27576414, -0.20099992, -0.22190341,\n",
              "         -0.3663994 , -0.23690987,  0.38386333,  0.07708398,  0.41189143,\n",
              "          0.23054412,  0.12123533, -0.3438299 ,  0.20011172,  0.09990514,\n",
              "          0.5223809 , -0.18318588, -0.08146498, -0.25972894,  0.28303325,\n",
              "          0.27940306,  0.30553862, -0.10511122, -0.07770795, -0.33227167,\n",
              "         -0.4902872 , -0.46147928],\n",
              "        [-0.34797686, -0.1132426 ,  0.17768958, -0.09226096,  0.29517606,\n",
              "         -0.09042808,  0.33483312, -0.17276102,  0.3497959 , -0.23953933,\n",
              "          0.33694056,  0.0773586 ,  0.02620226,  0.18867418,  0.20620385,\n",
              "         -0.2884768 ,  0.05350146,  0.30366752, -0.1547832 ,  0.19592997,\n",
              "          0.06320545,  0.21034196, -0.08939886, -0.32676974,  0.00865975,\n",
              "         -0.09168845, -0.24840331,  0.30947503, -0.17586547,  0.34417173,\n",
              "          0.28961846, -0.2506612 ],\n",
              "        [ 0.15567377,  0.12032462,  0.3953957 ,  0.07201043, -0.13824314,\n",
              "          0.31756276,  0.09969306, -0.20725605, -0.04172918, -0.07486048,\n",
              "          0.1910862 ,  0.08708161,  0.25544053, -0.02775326,  0.4867397 ,\n",
              "          0.18782973,  0.02260839, -0.267023  , -0.11308531,  0.16130458,\n",
              "         -0.09040979, -0.29241621,  0.15116581, -0.02737969,  0.39734802,\n",
              "          0.19888921,  0.5302482 ,  0.4110539 ,  0.42901978, -0.16151701,\n",
              "         -0.12757787,  0.1363815 ],\n",
              "        [-0.20735414,  0.13031495, -0.25047255,  0.35068074,  0.11994809,\n",
              "         -0.03523275, -0.2934749 , -0.18937987, -0.17014253,  0.23242936,\n",
              "         -0.07418275, -0.17323636,  0.22483352,  0.2640802 , -0.32924443,\n",
              "         -0.19758257, -0.23980401, -0.32546353,  0.31247082,  0.2825288 ,\n",
              "          0.06651977, -0.18703178,  0.27178916,  0.05353677,  0.08696708,\n",
              "          0.22926757,  0.28540656,  0.3352578 ,  0.00814447,  0.18537727,\n",
              "         -0.25884402, -0.01309419],\n",
              "        [-0.06626523, -0.09508312, -0.19373287, -0.35250425,  0.30930904,\n",
              "          0.32214895,  0.20599696,  0.31749633, -0.08782029, -0.09803072,\n",
              "         -0.07029048, -0.2197019 ,  0.25401923,  0.33383355,  0.16632149,\n",
              "         -0.05994394,  0.09506971, -0.18081512,  0.07213727,  0.28454837,\n",
              "          0.17725286,  0.34252653, -0.01577649,  0.2825164 ,  0.3509017 ,\n",
              "         -0.00406969,  0.3242428 ,  0.11974517,  0.33259383,  0.30430827,\n",
              "         -0.12044364, -0.17356358],\n",
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              " array([[-2.0854488e-01, -2.2494961e-01],\n",
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              "        [-2.8225639e-01,  2.1832278e-01],\n",
              "        [-3.0240321e-01,  1.2400746e-04],\n",
              "        [-3.9892584e-01,  2.5842905e-01],\n",
              "        [-1.4346963e-01, -2.7779937e-02],\n",
              "        [-1.8145864e-01,  4.5607266e-01],\n",
              "        [-5.0627959e-01,  4.8804354e-02],\n",
              "        [-3.0192873e-01,  3.4604207e-01],\n",
              "        [ 4.6049118e-01, -6.4139301e-01],\n",
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              "        [-5.4800391e-01,  3.4798849e-01],\n",
              "        [ 8.3279979e-01, -6.3892329e-01],\n",
              "        [-3.5240918e-02, -3.5360217e-01],\n",
              "        [-1.1900997e-01, -3.9020324e-01],\n",
              "        [-5.6501085e-01,  4.8395801e-01],\n",
              "        [-4.9118057e-01,  6.0751367e-01],\n",
              "        [ 7.5861633e-02,  5.7832849e-01],\n",
              "        [-4.8293221e-01, -1.1099409e-01],\n",
              "        [-5.6380254e-01,  2.4943946e-02],\n",
              "        [ 2.1911591e-02, -2.2270282e-01],\n",
              "        [ 8.4695935e-01, -1.4659539e-01],\n",
              "        [ 4.8998317e-01, -3.4663731e-01]], dtype=float32),\n",
              " array([ 0.15320268, -0.15320267], dtype=float32)]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-V_F7-TT9wf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}