CIFAR10-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2fd10783-86e4-408f-8072-c6285ba70cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import tensorflow_datasets as tfds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bb8c093a-1a37-402b-a1a5-53371d16bf74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDownloading and preparing dataset Unknown size (download: Unknown size, generated: Unknown size, total: Unknown size) to C:\\Users\\Kunda\\tensorflow_datasets\\cifar10\\3.0.2...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df5106d140f547368bfdaad293a3816e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Completed...: 0 url [00:00, ? url/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b7226784e6904ce29a28b7da95705d16",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dl Size...: 0 MiB [00:00, ? MiB/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "66ba1793392e45edbf628d5e8def2678",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Extraction completed...: 0 file [00:00, ? file/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating splits...:   0%|          | 0/2 [00:00<?, ? splits/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train examples...: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling C:\\Users\\Kunda\\tensorflow_datasets\\cifar10\\3.0.2.incomplete7CJ3KQ\\cifar10-train.tfrecord*...:   0%| …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating test examples...: 0 examples [00:00, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Shuffling C:\\Users\\Kunda\\tensorflow_datasets\\cifar10\\3.0.2.incomplete7CJ3KQ\\cifar10-test.tfrecord*...:   0%|  …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mDataset cifar10 downloaded and prepared to C:\\Users\\Kunda\\tensorflow_datasets\\cifar10\\3.0.2. Subsequent calls will reuse this data.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "#load dataset\n",
    "(ds_train, ds_test), ds_info = tfds.load(\n",
    "    'cifar10',\n",
    "    split=['train', 'test'],\n",
    "    shuffle_files=True,\n",
    "    as_supervised=True,\n",
    "    with_info=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "283c0067-f918-47b1-bb13-eddd2ba63b95",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create function to normalize images\n",
    "def norm_img(image, label):\n",
    "    return tf.cast(image, tf.float32)/255., label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "66de5e40-56ff-406d-823a-31a3f191cde2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build train input pipeline\n",
    "ds_train = ds_train.map(\n",
    "    norm_img, num_parallel_calls = tf.data.AUTOTUNE\n",
    ")\n",
    "ds_train = ds_train.cache()\n",
    "ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)\n",
    "ds_train = ds_train.batch(130)\n",
    "ds_train = ds_train.prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9b0feb76-30e8-4bda-addf-429bf6c83ac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build test pipeline\n",
    "ds_test = ds_test.map(\n",
    "    norm_img, num_parallel_calls = tf.data.AUTOTUNE\n",
    ")\n",
    "ds_test = ds_test.batch(130)\n",
    "ds_test = ds_test.cache()\n",
    "ds_test = ds_test.prefetch(tf.data.AUTOTUNE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "16ed07a4-005e-46b0-91f4-938afeb97541",
   "metadata": {},
   "outputs": [],
   "source": [
    "#build, compile & fit model\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(32, 32, 3)),\n",
    "    tf.keras.layers.Dense(170, activation='relu'),\n",
    "    tf.keras.layers.Dense(30),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1953f348-adfb-409f-8d40-2cb9f9c04e7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compile model\n",
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "    metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "51b09d24-5b28-42f4-b5a1-b7de7fcd738e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 17ms/step - loss: 2.1108 - sparse_categorical_accuracy: 0.2690 - val_loss: 1.8503 - val_sparse_categorical_accuracy: 0.3341\n",
      "Epoch 2/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - loss: 1.7899 - sparse_categorical_accuracy: 0.3658 - val_loss: 1.7604 - val_sparse_categorical_accuracy: 0.3821\n",
      "Epoch 3/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 16ms/step - loss: 1.7076 - sparse_categorical_accuracy: 0.3946 - val_loss: 1.6861 - val_sparse_categorical_accuracy: 0.4048\n",
      "Epoch 4/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - loss: 1.6539 - sparse_categorical_accuracy: 0.4109 - val_loss: 1.6092 - val_sparse_categorical_accuracy: 0.4289\n",
      "Epoch 5/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 17ms/step - loss: 1.6082 - sparse_categorical_accuracy: 0.4314 - val_loss: 1.6139 - val_sparse_categorical_accuracy: 0.4281\n",
      "Epoch 6/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 16ms/step - loss: 1.5834 - sparse_categorical_accuracy: 0.4417 - val_loss: 1.5595 - val_sparse_categorical_accuracy: 0.4478\n",
      "Epoch 7/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 18ms/step - loss: 1.5447 - sparse_categorical_accuracy: 0.4528 - val_loss: 1.5672 - val_sparse_categorical_accuracy: 0.4465\n",
      "Epoch 8/8\n",
      "\u001b[1m385/385\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 16ms/step - loss: 1.5254 - sparse_categorical_accuracy: 0.4639 - val_loss: 1.5538 - val_sparse_categorical_accuracy: 0.4476\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x28a40a94cd0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fit model\n",
    "model.fit(\n",
    "    ds_train,\n",
    "    epochs=8,\n",
    "    validation_data=ds_test,\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}