classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python382jvsc74a57bd0083423ecd8117d739a52e46879ebb0d2ebd3c78434762fd7c17c9091748c8df0",
      "display_name": "Python 3.8.2 64-bit ('env_hacktj': venv)"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "L1WtoaOHVrVh"
      },
      "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\n",
        "from tensorflow.keras.models import Sequential"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "57CcilYSG0zv"
      },
      "source": [
        "import pathlib\n",
        "data_dir = pathlib.Path('./pre_processed_training_data')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbtTDYhOHZb6"
      },
      "source": [
        "image_count = len(list(data_dir.glob('*/*.jpg')))\n",
        "print(image_count)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "24000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PVmwkOSdHZ5A"
      },
      "source": [
        "Here are some roses:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N1loMlbYHeiJ"
      },
      "source": [
        "roses = list(data_dir.glob('brown/*'))\n",
        "PIL.Image.open(str(roses[0]))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=54x98 at 0x7F96E429CFA0>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGEqiBbRHnyI"
      },
      "source": [
        "And some tulips:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HyQkfPGdHilw"
      },
      "source": [
        "tulips = list(data_dir.glob('yellow/*'))\n",
        "PIL.Image.open(str(tulips[0]))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=54x98 at 0x7F96E8D28610>"
            ],
            "image/png": "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\n"
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "anqiK_AGI086"
      },
      "source": [
        "Define some parameters for the loader:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H74l2DoDI2XD"
      },
      "source": [
        "batch_size = 32\n",
        "img_height = 98\n",
        "img_width = 54"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFBhRrrEI49z"
      },
      "source": [
        "It's good practice to use a validation split when developing your model. Let's use 80% of the images for training, and 20% for validation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIR0kRZiI_AT"
      },
      "source": [
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"training\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 24000 files belonging to 6 classes.\nUsing 19200 files for training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iscU3UoVJBXj"
      },
      "source": [
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "  data_dir,\n",
        "  validation_split=0.2,\n",
        "  subset=\"validation\",\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  batch_size=batch_size)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 24000 files belonging to 6 classes.\nUsing 4800 files for validation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLQULyAvJC3X"
      },
      "source": [
        "You can find the class names in the `class_names` attribute on these datasets. These correspond to the directory names in alphabetical order."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZHAxkHX5JD3k"
      },
      "source": [
        "class_names = train_ds.class_names\n",
        "print(class_names)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Blue', 'Brown', 'Empty', 'Green', 'Purple', 'Yellow']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_uoVvxSLJW9m"
      },
      "source": [
        "## Visualize the data\n",
        "\n",
        "Here are the first 9 images from the training dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wBmEA9c0JYes"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for images, labels in train_ds.take(1):\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(class_names[labels[i]])\n",
        "    plt.axis(\"off\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x720 with 9 Axes>",
            "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<svg height=\"573.118125pt\" version=\"1.1\" viewBox=\"0 0 496.380792 573.118125\" width=\"496.380792pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-04-11T13:03:19.417203</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.4.1, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5M6BXtXFJdW0"
      },
      "source": [
        "You will train a model using these datasets by passing them to `model.fit` in a moment. If you like, you can also manually iterate over the dataset and retrieve batches of images:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2-MfMoenJi8s"
      },
      "source": [
        "for image_batch, labels_batch in train_ds:\n",
        "  print(image_batch.shape)\n",
        "  print(labels_batch.shape)\n",
        "  break"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32, 98, 54, 3)\n(32,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wj4FrKxxJkoW"
      },
      "source": [
        "The `image_batch` is a tensor of the shape `(32, 180, 180, 3)`. This is a batch of 32 images of shape `180x180x3` (the last dimension refers to color channels RGB). The `label_batch` is a tensor of the shape `(32,)`, these are corresponding labels to the 32 images. \n",
        "\n",
        "You can call `.numpy()` on the `image_batch` and `labels_batch` tensors to convert them to a `numpy.ndarray`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4Dr0at41KcAU"
      },
      "source": [
        "## Configure the dataset for performance\n",
        "\n",
        "Let's make sure to use buffered prefetching so you can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data.\n",
        "\n",
        "`Dataset.cache()` keeps the images in memory after they're loaded off disk during the first epoch. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.\n",
        "\n",
        "`Dataset.prefetch()` overlaps data preprocessing and model execution while training. \n",
        "\n",
        "Interested readers can learn more about both methods, as well as how to cache data to disk in the [data performance guide](https://www.tensorflow.org/guide/data_performance#prefetching)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOjJSm7DKoZA"
      },
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8GUnmPF4JvEf"
      },
      "source": [
        "## Standardize the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e56VXHMWJxYT"
      },
      "source": [
        "The RGB channel values are in the `[0, 255]` range. This is not ideal for a neural network; in general you should seek to make your input values small. Here, you will standardize values to be in the `[0, 1]` range by using a Rescaling layer."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PEYxo2CTJvY9"
      },
      "source": [
        "normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8aGpkwFaIw4i"
      },
      "source": [
        "Note: The Keras Preprocessing utilities and layers introduced in this section are currently experimental and may change."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bl4RmanbJ4g0"
      },
      "source": [
        "There are two ways to use this layer. You can apply it to the dataset by calling map:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9o9ESaJJ502"
      },
      "source": [
        "normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))\n",
        "image_batch, labels_batch = next(iter(normalized_ds))\n",
        "first_image = image_batch[0]\n",
        "# Notice the pixels values are now in `[0,1]`.\n",
        "print(np.min(first_image), np.max(first_image)) "
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2784314 0.5058824\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XWEOmRSBJ9J8"
      },
      "source": [
        "Or, you can include the layer inside your model definition, which can simplify deployment. Let's use the second approach here."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsRk1xCwKZR4"
      },
      "source": [
        "Note: you previously resized images using the `image_size` argument of `image_dataset_from_directory`. If you want to include the resizing logic in your model as well, you can use the [Resizing](https://www.tensorflow.org/api_docs/python/tf/keras/layers/experimental/preprocessing/Resizing) layer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WcUTyDOPKucd"
      },
      "source": [
        "# Create the model\n",
        "\n",
        "The model consists of three convolution blocks with a max pool layer in each of them. There's a fully connected layer with 128 units on top of it that is activated by a `relu` activation function. This model has not been tuned for high accuracy, the goal of this tutorial is to show a standard approach. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QR6argA1K074"
      },
      "source": [
        "num_classes = 6\n",
        "\n",
        "model = Sequential([\n",
        "  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),\n",
        "  layers.Conv2D(16, 3, padding='same', activation='relu'),\n",
        "  layers.MaxPooling2D(),\n",
        "  layers.Conv2D(32, 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(num_classes)\n",
        "])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaKFzz72Lqpg"
      },
      "source": [
        "## Compile the model\n",
        "\n",
        "For this tutorial, choose the `optimizers.Adam` optimizer and `losses.SparseCategoricalCrossentropy` loss function. To view training and validation accuracy for each training epoch, pass the `metrics` argument."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jloGNS1MLx3A"
      },
      "source": [
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aMJ4DnuJL55A"
      },
      "source": [
        "## Model summary\n",
        "\n",
        "View all the layers of the network using the model's `summary` method:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "llLYH-BXL7Xe"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n_________________________________________________________________\nLayer (type)                 Output Shape              Param #   \n=================================================================\nrescaling_1 (Rescaling)      (None, 98, 54, 3)         0         \n_________________________________________________________________\nconv2d (Conv2D)              (None, 98, 54, 16)        448       \n_________________________________________________________________\nmax_pooling2d (MaxPooling2D) (None, 49, 27, 16)        0         \n_________________________________________________________________\nconv2d_1 (Conv2D)            (None, 49, 27, 32)        4640      \n_________________________________________________________________\nmax_pooling2d_1 (MaxPooling2 (None, 24, 13, 32)        0         \n_________________________________________________________________\nconv2d_2 (Conv2D)            (None, 24, 13, 64)        18496     \n_________________________________________________________________\nmax_pooling2d_2 (MaxPooling2 (None, 12, 6, 64)         0         \n_________________________________________________________________\nflatten (Flatten)            (None, 4608)              0         \n_________________________________________________________________\ndense (Dense)                (None, 128)               589952    \n_________________________________________________________________\ndense_1 (Dense)              (None, 6)                 774       \n=================================================================\nTotal params: 614,310\nTrainable params: 614,310\nNon-trainable params: 0\n_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NiYHcbvaL9H-"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fWToCqYMErH"
      },
      "source": [
        "epochs=8\n",
        "history = model.fit(\n",
        "  train_ds,\n",
        "  validation_data=val_ds,\n",
        "  epochs=epochs\n",
        ")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/8\n",
            "600/600 [==============================] - 69s 114ms/step - loss: 0.2012 - accuracy: 0.9400 - val_loss: 2.1711e-05 - val_accuracy: 1.0000\n",
            "Epoch 2/8\n",
            "600/600 [==============================] - 72s 119ms/step - loss: 1.6363e-05 - accuracy: 1.0000 - val_loss: 4.9071e-06 - val_accuracy: 1.0000\n",
            "Epoch 3/8\n",
            "600/600 [==============================] - 73s 122ms/step - loss: 4.1929e-06 - accuracy: 1.0000 - val_loss: 1.7055e-06 - val_accuracy: 1.0000\n",
            "Epoch 4/8\n",
            "600/600 [==============================] - 75s 125ms/step - loss: 1.6657e-06 - accuracy: 1.0000 - val_loss: 7.9669e-07 - val_accuracy: 1.0000\n",
            "Epoch 5/8\n",
            "600/600 [==============================] - 64s 107ms/step - loss: 7.1828e-07 - accuracy: 1.0000 - val_loss: 4.1251e-07 - val_accuracy: 1.0000\n",
            "Epoch 6/8\n",
            "600/600 [==============================] - 61s 102ms/step - loss: 4.4394e-07 - accuracy: 1.0000 - val_loss: 2.4386e-07 - val_accuracy: 1.0000\n",
            "Epoch 7/8\n",
            "600/600 [==============================] - 63s 105ms/step - loss: 2.4332e-07 - accuracy: 1.0000 - val_loss: 1.5244e-07 - val_accuracy: 1.0000\n",
            "Epoch 8/8\n",
            "600/600 [==============================] - 65s 109ms/step - loss: 1.5427e-07 - accuracy: 1.0000 - val_loss: 9.8124e-08 - val_accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SyFKdQpXMJT4"
      },
      "source": [
        "## Visualize training results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dFvOvmAmMK9w"
      },
      "source": [
        "Create plots of loss and accuracy on the training and validation sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWnopEChMMCn"
      },
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtv5VbaVb-3W"
      },
      "source": [
        "## Predict on new data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dC40sRITBSsQ"
      },
      "source": [
        "# sunflower_url = \"https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg\"\n",
        "# sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)\n",
        "\n",
        "sunflower_path = '/Users/akashpamal/Documents/TJHSST/Grade11/CurbsideClassifier/pre_processed_training_data/Blue/image_67.jpg'\n",
        "\n",
        "img = keras.preprocessing.image.load_img(\n",
        "    sunflower_path, target_size=(img_height, img_width)\n",
        ")\n",
        "img_array = keras.preprocessing.image.img_to_array(img)\n",
        "img_array = tf.expand_dims(img_array, 0) # Create a batch\n",
        "\n",
        "predictions = model.predict(img_array)\n",
        "score = tf.nn.softmax(predictions[0])\n",
        "\n",
        "print(\n",
        "    \"This image most likely belongs to {} with a {:.2f} percent confidence.\"\n",
        "    .format(class_names[np.argmax(score)], 100 * np.max(score))\n",
        ")"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This image most likely belongs to Blue with a 100.00 percent confidence.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {},
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "INFO:tensorflow:Assets written to: saved_model_1/assets\n"
          ]
        }
      ],
      "source": [
        "model.save('saved_model_1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    }
  ]
}