fashion_recognition_with_dnn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%reset -f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.4.1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.fashion_mnist;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "(training_images, training_labels), (test_images, test_labels) = mnist.load_data();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_images = training_images / 255.0;\n",
    "test_images = test_images / 255.0;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([tf.keras.layers.Flatten(),\n",
    "                                    tf.keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "                                    tf.keras.layers.Dense(10, activation=tf.nn.softmax)]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.6327 - accuracy: 0.7806\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3896 - accuracy: 0.8600\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3379 - accuracy: 0.8765\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3132 - accuracy: 0.8839\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2931 - accuracy: 0.8920\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer = tf.optimizers.Adam(),\n",
    "             loss = 'sparse_categorical_crossentropy',\n",
    "             metrics=['accuracy']);\n",
    "model.fit(training_images, training_labels, epochs=5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 2ms/step - loss: 0.3495 - accuracy: 0.8748\n"
     ]
    }
   ],
   "source": [
    "model.evaluate(test_images, test_labels);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np;\n",
    "np.set_printoptions(linewidth=200);\n",
    "import matplotlib.pyplot as plt;\n",
    "plt.imshow(training_images[42]);\n",
    "print(training_labels[42]);\n",
    "print(training_images[42]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2.8777869e-08 2.2384327e-12 1.2080282e-11 4.1490127e-11 2.7494791e-11 2.0149492e-03 1.7486158e-07 2.8295120e-02 4.1872067e-10 9.6968967e-01]\n"
     ]
    }
   ],
   "source": [
    "classifications = model.predict(test_images);\n",
    "print(classifications[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(test_labels[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(test_images[0]);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}