NeuralNetworkModelToClassifyImagesOfClothing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in /usr/local/lib/python3.6/site-packages\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/site-packages\n",
      "Collecting keras\n",
      "  Downloading https://files.pythonhosted.org/packages/34/7d/b1dedde8af99bd82f20ed7e9697aac0597de3049b1f786aa2aac3b9bd4da/Keras-2.2.2-py2.py3-none-any.whl (299kB)\n",
      "\u001b[K    100% |████████████████████████████████| 307kB 1.7MB/s ta 0:00:01\n",
      "\u001b[?25hCollecting tensorflow\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "  Downloading https://files.pythonhosted.org/packages/57/38/96ee343729363297d62311272adc28b466d30f74c87dc645badb0102af56/tensorflow-1.9.0-cp36-cp36m-macosx_10_11_x86_64.whl (48.9MB)\n",
      "\u001b[K    100% |████████████████████████████████| 48.9MB 34kB/s  eta 0:00:01 2% |▊                               | 1.1MB 4.1MB/s eta 0:00:12\n",
      "\u001b[?25hRequirement already satisfied: pytz in /usr/local/lib/python3.6/site-packages (from matplotlib)\n",
      "Requirement already satisfied: python-dateutil>=2.0 in /Users/jay/Library/Python/3.6/lib/python/site-packages (from matplotlib)\n",
      "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.6/site-packages (from matplotlib)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/site-packages (from matplotlib)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/site-packages (from matplotlib)\n",
      "Collecting keras-preprocessing==1.0.2 (from keras)\n",
      "  Downloading https://files.pythonhosted.org/packages/71/26/1e778ebd737032749824d5cba7dbd3b0cf9234b87ab5ec79f5f0403ca7e9/Keras_Preprocessing-1.0.2-py2.py3-none-any.whl\n",
      "Collecting keras-applications==1.0.4 (from keras)\n",
      "  Downloading https://files.pythonhosted.org/packages/54/90/8f327deaa37a71caddb59b7b4aaa9d4b3e90c0e76f8c2d1572005278ddc5/Keras_Applications-1.0.4-py2.py3-none-any.whl (43kB)\n",
      "\u001b[K    100% |████████████████████████████████| 51kB 6.5MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: pyyaml in /Users/jay/Library/Python/3.6/lib/python/site-packages (from keras)\n",
      "Collecting h5py (from keras)\n",
      "  Downloading https://files.pythonhosted.org/packages/44/81/50a0560aac57a33c2a624d9e160735f39d7a6324e3f6f115425a1bf01dd9/h5py-2.8.0-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl (6.0MB)\n",
      "\u001b[K    100% |████████████████████████████████| 6.1MB 281kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy>=0.14 in /usr/local/lib/python3.6/site-packages (from keras)\n",
      "Requirement already satisfied: setuptools<=39.1.0 in /usr/local/lib/python3.6/site-packages (from tensorflow)\n",
      "Collecting tensorboard<1.10.0,>=1.9.0 (from tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "  Downloading https://files.pythonhosted.org/packages/9e/1f/3da43860db614e294a034e42d4be5c8f7f0d2c75dc1c428c541116d8cdab/tensorboard-1.9.0-py3-none-any.whl (3.3MB)\n",
      "\u001b[K    100% |████████████████████████████████| 3.3MB 508kB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/site-packages (from tensorflow)\n",
      "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/site-packages (from tensorflow)\n",
      "Requirement already satisfied: protobuf>=3.4.0 in /usr/local/lib/python3.6/site-packages (from tensorflow)\n",
      "Collecting astor>=0.6.0 (from tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "  Downloading https://files.pythonhosted.org/packages/35/6b/11530768cac581a12952a2aad00e1526b89d242d0b9f59534ef6e6a1752f/astor-0.7.1-py2.py3-none-any.whl\n",
      "Collecting absl-py>=0.1.6 (from tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "  Downloading https://files.pythonhosted.org/packages/96/5d/18feb90462c8edaae71305716c7e8bac479fc9dface63221f808a6b95880/absl-py-0.3.0.tar.gz (84kB)\n",
      "\u001b[K    100% |████████████████████████████████| 92kB 7.7MB/s eta 0:00:01\n",
      "\u001b[?25hCollecting gast>=0.2.0 (from tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "Collecting termcolor>=1.1.0 (from tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "Collecting markdown>=2.6.8 (from tensorboard<1.10.0,>=1.9.0->tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "  Downloading https://files.pythonhosted.org/packages/6d/7d/488b90f470b96531a3f5788cf12a93332f543dbab13c423a5e7ce96a0493/Markdown-2.6.11-py2.py3-none-any.whl (78kB)\n",
      "\u001b[K    100% |████████████████████████████████| 81kB 1.9MB/s ta 0:00:011\n",
      "\u001b[?25hCollecting werkzeug>=0.11.10 (from tensorboard<1.10.0,>=1.9.0->tensorflow)\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "\u001b[33m  Cache entry deserialization failed, entry ignored\u001b[0m\n",
      "  Downloading https://files.pythonhosted.org/packages/20/c4/12e3e56473e52375aa29c4764e70d1b8f3efa6682bef8d0aae04fe335243/Werkzeug-0.14.1-py2.py3-none-any.whl (322kB)\n",
      "\u001b[K    100% |████████████████████████████████| 327kB 3.1MB/s eta 0:00:01\n",
      "\u001b[?25hBuilding wheels for collected packages: absl-py\n",
      "  Running setup.py bdist_wheel for absl-py ... \u001b[?25ldone\n",
      "\u001b[?25h  Stored in directory: /Users/jay/Library/Caches/pip/wheels/4c/16/ef/e36a23f2432e9220f8845f94e2c3abd39e7d9d1cd458d3159d\n",
      "Successfully built absl-py\n",
      "Installing collected packages: keras-preprocessing, h5py, keras-applications, keras, markdown, werkzeug, tensorboard, astor, absl-py, gast, termcolor, tensorflow\n",
      "Successfully installed absl-py-0.3.0 astor-0.7.1 gast-0.2.0 h5py-2.8.0 keras-2.2.2 keras-applications-1.0.4 keras-preprocessing-1.0.2 markdown-2.6.11 tensorboard-1.9.0 tensorflow-1.9.0 termcolor-1.1.0 werkzeug-0.14.1\n",
      "\u001b[33mYou are using pip version 9.0.1, however version 18.0 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
      "1.9.0\n"
     ]
    }
   ],
   "source": [
    "# Install a pip package in the current Jupyter kernel\n",
    "import sys\n",
    "!{sys.executable} -m pip install numpy matplotlib keras tensorflow\n",
    "\n",
    "# TensorFlow and tf.keras\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# Helper libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(tf.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/numpy']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# find out which location has been used using the __path__ attribute of an imported module:\n",
    "np.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
      "32768/29515 [=================================] - 0s 1us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
      "26427392/26421880 [==============================] - 4s 0us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
      "8192/5148 [===============================================] - 0s 3us/step\n",
      "Downloading data from http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
      "4423680/4422102 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# The train_images and train_labels arrays are the training set—the data the model uses to learn.\n",
    "# The model is tested against the test set, the test_images, and test_labels arrays.\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', \n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num Dimensions: 3\n",
      "train_images size:  47040000\n",
      "train_images shape:  (60000, 28, 28)\n",
      "train_images d Type: uint8\n"
     ]
    }
   ],
   "source": [
    "print(\"Num Dimensions:\", train_images.ndim)\n",
    "print(\"train_images size: \", train_images.size)\n",
    "print(\"train_images shape: \", train_images.shape)\n",
    "print(\"train_images d Type:\", train_images.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3 4 8 2]\n",
      " [4 9 3 2]\n",
      " [4 1 9 7]]\n",
      "(3, 4) 2\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Each label is an integer between 0 and 9:\n",
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109bb5da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocess the data\n",
    "# The data must be preprocessed before training the network. \n",
    "# If you inspect the first image in the training set, you will see that the pixel values fall in the range of 0 to 255:\n",
    "\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.gca().grid(False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale these values to a range of 0 to 1 before feeding to the neural network model. For this, cast the datatype of the image components from an integer to a float, and divide by 255\n",
    "\n",
    "train_images = train_images / 255.0\n",
    "test_images = test_images / 255.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1083d6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the first 25 images from the training set and display the class name below each image. \n",
    "# Verify that the data is in the correct format and we're ready to build and train the network.\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('off')\n",
    "    plt.imshow(train_images[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(class_names[train_labels[i]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build teh model\n",
    "# Setup the layers\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation=tf.nn.relu),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 9s 158us/step - loss: 0.4981 - acc: 0.8233 1s - loss: 0.509\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 4s 67us/step - loss: 0.3734 - acc: 0.8670\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 4s 67us/step - loss: 0.3375 - acc: 0.8776: 0s - loss\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 3s 57us/step - loss: 0.3135 - acc: 0.8848: 1s - loss: 0.3 - ETA: 0s - loss: 0.3133 - acc: 0.88 - ETA: 0s - loss: 0.3140 - acc - ETA: 0s - loss: 0.3137 - acc: 0.88\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 4s 61us/step - loss: 0.2958 - acc: 0.8912: 3s - - ETA: 0s - loss: 0.2959 - acc: 0.891\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x109ab4240>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compiel the model. i.e. Configre to use Optimizer, Loss function and Metric\n",
    "\n",
    "model.compile(optimizer=tf.train.AdamOptimizer(), \n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Train the model\n",
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 2s 198us/step\n",
      "Test accuracy: 0.8741\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the accuracy\n",
    "test_loss, test_acc = model.evaluate(test_images, test_labels)\n",
    "\n",
    "print('Test accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  7.94606694e-06   8.35263148e-09   3.81819888e-07   3.74573960e-06\n",
      "   2.05903223e-08   5.85953007e-03   3.13778037e-05   6.30616546e-02\n",
      "   6.32147521e-06   9.31029022e-01]\n"
     ]
    }
   ],
   "source": [
    "# Make Predictions\n",
    "\n",
    "predictions = model.predict(test_images)\n",
    "\n",
    "# Displays the confidence score for each output category. In this case, we have 10 class output categories\n",
    "print(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb73f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the first 25 test images, their predicted label, and the true label\n",
    "# Color correct predictions in green, incorrect predictions in red\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('off')\n",
    "    plt.imshow(test_images[i], cmap=plt.cm.binary)\n",
    "    predicted_label = np.argmax(predictions[i])\n",
    "    true_label = test_labels[i]\n",
    "    if predicted_label == true_label:\n",
    "      color = 'green'\n",
    "    else:\n",
    "      color = 'red'\n",
    "    plt.xlabel(\"{} ({})\".format(class_names[predicted_label], \n",
    "                                  class_names[true_label]),\n",
    "                                  color=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28) 2\n",
      "(1, 28, 28)\n",
      "[[  7.94605967e-06   8.35263148e-09   3.81819518e-07   3.74573619e-06\n",
      "    2.05903223e-08   5.85953007e-03   3.13778037e-05   6.30616099e-02\n",
      "    6.32146339e-06   9.31029022e-01]]\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "# tf.keras models are optimized to make predictions on a batch, or collection\n",
    "# Grab an image from the test dataset\n",
    "img = test_images[0]\n",
    "\n",
    "print(img.shape, img.ndim)\n",
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "print(img.shape)\n",
    "\n",
    "\n",
    "predictions = model.predict(img)\n",
    "print(predictions)\n",
    "\n",
    "\n",
    "prediction = predictions[0]\n",
    "print(np.argmax(prediction))"
   ]
  },
  {
   "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}