Tensorflow Test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.0\n"
     ]
    }
   ],
   "source": [
    "\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",
    "#check tensorflow version\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#load data\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "\n",
    "#format data...\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n",
    "\n",
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#quick img check\n",
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#resize and make to float\n",
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot check items\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 10s 169us/sample - loss: 0.4947 - accuracy: 0.8272\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 5s 88us/sample - loss: 0.3739 - accuracy: 0.8655\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 5s 88us/sample - loss: 0.3344 - accuracy: 0.8786\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 5s 88us/sample - loss: 0.3125 - accuracy: 0.8850\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 5s 83us/sample - loss: 0.2927 - accuracy: 0.8929\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 5s 84us/sample - loss: 0.2794 - accuracy: 0.8970\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 5s 85us/sample - loss: 0.2662 - accuracy: 0.9030\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.2556 - accuracy: 0.9050\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 5s 84us/sample - loss: 0.2459 - accuracy: 0.9086\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 6s 95us/sample - loss: 0.2366 - accuracy: 0.9119\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x176e59e1b08>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#build neural network model\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10)\n",
    "])\n",
    "\n",
    "#compile model\n",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "#fit model\n",
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/1 - 1s - loss: 0.2194 - accuracy: 0.8739\n",
      "\n",
      "Test accuracy: 0.8739\n"
     ]
    }
   ],
   "source": [
    "#test model\n",
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#make predictions\n",
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])\n",
    "\n",
    "predictions = probability_model.predict(test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at a prediction\n",
    "predictions[0]\n",
    "\n",
    "#check prediction\n",
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#define plot function\n",
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array, true_label[i], img[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\n",
    "  plt.yticks([])\n",
    "\n",
    "  plt.imshow(img, cmap=plt.cm.binary)\n",
    "\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "  if predicted_label == true_label:\n",
    "    color = 'blue'\n",
    "  else:\n",
    "    color = 'red'\n",
    "\n",
    "  plt.xlabel(\"{} {:2.0f}% ({})\".format(class_names[predicted_label],\n",
    "                                100*np.max(predictions_array),\n",
    "                                class_names[true_label]),\n",
    "                                color=color)\n",
    "\n",
    "def plot_value_array(i, predictions_array, true_label):\n",
    "  predictions_array, true_label = predictions_array, true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks(range(10))\n",
    "  plt.yticks([])\n",
    "  thisplot = plt.bar(range(10), predictions_array, color=\"#777777\")\n",
    "  plt.ylim([0, 1])\n",
    "  predicted_label = np.argmax(predictions_array)\n",
    "\n",
    "  thisplot[predicted_label].set_color('red')\n",
    "  thisplot[true_label].set_color('blue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#verify predictions\n",
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#verify predictions\n",
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions[i], test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions[i],  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot some of the predictions in grid\n",
    "# Plot the first X test images, their predicted labels, and the true labels.\n",
    "# Color correct predictions in blue and incorrect predictions in red.\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "num_images = num_rows*num_cols\n",
    "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
    "for i in range(num_images):\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
    "  plot_image(i, predictions[i], test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions[i], test_labels)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Grab an image from the test dataset.\n",
    "img = test_images[1]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.68315955e-06 1.01573914e-13 9.91644800e-01 4.46784184e-13\n",
      "  2.07129167e-03 5.66832432e-12 6.28016144e-03 6.19681632e-19\n",
      "  2.68362416e-10 3.29507146e-13]]\n"
     ]
    }
   ],
   "source": [
    "#make predictions on single image\n",
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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": [
    "#plot\n",
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#what is its guess\n",
    "np.argmax(predictions_single[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}