keras_first_neural_net.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "keras-first-neural-net.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNs7hjR+GV4OZ7gkI3lKO8w",
      "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/brunocosouza/deep_learning/blob/main/keras_first_neural_net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzlGI_DlJFQW"
      },
      "source": [
        "import tensorflow as tf\n",
        "from   tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hmC5jfy9JeqR",
        "outputId": "9b6dcb1d-097e-42fa-ceff-064a643acffd"
      },
      "source": [
        "# Scaling the images\n",
        "X_train = np.load(\"X_train.npy\") / 255\n",
        "X_train_gray = np.mean(X_train, axis = -1)\n",
        "Y_train = np.load(\"Y_train.npy\")\n",
        "\n",
        "# Shape\n",
        "X_train_gray.shape, Y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((213, 200, 200), (213,))"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uGdmdY_mMdP7",
        "outputId": "db028a6e-b02b-43ee-dd05-d07ee1a71ff5"
      },
      "source": [
        "# Scaling valid dataset\n",
        "X_valid = np.load(\"X_valid.npy\") / 255\n",
        "X_valid_gray = np.mean(X_valid, axis = -1)\n",
        "Y_valid = np.load(\"Y_valid.npy\")\n",
        "\n",
        "#Shape\n",
        "X_valid_gray.shape, Y_valid.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((71, 200, 200), (71,))"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5YVps-QbMd9j",
        "outputId": "b54b7dbf-4729-4648-881f-fa3ce60669a1"
      },
      "source": [
        "# Scaling test dataset\n",
        "X_test = np.load(\"X_test.npy\") / 255\n",
        "X_test_gray = np.mean(X_test, axis = -1)\n",
        "Y_test = np.load(\"Y_valid.npy\")\n",
        "\n",
        "#Shape\n",
        "X_test_gray.shape, Y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((71, 200, 200), (71,))"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q76SXXOmMnOz"
      },
      "source": [
        "import tensorflow as tf\n",
        "from   tensorflow import keras\n",
        "\n",
        "# inherit Model from keras: the Subclass type\n",
        "class MyModel(keras.models.Model):\n",
        "    def __init__(self, shape=[200,200], activation=\"relu\", **kwargs):\n",
        "        super().__init__(**kwargs)\n",
        "        self.input1  = keras.layers.Input(shape, name=\"input\")\n",
        "        self.flatten = keras.layers.Flatten(input_shape=[200,200],  name=\"flatten\")\n",
        "        self.hidden1 = keras.layers.Dense(400, activation=\"relu\", name=\"hidden1\", kernel_initializer=\"he_uniform\")\n",
        "        self.hidden2 = keras.layers.Dense(100, activation=\"relu\", name=\"hidden2\", kernel_initializer=\"he_uniform\")\n",
        "        self.output1 = keras.layers.Dense(10, activation=\"softmax\", name=\"decision\")\n",
        "        \n",
        "    def call(self, inputs):\n",
        "        input1  = inputs # in case you had multiple inputs, you could separate them from a list\n",
        "        flatten = self.flatten(input1)\n",
        "        hidden1 = self.hidden1(flatten)\n",
        "        hidden2 = self.hidden2(hidden1)\n",
        "        concat  = keras.layers.concatenate([hidden1, hidden2])\n",
        "        output1 = self.output1(concat)\n",
        "        return output1 # in case you had multiple outputs, you could return them in a list\n",
        "\n",
        "keras.backend.clear_session() # clean memory from previous models \n",
        "np.random.seed(42)            \n",
        "tf.random.set_seed(42)\n",
        "model = MyModel()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dhloQPEjMucb"
      },
      "source": [
        "# Pick an optimizer\n",
        "\n",
        "opt =  keras.optimizers.SGD(learning_rate=1e-3, momentum=0.9, nesterov=True)\n",
        "# opt =  keras.optimizers.RMSProp(learning_rate=1e-3, rho=0.9)\n",
        "# opt =  keras.optimizers.Adam(learning_rate=1e-3, beta_1=0.9, beta_2=0.999)\n",
        "\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", \n",
        "              optimizer=opt, \n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "# Define a learning rate schedule as a callback function\n",
        "\n",
        "def exponential_decay(epoch):\n",
        "    return(0.01*0.1**(epoch/20))\n",
        "\n",
        "num_epochs   = 20\n",
        "lr_scheduler = keras.callbacks.LearningRateScheduler(exponential_decay)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzRDWErvMvti",
        "outputId": "c1b9c2c1-6ba0-48eb-c1db-4568fd4bd45a"
      },
      "source": [
        "history  = model.fit(X_train_gray, Y_train, epochs=num_epochs, validation_data=(X_valid_gray, Y_valid))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "7/7 [==============================] - 1s 122ms/step - loss: 1.9248 - accuracy: 0.3568 - val_loss: 1.4897 - val_accuracy: 0.5493\n",
            "Epoch 2/20\n",
            "7/7 [==============================] - 1s 99ms/step - loss: 1.1969 - accuracy: 0.6197 - val_loss: 1.2076 - val_accuracy: 0.5775\n",
            "Epoch 3/20\n",
            "7/7 [==============================] - 1s 96ms/step - loss: 0.8483 - accuracy: 0.7559 - val_loss: 1.0510 - val_accuracy: 0.6479\n",
            "Epoch 4/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.6455 - accuracy: 0.8357 - val_loss: 0.9371 - val_accuracy: 0.6620\n",
            "Epoch 5/20\n",
            "7/7 [==============================] - 1s 96ms/step - loss: 0.5016 - accuracy: 0.8920 - val_loss: 0.8838 - val_accuracy: 0.6761\n",
            "Epoch 6/20\n",
            "7/7 [==============================] - 1s 92ms/step - loss: 0.4029 - accuracy: 0.9343 - val_loss: 0.8394 - val_accuracy: 0.6761\n",
            "Epoch 7/20\n",
            "7/7 [==============================] - 1s 93ms/step - loss: 0.3292 - accuracy: 0.9577 - val_loss: 0.8027 - val_accuracy: 0.7324\n",
            "Epoch 8/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.2814 - accuracy: 0.9577 - val_loss: 0.7823 - val_accuracy: 0.7465\n",
            "Epoch 9/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.2430 - accuracy: 0.9718 - val_loss: 0.7724 - val_accuracy: 0.7465\n",
            "Epoch 10/20\n",
            "7/7 [==============================] - 1s 95ms/step - loss: 0.2133 - accuracy: 0.9812 - val_loss: 0.7570 - val_accuracy: 0.7324\n",
            "Epoch 11/20\n",
            "7/7 [==============================] - 1s 98ms/step - loss: 0.1900 - accuracy: 0.9812 - val_loss: 0.7471 - val_accuracy: 0.7606\n",
            "Epoch 12/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.1695 - accuracy: 0.9812 - val_loss: 0.7314 - val_accuracy: 0.7606\n",
            "Epoch 13/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.1542 - accuracy: 0.9906 - val_loss: 0.7255 - val_accuracy: 0.7746\n",
            "Epoch 14/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.1397 - accuracy: 0.9953 - val_loss: 0.7206 - val_accuracy: 0.7887\n",
            "Epoch 15/20\n",
            "7/7 [==============================] - 1s 93ms/step - loss: 0.1282 - accuracy: 0.9953 - val_loss: 0.7125 - val_accuracy: 0.7746\n",
            "Epoch 16/20\n",
            "7/7 [==============================] - 1s 97ms/step - loss: 0.1186 - accuracy: 0.9953 - val_loss: 0.7054 - val_accuracy: 0.7887\n",
            "Epoch 17/20\n",
            "7/7 [==============================] - 1s 95ms/step - loss: 0.1097 - accuracy: 0.9953 - val_loss: 0.7023 - val_accuracy: 0.7887\n",
            "Epoch 18/20\n",
            "7/7 [==============================] - 1s 94ms/step - loss: 0.1025 - accuracy: 0.9953 - val_loss: 0.7016 - val_accuracy: 0.7887\n",
            "Epoch 19/20\n",
            "7/7 [==============================] - 1s 95ms/step - loss: 0.0958 - accuracy: 0.9953 - val_loss: 0.6985 - val_accuracy: 0.7887\n",
            "Epoch 20/20\n",
            "7/7 [==============================] - 1s 93ms/step - loss: 0.0907 - accuracy: 0.9953 - val_loss: 0.6866 - val_accuracy: 0.7887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "SjMv0TNONJEa",
        "outputId": "562be958-1314-459a-b58b-a7f71ab680b4"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "pd.DataFrame(history.history).plot(figsize=(8, 5))\n",
        "plt.grid(True)\n",
        "plt.gca().set_ylim(0, 1)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQPimViARRpJ",
        "outputId": "e0e7b887-6638-422e-d046-98b44369c56e"
      },
      "source": [
        "model.save(\"my_keras_model.tf\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: my_keras_model.tf/assets\n"
          ]
        }
      ]
    }
  ]
}