TensorFlow_Keras_MNIST_simple.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.3"
    },
    "colab": {
      "name": "TensorFlow Keras MNIST simple.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/oObelix/DataScience/blob/master/TensorFlow_Keras_MNIST_simple.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "05y61jEErBA7"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "from tensorflow.keras.datasets import mnist"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "obLaywLGrBA8"
      },
      "source": [
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n",
        "\n",
        "x_train = x_train / 255\n",
        "x_test = x_test / 255\n",
        "\n",
        "y_train_cat = tf.keras.utils.to_categorical(y_train)\n",
        "y_test_cat = tf.keras.utils.to_categorical(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wxqnqfsorBA9",
        "outputId": "7d0c6692-2bb5-46d9-9ed3-aa17f4e402ee"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "for i in range(25):\n",
        "    plt.subplot(5, 5, i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(x_test[i], cmap=plt.cm.binary)\n",
        "    plt.title(y_test[i])\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "znTSA_d7rBA9",
        "outputId": "e5bfd4ba-7a45-4bad-8184-1c3bef0c7e7e"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    Flatten(input_shape=(28, 28, 1)),\n",
        "    Dense(128, activation=\"relu\"),\n",
        "    Dense(10, activation=\"softmax\"),\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               100480    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 101,770\n",
            "Trainable params: 101,770\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgC6b137rBA-"
      },
      "source": [
        "model.compile(\n",
        "    optimizer=\"adam\",\n",
        "    loss=\"categorical_crossentropy\",\n",
        "    metrics=[\"accuracy\"]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35wmuZe9rBA-",
        "outputId": "3ef28579-54ce-47af-af29-a11915c67a3c"
      },
      "source": [
        "history = model.fit(x=x_train, y=y_train_cat, epochs=5, validation_split=0.2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1500/1500 [==============================] - 2s 1ms/step - loss: 0.2887 - accuracy: 0.9176 - val_loss: 0.1495 - val_accuracy: 0.9581\n",
            "Epoch 2/5\n",
            "1500/1500 [==============================] - 1s 961us/step - loss: 0.1297 - accuracy: 0.9621 - val_loss: 0.1174 - val_accuracy: 0.9684\n",
            "Epoch 3/5\n",
            "1500/1500 [==============================] - 1s 958us/step - loss: 0.0892 - accuracy: 0.9730 - val_loss: 0.1099 - val_accuracy: 0.9688\n",
            "Epoch 4/5\n",
            "1500/1500 [==============================] - 2s 1ms/step - loss: 0.0665 - accuracy: 0.9801 - val_loss: 0.0887 - val_accuracy: 0.9737\n",
            "Epoch 5/5\n",
            "1500/1500 [==============================] - 1s 968us/step - loss: 0.0502 - accuracy: 0.9847 - val_loss: 0.0865 - val_accuracy: 0.9751\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_u3zG7FrrBA_",
        "outputId": "ccf93e47-42e1-4144-ee5a-7edc1690611b"
      },
      "source": [
        "model.evaluate(x_test, y_test_cat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "313/313 [==============================] - 0s 887us/step - loss: 0.0791 - accuracy: 0.9757\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07912622392177582, 0.9757000207901001]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "patnaR9DrBA_",
        "outputId": "1ae4d74c-85e9-49b6-da71-d72e1d3463d0"
      },
      "source": [
        "plt.imshow(x_test[0], cmap=plt.cm.binary)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12t61rOArBBA",
        "outputId": "e72f924c-1b11-4e3d-e625-98c468065f3a"
      },
      "source": [
        "model.predict(np.expand_dims(x_test[0], axis=0)).argmax()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWU-XTAwrBBA",
        "outputId": "42bf8349-89a9-4736-9a00-ee32eb1bc4f4"
      },
      "source": [
        "model.predict(np.array([x_test[0]])).argmax()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "TtHd11-hrBBA",
        "outputId": "bb897fb5-6801-4abb-c8f2-079217c2f11d"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "for i in range(25):\n",
        "    plt.subplot(5, 5, i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.imshow(x_test[i], cmap=plt.cm.binary)\n",
        "    predicted = model.predict(np.expand_dims(x_test[i], axis=0)).argmax()\n",
        "    color = \"black\" if predicted == y_test[i] else \"red\"\n",
        "    plt.title(predicted, c=color)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mh4oME1arBBB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}