IT496_ViktoriiaO_FirstNeuralNetwork.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IT496_ViktoriiaO_FirstNeuralNetwork.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMdQn2AcrY9Z8x6dxK8Kl/Q",
      "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/github/vovsiienko/IT496_Course_acitvities/blob/master/IT496_ViktoriiaO_FirstNeuralNetwork.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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jfbtklydAN8",
        "outputId": "f0aaee5f-9a64-49e6-c8d8-0dd73db157bc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.8.2\n"
          ]
        }
      ],
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fashion_mnist=keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
        "\n",
        "class_names=['T-shirt/top', 'Trousers', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneakers', 'Bag', 'Ankel boot']\n",
        "\n",
        "print('Size of the mage tain dataset: ', train_images.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2eaDYYgteGGV",
        "outputId": "43b60bfe-49b9-4b3a-cb52-1f5d83822831"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "26435584/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n",
            "Size of the mage tain dataset:  (60000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Length of the labels: ', len(train_labels))\n",
        "print('These are labels: ', train_labels)\n",
        "print('Size of the test dataset: ', test_images.shape)\n",
        "print('Length of the labels for test data: ', len(test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MolBBNQSfANb",
        "outputId": "0878e531-0369-417c-deef-ef8b312289a5"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of the labels:  60000\n",
            "These are labels:  [9 0 0 ... 3 0 5]\n",
            "Size of the test dataset:  (10000, 28, 28)\n",
            "Length of the labels for test data:  10000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure()\n",
        "plt.imshow(train_images[1])\n",
        "plt.colorbar()\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "AJEbGa9kf8-N",
        "outputId": "0ad95e7c-76b7-4229-945e-8dc87c9efa15"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_images=train_images/255.0\n",
        "test_images=test_images/255.0\n",
        "plt.figure()\n",
        "plt.imshow(train_images[1], cmap=plt.cm.binary)\n",
        "plt.grid(False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "jHvmgORVgEyU",
        "outputId": "49658dda-c55e-4206-e389-4be3d2ae1ee7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARqklEQVR4nO3dXYyV5bUH8P8SGEAG+ZBxBMFDJZAomgLZGU8saTxpTlFugBtSLionNtILSVpDYo1e1JuTqJH21ORIpEpKj9XSSDliYk7wIAQbTeMGQUA4Dhq0AsLwLfIN61zMSzPFedca9rPf/W5n/X/JZGb22u/ez2zmz7tnr/08j6gqiKj/u67sARBRYzDsREEw7ERBMOxEQTDsREEMbOSdjRkzRidOnNjIu+wXzp49a9Y///zz3NqoUaPMY6+//nqzLiJJdWvsx44dM48dPHiwWb/55pvN+oABA8x6f7R3714cPny413+UpLCLyH0AfgNgAIAXVfUp6/oTJ05EtVpNucvCeC1I75e6SLt27TLrixcvzq3Nnz/fPHb69OlmvaWlxawPHGj/Cu3cuTO3tmbNGvPY2267zaw/+uijZn3kyJFmvT+qVCq5tZqfxovIAAD/CeB+AHcAWCAid9R6e0RUrJS/2TsA7FHVT1X1PIA/AphTn2ERUb2lhP0WAH/r8f0X2WX/QEQWiUhVRKpdXV0Jd0dEKQp/NV5Vl6tqRVUrbW1tRd8dEeVICfs+ABN6fD8+u4yImlBK2N8HMFlEviMiLQB+BGBtfYZFRPUmKbPeRGQ2gP9Ad+tthar+u3X9SqWiRbXeymydffDBB2Z91apVZn316tVm3esXnzp1Krd25swZ89ijR4+a9SJNmTLFrF93nX0u2r17t1m3+vCzZs0yj12yZIlZv+uuu8x6WSqVCqrVav377Kr6JoA3U26DiBqDb5clCoJhJwqCYScKgmEnCoJhJwqCYScKoqHz2YuU2kc/efKkWX/ggQdya9u2bTOP9d4D0NraataHDh1q1q05616P/uLFi2b9xIkTZt2bD2/df+q/WUdHh1m35tK/++675rEbN2406zNnzjTrL7/8slkvA8/sREEw7ERBMOxEQTDsREEw7ERBMOxEQfSb1luqefPmmXVrueb29nbzWK/FdOnSJbOesiSyd9teW/DGG29Muv2U+05ltSyHDBliHuv9m73zzjtm3VsR+PbbbzfrReCZnSgIhp0oCIadKAiGnSgIhp0oCIadKAiGnSiIMH32zZs3m3Wrjw4AY8aMya1500Q93nLP+/bZe29Yx1++fNk81tuF1euje8s9W86fP2/WBw0aZNaHDx9u1sePH59b835uj/dzv/jii2Z96dKlSfdfC57ZiYJg2ImCYNiJgmDYiYJg2ImCYNiJgmDYiYII02ffsGGDWT937pxZt5Yl9nquXq978ODBZv2ZZ54x62PHjs2tTZgwwTx2//79Nd824P9sVq/c67NbW1EDwJYtW8z6c889l1tra2szj71w4YJZ9/7NvW24y+izJ4VdRPYC+ArAJQAXVbVSj0ERUf3V48z+L6p6uA63Q0QF4t/sREGkhl0BrBORzSKyqLcriMgiEamKSLWrqyvx7oioVqlhn6mqMwDcD+BhEfn+1VdQ1eWqWlHViveiCBEVJynsqrov+3wIwBoA9k57RFSamsMuIsNEZPiVrwH8EMCOeg2MiOor5dX4dgBrsvW1BwJ4RVX/py6jKsBrr71m1r212a1+sjc3+vTp02Z9xIgRZv2hhx4y6+vWrcutefP4H3zwQbP+wgsvmPWpU6eadev9Cd5c+ZtuusmsP/LII2b9+eefz615fXRr3AAwbNgws757926z/vHHH+fWpkyZYh5bq5rDrqqfAvhuHcdCRAVi640oCIadKAiGnSgIhp0oCIadKIgwU1y3bdtm1r2poFabyJse6zlx4kTS8bNmzcqttba2msd6Wws/++yzZt3b6vqNN97IrXlLcE+fPt2se1NcrZao1w71prB6de/36b333sutFdV645mdKAiGnSgIhp0oCIadKAiGnSgIhp0oCIadKIh+02ffvn27WfdWyfGmuFp9dm+qprcl8+jRo826Z+fOnbk1b5nqAwcOmPUnnnjCrKuqWbeWkvaOtXrRfWEtg+0toe39PmRTu3MNHTrUrG/atCm3tnDhQvPYWvHMThQEw04UBMNOFATDThQEw04UBMNOFATDThREv+mzP/3002bd63V7SwOnzI0eMmSIWbd60QBQrVbN+pEjR3JrR48eNY/1llQ+ePCgWffGbv3s3pbNx48fN+urVq0y68eOHcuteX1w7769473H1Vviuwg8sxMFwbATBcGwEwXBsBMFwbATBcGwEwXBsBMF0W/67Pfcc49Z9/rFe/bsMevW2u5en33y5Mlm3VuD/O677zbr1tzr1PXPra2qAb+fbM1Z97a69tYJuOGGG8y6tf76119/bR7r/dzeXPxx48aZ9blz55r1IrhndhFZISKHRGRHj8tGi8hbItKZfR5V7DCJKFVfnsb/DsB9V132GID1qjoZwPrseyJqYm7YVXUTgKvfczkHwMrs65UAGv+chIiuSa0v0LWr6pXFy74E0J53RRFZJCJVEal2dXXVeHdElCr51XjtfqUi99UKVV2uqhVVrXiLPhJRcWoN+0ERGQsA2edD9RsSERWh1rCvBXBlvduFAF6vz3CIqCji9QtF5FUA9wIYA+AggF8C+G8AfwJwK4DPAMxXVXviNIBKpaLe3OyyWHOfAaCzszO3tmzZMvPYjRs3mvVbb73VrHv7t48cOTK35s0Z9/rJRfJ+97yxeesEWI/bnXfeaR77yiuvmPVmValUUK1We13U3n1TjaouyCn9IGlURNRQfLssURAMO1EQDDtREAw7URAMO1EQ/WaKa6pRo+yJex0dHbk1b1vkt99+26x72/+eO3fOrFvTNS9evGge601x9XjtM6vu3bf3c3vLWJ89eza35k2J7o94ZicKgmEnCoJhJwqCYScKgmEnCoJhJwqCYScKIkyf3esHe0sit7S05Na8Pvnw4cPNurdksrVUdF/u39KHKc4133bRUqbnWtOC+8L7N/PeQ1DG48ozO1EQDDtREAw7URAMO1EQDDtREAw7URAMO1EQYfrsXl/TmxttmTRpkln3thb25pxbPX6P93M3c5/d+7m9ZbItI0aMqPlYwO/xe++NKAPP7ERBMOxEQTDsREEw7ERBMOxEQTDsREEw7ERBhOmze1L6pkOHDjWP9daVt9Y3B/z3AFhz8VP76CnrwgNpc869LZlPnz5t1q2xNWMfvGjumV1EVojIIRHZ0eOyJ0Vkn4hszT5mFztMIkrVl6fxvwNwXy+X/1pVp2Ufb9Z3WERUb27YVXUTgKMNGAsRFSjlBbrFIvJh9jQ/d6M0EVkkIlURqXZ1dSXcHRGlqDXsywBMAjANwAEAS/OuqKrLVbWiqpW2trYa746IUtUUdlU9qKqXVPUygN8CyN/ilIiaQk1hF5GxPb6dB2BH3nWJqDm4fXYReRXAvQDGiMgXAH4J4F4RmQZAAewF8NMCx9gQKfO2vTXCU9cQT+2Fp9x2Sp8csMeWMm7Af1yttd1T96Vv5vX087hhV9UFvVz8UgFjIaIC8e2yREEw7ERBMOxEQTDsREEw7ERBcIprA+zfv9+se9sHe9sDW1KnqJbJG5s39dc63lu+uz/imZ0oCIadKAiGnSgIhp0oCIadKAiGnSgIhp0oCPbZM0VOWUxdttjbmtiarpnaZy9yKWrvWO/n9pbotm4/tc/+bZziyjM7URAMO1EQDDtREAw7URAMO1EQDDtREAw7URDsszeA1w9O2S7aOz51GWuvH+3NKbdu35un741t4MDaf32PHz9e87HfVjyzEwXBsBMFwbATBcGwEwXBsBMFwbATBcGwEwXBPnsDpM5n96TMGfd4vfCUXnfqVtTe8dZ7AM6cOWMe6+mX89lFZIKIbBCRj0Rkp4j8LLt8tIi8JSKd2edRxQ+XiGrVl6fxFwEsUdU7APwzgIdF5A4AjwFYr6qTAazPvieiJuWGXVUPqOqW7OuvAOwCcAuAOQBWZldbCWBuUYMkonTX9AKdiEwEMB3AXwG0q+qBrPQlgPacYxaJSFVEql1dXQlDJaIUfQ67iLQCWA3g56p6smdNu19J6fXVFFVdrqoVVa20tbUlDZaIatensIvIIHQH/Q+q+ufs4oMiMjarjwVwqJghElE9uH0T6e4xvARgl6r+qkdpLYCFAJ7KPr9eyAj7gZQtl/uiyDZQkVs6e+P2pv56x1stz9OnT5vH9kd9aZJ+D8CPAWwXka3ZZY+jO+R/EpGfAPgMwPxihkhE9eCGXVX/AiDvv9Af1Hc4RFQUvl2WKAiGnSgIhp0oCIadKAiGnSgITnHNlDll0esnFym1j57yHoLUKa7e42ZNvy36vQ/NiGd2oiAYdqIgGHaiIBh2oiAYdqIgGHaiIBh2oiDYZ8+kLltsaWlpMeupyxpbvC2bi9wuui/3b0ntw1tjT+2z98ulpImof2DYiYJg2ImCYNiJgmDYiYJg2ImCYNiJgmCfvQmk9rqtfrN326l1r4+eMl8+dV15C+ezE1G/xbATBcGwEwXBsBMFwbATBcGwEwXBsBMF0Zf92ScA+D2AdgAKYLmq/kZEngTwEICu7KqPq+qbRQ20aEXOTx43bpxZ7+zsNOvW+ueA3ev2+uDnz5+v+bYB/3Gz6t7PdeHCBbOeIuJ89r68qeYigCWqukVEhgPYLCJvZbVfq+qzxQ2PiOqlL/uzHwBwIPv6KxHZBeCWogdGRPV1TX+zi8hEANMB/DW7aLGIfCgiK0RkVM4xi0SkKiLVrq6u3q5CRA3Q57CLSCuA1QB+rqonASwDMAnANHSf+Zf2dpyqLlfViqpW2tra6jBkIqpFn8IuIoPQHfQ/qOqfAUBVD6rqJVW9DOC3ADqKGyYRpXLDLt0vO74EYJeq/qrH5WN7XG0egB31Hx4R1UtfXo3/HoAfA9guIluzyx4HsEBEpqG7HbcXwE8LGWE/cPz4cbN+6tQps+61oI4cOZJb81pM3jTRIttfXuvNG/v48ePNurVE9yeffGIe6ylyCe2i9OXV+L8A6K2p+K3tqRNF1Hz//RBRIRh2oiAYdqIgGHaiIBh2oiAYdqIguJR0psgtm2fMmGHWp06datZHjhxp1lN64V6/uLW11aynbKucMnUXAAYNGmTWrfc3dHSkveGzGfvonm/fiImoJgw7URAMO1EQDDtREAw7URAMO1EQDDtREJKype4135lIF4DPelw0BsDhhg3g2jTr2Jp1XADHVqt6ju2fVLXX9d8aGvZv3LlIVVUrpQ3A0Kxja9ZxARxbrRo1Nj6NJwqCYScKouywLy/5/i3NOrZmHRfAsdWqIWMr9W92Imqcss/sRNQgDDtREKWEXUTuE5H/E5E9IvJYGWPIIyJ7RWS7iGwVkWrJY1khIodEZEePy0aLyFsi0pl97nWPvZLG9qSI7Mseu60iMruksU0QkQ0i8pGI7BSRn2WXl/rYGeNqyOPW8L/ZRWQAgI8B/CuALwC8D2CBqn7U0IHkEJG9ACqqWvobMETk+wBOAfi9qt6ZXfYMgKOq+lT2H+UoVf1Fk4ztSQCnyt7GO9utaGzPbcYBzAXwbyjxsTPGNR8NeNzKOLN3ANijqp+q6nkAfwQwp4RxND1V3QTg6FUXzwGwMvt6Jbp/WRouZ2xNQVUPqOqW7OuvAFzZZrzUx84YV0OUEfZbAPytx/dfoLn2e1cA60Rks4gsKnswvWhX1QPZ118CaC9zML1wt/FupKu2GW+ax66W7c9T8QW6b5qpqjMA3A/g4ezpalPS7r/Bmql32qdtvBull23G/67Mx67W7c9TlRH2fQAm9Ph+fHZZU1DVfdnnQwDWoPm2oj54ZQfd7POhksfzd820jXdv24yjCR67Mrc/LyPs7wOYLCLfEZEWAD8CsLaEcXyDiAzLXjiBiAwD8EM031bUawEszL5eCOD1EsfyD5plG++8bcZR8mNX+vbnqtrwDwCz0f2K/CcAnihjDDnjug3AtuxjZ9ljA/Aqup/WXUD3axs/AXAjgPUAOgH8L4DRTTS2/wKwHcCH6A7W2JLGNhPdT9E/BLA1+5hd9mNnjKshjxvfLksUBF+gIwqCYScKgmEnCoJhJwqCYScKgmEnCoJhJwri/wFw4AcBUhlL0QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#creating your first Neural Network\n",
        "from keras.layers import Dense, Flatten\n",
        "myModel=keras.Sequential([\n",
        "        keras.layers.Flatten(input_shape=(28,28)),\n",
        "        #keras.layers.Dense(128, activation='relu'),\n",
        "        keras.layers.Dense(224, activation='relu'),\n",
        "        keras.layers.Dense(10,activation='softmax')\n",
        "        ])\n",
        "myModel.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "myModel.fit(train_images, train_labels, epochs=10)\n",
        "test_loss, test_acc=myModel.evaluate(test_images, test_labels, verbose=2)\n",
        "print('\\nTest accuracy: ', test_acc)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wC0Df3ijhQra",
        "outputId": "c981b16c-82ac-44fd-9973-5607450d5db9"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.4839 - accuracy: 0.8285\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3654 - accuracy: 0.8678\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3273 - accuracy: 0.8798\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.3022 - accuracy: 0.8888\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2869 - accuracy: 0.8942\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2718 - accuracy: 0.8985\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2603 - accuracy: 0.9017\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2474 - accuracy: 0.9089\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2367 - accuracy: 0.9110\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2290 - accuracy: 0.9141\n",
            "313/313 - 1s - loss: 0.3275 - accuracy: 0.8896 - 555ms/epoch - 2ms/step\n",
            "\n",
            "Test accuracy:  0.8895999789237976\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=myModel.predict(test_images)\n",
        "np.argmax(predictions[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xjSVQ3pWi7JV",
        "outputId": "948feea2-881d-4308-9f48-64398532355c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    }
  ]
}