|
|
{ "cells": [ { "cell_type": "markdown", "id": "2eaba66b", "metadata": {}, "source": [ "Read and Display Horse or Human machine learning dataset" ] }, { "cell_type": "code", "execution_count": null, "id": "f1e48ac0", "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "import numpy as np\n", "import tensorflow_datasets as tfds\n", "from tensorflow.keras import regularizers\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": null, "id": "feda024e", "metadata": {}, "outputs": [], "source": [ "# Load the horse or human dataset\n", "#(300, 300, 3) unint8\n", "dataset, label = tfds.load('horses_or_humans', with_info=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "35991dec", "metadata": {}, "outputs": [], "source": [ "# Extract the horse/human class\n", "horse_ds = dataset['train'].filter(lambda x: x['label'] == 0)\n", "human_ds = dataset['train'].filter(lambda x: x['label'] == 1)" ] }, { "cell_type": "code", "execution_count": null, "id": "fab03aa8", "metadata": {}, "outputs": [], "source": [ "# Take a few examples < 16\n", "n_examples = 5\n", "horse_examples = horse_ds.take(n_examples)\n", "human_examples = human_ds.take(n_examples)" ] }, { "cell_type": "code", "execution_count": null, "id": "c33f1acd", "metadata": {}, "outputs": [], "source": [ "# Display the examples\n", "fig, axes = plt.subplots(1, n_examples, figsize=(15, 15))\n", "for i, example in enumerate(human_examples):\n", " image = example['image']\n", " axes[i].imshow(image)\n", " axes[i].set_title(f\"humans {i+1}\")\n", "plt.show()\n", "\n", "fig, axes = plt.subplots(1, n_examples, figsize=(15, 15))\n", "for i, example in enumerate(horse_examples):\n", " image = example['image']\n", " axes[i].imshow(image)\n", " axes[i].set_title(f\"horses {i+1}\")\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": null, "id": "25f3eeb3", "metadata": {}, "outputs": [], "source": [ "# Split the dataset into training and validation sets\n", "# as_supervised: Specifies whether to return the dataset as a tuple\n", "# of (input, label) pairs.\n", "train_dataset, valid_dataset = tfds.load('horses_or_humans', split=['train','test'], as_supervised=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "29dc0e62", "metadata": {}, "outputs": [], "source": [ "# Get the number of elements in the training and validation dataset\n", "train_size = tf.data.experimental.cardinality(train_dataset).numpy()\n", "valid_size = tf.data.experimental.cardinality(valid_dataset).numpy()\n", "print(\"Training dataset size:\", train_size)\n", "print(\"Validation dataset size:\", valid_size)" ] }, { "cell_type": "code", "execution_count": null, "id": "db8aaf91", "metadata": {}, "outputs": [], "source": [ "IMG_SIZE = 300\n", "NUM_CLASSES = 2\n", "\n", "def preprocess(image, label):\n", " image = tf.cast(image, tf.float32)\n", "# # Resize the images to a fixed size\n", " image = tf.image.resize(image, (IMG_SIZE, IMG_SIZE))\n", "# # Rescale the pixel values to be between 0 and 1\n", " image = image / 255.0\n", " label = tf.one_hot(label, NUM_CLASSES)\n", " return image, label" ] }, { "cell_type": "code", "execution_count": null, "id": "d59661c3", "metadata": {}, "outputs": [], "source": [ "# Apply the preprocessing function to the datasets\n", "train_dataset = train_dataset.map(preprocess)\n", "valid_dataset = valid_dataset.map(preprocess)\n", "\n", "# Batch and shuffle the datasets\n", "train_dataset = train_dataset.shuffle(1000).batch(80)\n", "valid_dataset = valid_dataset.batch(20)" ] }, { "cell_type": "code", "execution_count": null, "id": "13af7d53", "metadata": {}, "outputs": [], "source": [ "# Store images and labels of the validation data for predictions\n", "for images, labels in valid_dataset:\n", " x_val = images\n", " y_val = labels\n", " \n", "print(x_val.shape, y_val.shape)" ] }, { "cell_type": "code", "execution_count": null, "id": "67e152ff-0713-4629-8471-1afbb1bf22a6", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:ML]", "language": "python", "name": "conda-env-ML-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.9" } }, "nbformat": 4, "nbformat_minor": 5}
|
