tf_cnn.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": "3.8.5"
    },
    "colab": {
      "name": "tf_cnn.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "tV0ERF6VQpn3"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers, datasets, models\n",
        "from tensorflow.keras.models import Sequential"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SQUwwGUSQpos"
      },
      "source": [
        "## Prepare Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Md-nuU-BQpo3",
        "outputId": "9a9b9196-82e4-46f8-aa01-a4cdf48accb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "(train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()\n",
        "\n",
        "train_images = train_images.reshape((60000, 28, 28, 1))\n",
        "test_images = test_images.reshape((10000, 28, 28, 1))\n",
        "\n",
        "# Normalize pixel values to be between 0 and 1\n",
        "train_images, test_images = train_images / 255.0, test_images / 255.0\n",
        "\n",
        "print(\"TRAIN IMAGES: \", train_images.shape)\n",
        "print(\"TEST IMAGES: \", test_images.shape)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TRAIN IMAGES:  (60000, 28, 28, 1)\n",
            "TEST IMAGES:  (10000, 28, 28, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u85pikI_QppL"
      },
      "source": [
        "## Create Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABR85VMcQppM"
      },
      "source": [
        "num_classes = 10\n",
        "img_height = 28\n",
        "img_width = 28\n",
        "\n",
        "model = Sequential([\n",
        "    layers.Conv2D(64, (3, 3), activation='relu', input_shape=(28, 28, 1)),\n",
        "    layers.Conv2D(32, 3, padding='same', activation='relu'),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Conv2D(64, 3, padding='same', activation='relu'),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(128, activation='relu'),\n",
        "    layers.Dense(10, activation='sigmoid')\n",
        "])"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q273hPK2Qppc"
      },
      "source": [
        "## Compile Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K3OcHt-TQppf"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "eehWMg2sQppr",
        "outputId": "5981b510-ee66-4494-e6bc-c2d55e8d6d9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 26, 26, 64)        640       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 26, 26, 32)        18464     \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 13, 13, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 13, 13, 16)        4624      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 6, 6, 64)          9280      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 3, 3, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               73856     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 108,154\n",
            "Trainable params: 108,154\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "036GaKZGQpqE"
      },
      "source": [
        "## Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4hi0rW_QpqL",
        "outputId": "a08c9ce3-3b24-4dd2-ac2f-cddaca285002",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        }
      },
      "source": [
        "epochs = 10\n",
        "history = model.fit(\n",
        "  train_images, \n",
        "  train_labels,\n",
        "  epochs = epochs\n",
        ")"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.5233 - accuracy: 0.9378\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4818 - accuracy: 0.9771\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4766 - accuracy: 0.9821\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4751 - accuracy: 0.9833\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4730 - accuracy: 0.9851\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4740 - accuracy: 0.9844\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4751 - accuracy: 0.9807\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4734 - accuracy: 0.9831\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4738 - accuracy: 0.9822\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 1.4765 - accuracy: 0.9758\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J1ThXQxmQpqZ"
      },
      "source": [
        "## Visualize Training Results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThQIghvNQpqb",
        "outputId": "97cb4d08-1c11-40f5-9732-3dff55937a6a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        }
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "loss=history.history['loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, loss, label='Loss')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training Accuracy and Loss')\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Training Accuracy and Loss')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bLqZHZ1uVhpI"
      },
      "source": [
        "## Test Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUKvyxTgVgYN",
        "outputId": "62348f85-fb40-4761-d872-90fe597a6114",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "source": [
        "image = (train_images[1]).reshape(1,28,28,1)\n",
        "model_pred = model.predict_classes(image, verbose=0)\n",
        "plt.imshow(image.reshape(28,28))\n",
        "print('Prediction of model: {}'.format(model_pred[0]))"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From <ipython-input-8-590799ffdfa8>:2: Sequential.predict_classes (from tensorflow.python.keras.engine.sequential) is deprecated and will be removed after 2021-01-01.\n",
            "Instructions for updating:\n",
            "Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "Prediction of model: 0\n"
          ],
          "name": "stdout"
        },
        {
          "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": "26tM44tYTcAq",
        "outputId": "1a2166b6-57cf-466a-b049-73da63119782",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "image = (train_images[2]).reshape(1,28,28,1)\n",
        "model_pred = model.predict_classes(image, verbose=0)\n",
        "plt.imshow(image.reshape(28,28))\n",
        "print('Prediction of model: {}'.format(model_pred[0]))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction of model: 4\n"
          ],
          "name": "stdout"
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "pv4mRzFPV8RV"
      },
      "source": [
        "## Test Multiple Image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXdh59ABV4Hi",
        "outputId": "12f250e5-c117-4d31-a84b-cfe1264ec27f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "source": [
        "\n",
        "images = test_images[1:5]\n",
        "images = images.reshape(images.shape[0], 28, 28)\n",
        "print (\"Test images array shape: {}\".format(images.shape))\n",
        "\n",
        "\n",
        "for i, test_image in enumerate(images, start=1):\n",
        "\n",
        "    org_image = test_image\n",
        "    test_image = test_image.reshape(1,28,28,1)\n",
        "    prediction = model.predict_classes(test_image, verbose=0)\n",
        "\n",
        "    print (\"Predicted digit: {}\".format(prediction[0]))\n",
        "    plt.subplot(220+i)\n",
        "    plt.axis('off')\n",
        "    plt.title(\"Predicted digit: {}\".format(prediction[0]))\n",
        "    plt.imshow(org_image, cmap=plt.get_cmap('gray'))\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test images array shape: (4, 28, 28)\n",
            "Predicted digit: 2\n",
            "Predicted digit: 1\n",
            "Predicted digit: 0\n",
            "Predicted digit: 4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FphNanNsW8AL"
      },
      "source": [
        "## Save Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NXj_sZ1lV_S2"
      },
      "source": [
        "model.save(\"tf-cnn-model.h5\")"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9hVmcb9EXwDI"
      },
      "source": [
        "## Load Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6I1LBQaWsYx"
      },
      "source": [
        "loaded_model = models.load_model(\"tf-cnn-model.h5\")"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkSnt7uNX2fn",
        "outputId": "2b2a6d70-f3e0-4ec0-a695-a8fd2063e006",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "source": [
        "image = (train_images[2]).reshape(1,28,28,1)\n",
        "model_pred = loaded_model.predict_classes(image, verbose=0)\n",
        "plt.imshow(image.reshape(28,28))\n",
        "print('Prediction of model: {}'.format(model_pred[0]))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Prediction of model: 4\n"
          ],
          "name": "stdout"
        },
        {
          "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": "5b6Qa1-ZX_8j"
      },
      "source": [
        ""
      ],
      "execution_count": 13,
      "outputs": []
    }
  ]
}