official_accuracy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\adria\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\adria\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\adria\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n",
      "Epoch 1/10\n",
      "WARNING:tensorflow:From C:\\Users\\adria\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\adria\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "1875/1875 [==============================] - 10s 5ms/step - loss: 0.4975 - accuracy: 0.8255\n",
      "Epoch 2/10\n",
      "1875/1875 [==============================] - 5s 3ms/step - loss: 0.3737 - accuracy: 0.8659\n",
      "Epoch 3/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.3374 - accuracy: 0.8778\n",
      "Epoch 4/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3105 - accuracy: 0.8861\n",
      "Epoch 5/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2943 - accuracy: 0.8917\n",
      "Epoch 6/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2797 - accuracy: 0.8969\n",
      "Epoch 7/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2689 - accuracy: 0.9006\n",
      "Epoch 8/10\n",
      "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2557 - accuracy: 0.9044\n",
      "Epoch 9/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2469 - accuracy: 0.9080\n",
      "Epoch 10/10\n",
      "1875/1875 [==============================] - 7s 4ms/step - loss: 0.2378 - accuracy: 0.9113\n",
      "313/313 - 0s - loss: 0.3429 - accuracy: 0.8809 - 486ms/epoch - 2ms/step\n",
      "\n",
      "Test accuracy: 0.8809000253677368\n"
     ]
    }
   ],
   "source": [
    "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n",
    "\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n",
    "\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(25):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])\n",
    "plt.show()\n",
    "\n",
    "\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    tf.keras.layers.Dense(128, activation='relu'),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "model.fit(train_images, train_labels, epochs=10)\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  }
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