lab3_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "from keras.datasets import fashion_mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
      "29515/29515 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
      "26421880/26421880 [==============================] - 7s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
      "5148/5148 [==============================] - 0s 0us/step\n",
      "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
      "4422102/4422102 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context\n",
    "# Load the dataset\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the pixel values to be between 0 and 1\n",
    "x_train = x_train.astype('float32') / 255\n",
    "x_test = x_test.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the labels to one-hot encoded vectors\n",
    "num_classes = 10\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build the model\n",
    "model = keras.Sequential([\n",
    "    layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)),\n",
    "    layers.MaxPooling2D((2,2)),\n",
    "    layers.Conv2D(64, (3,3), activation='relu'),\n",
    "    layers.MaxPooling2D((2,2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(128, activation='relu'),\n",
    "    layers.Dense(num_classes, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer='adam',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1875/1875 [==============================] - 42s 22ms/step - loss: 0.4482 - accuracy: 0.8370 - val_loss: 0.3396 - val_accuracy: 0.8744\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 40s 21ms/step - loss: 0.3007 - accuracy: 0.8890 - val_loss: 0.3058 - val_accuracy: 0.8884\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 40s 21ms/step - loss: 0.2527 - accuracy: 0.9065 - val_loss: 0.2837 - val_accuracy: 0.8954\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 40s 21ms/step - loss: 0.2211 - accuracy: 0.9176 - val_loss: 0.2597 - val_accuracy: 0.9040\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 40s 21ms/step - loss: 0.1944 - accuracy: 0.9270 - val_loss: 0.2541 - val_accuracy: 0.9103\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 40s 21ms/step - loss: 0.1710 - accuracy: 0.9346 - val_loss: 0.2493 - val_accuracy: 0.9119\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 41s 22ms/step - loss: 0.1511 - accuracy: 0.9431 - val_loss: 0.2688 - val_accuracy: 0.9097\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 42s 22ms/step - loss: 0.1310 - accuracy: 0.9507 - val_loss: 0.2764 - val_accuracy: 0.9055\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 41s 22ms/step - loss: 0.1169 - accuracy: 0.9557 - val_loss: 0.2817 - val_accuracy: 0.9144\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 41s 22ms/step - loss: 0.1031 - accuracy: 0.9603 - val_loss: 0.2955 - val_accuracy: 0.9083\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x15ae3bb50>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model\n",
    "model.fit(x_train.reshape(-1,28,28,1), y_train, epochs=10, batch_size=32, validation_data=(x_test.reshape(-1,28,28,1), y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 - 1s - loss: 0.2955 - accuracy: 0.9083 - 1s/epoch - 5ms/step\n",
      "Test accuracy: 0.90829998254776\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "test_loss, test_acc = model.evaluate(x_test.reshape(-1,28,28,1), y_test, verbose=2)\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### THANK YOU"
   ]
  }
 ],
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