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kiransair/tf_forum_16323.ipynb Secret

Created April 20, 2023 13:18
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tf_forum_16323.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyPruzAmO7Aw8jL6U3xdveZa",
"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/gist/kiransair/c253c3304b7ae21b2e836a034f756087/tf_forum_16323.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "JxULNIKs0DQT"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\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"
]
},
{
"cell_type": "code",
"source": [
"import pathlib\n",
"dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\"\n",
"data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)\n",
"data_dir = pathlib.Path(data_dir)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "fPxVlWji0DlH",
"outputId": "8f403f9d-7822-4861-ca35-2c6fabd761f8"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\n",
"228813984/228813984 [==============================] - 2s 0us/step\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"batch_size = 32\n",
"img_height = 180\n",
"img_width = 180"
],
"metadata": {
"id": "zIOHJXI80FOf"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"source": [
"train_ds = tf.keras.utils.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)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "rxki177w0HZM",
"outputId": "ffdb49f8-13ac-425b-93aa-e6d93f84e1dd"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found 3670 files belonging to 5 classes.\n",
"Using 2936 files for training.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"for x,y in train_ds:\n",
" print(x.shape)\n",
" print(y.shape)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "IIfiY-aX0I8-",
"outputId": "6d339c10-d587-4cac-e103-609dd01cc1bd"
},
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(32, 180, 180, 3)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
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"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(32, 180, 180, 3)\n",
"(32,)\n",
"(24, 180, 180, 3)\n",
"(24,)\n"
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "flQyeGKP0Lfq"
},
"execution_count": null,
"outputs": []
}
]
}
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