MLTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tutorial from https://www.tensorflow.org/tutorials/keras/classification\n",
    "#\n",
    "# Copyright (c) 2017 François Chollet\n",
    "#\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a\n",
    "# copy of this software and associated documentation files (the \"Software\"),\n",
    "# to deal in the Software without restriction, including without limitation\n",
    "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
    "# and/or sell copies of the Software, and to permit persons to whom the\n",
    "# Software is furnished to do so, subject to the following conditions:\n",
    "#\n",
    "# The above copyright notice and this permission notice shall be included in\n",
    "# all copies or substantial portions of the Software.\n",
    "#\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
    "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
    "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
    "# DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.1.0\n",
      "Keras version: 2.2.4-tf\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print(\"TensorFlow version: \" + tf.__version__)\n",
    "print(\"Keras version: \" + keras.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": [
    "plt.figure()\n",
    "plt.imshow(train_images[0])\n",
    "plt.colorbar()\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/10\n",
      "60000/60000 [==============================] - 4s 73us/sample - loss: 0.4937 - accuracy: 0.8262\n",
      "Epoch 2/10\n",
      "60000/60000 [==============================] - 4s 69us/sample - loss: 0.3723 - accuracy: 0.8666\n",
      "Epoch 3/10\n",
      "60000/60000 [==============================] - 4s 64us/sample - loss: 0.3323 - accuracy: 0.8792\n",
      "Epoch 4/10\n",
      "60000/60000 [==============================] - 4s 64us/sample - loss: 0.3105 - accuracy: 0.8856\n",
      "Epoch 5/10\n",
      "60000/60000 [==============================] - 4s 72us/sample - loss: 0.2935 - accuracy: 0.8916\n",
      "Epoch 6/10\n",
      "60000/60000 [==============================] - 4s 66us/sample - loss: 0.2785 - accuracy: 0.8963\n",
      "Epoch 7/10\n",
      "60000/60000 [==============================] - 4s 74us/sample - loss: 0.2665 - accuracy: 0.9016\n",
      "Epoch 8/10\n",
      "60000/60000 [==============================] - 4s 62us/sample - loss: 0.2552 - accuracy: 0.9051\n",
      "Epoch 9/10\n",
      "60000/60000 [==============================] - 4s 73us/sample - loss: 0.2462 - accuracy: 0.9077\n",
      "Epoch 10/10\n",
      "60000/60000 [==============================] - 5s 84us/sample - loss: 0.2384 - accuracy: 0.9121\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x13888dab2c8>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 - 0s - loss: 0.3355 - accuracy: 0.8856\n",
      "\n",
      "Test accuracy: 0.8856\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.1100980e-06 5.1349841e-11 3.3314165e-09 8.0042147e-11 3.6283161e-09\n",
      " 1.9983519e-03 3.8929179e-06 7.7223890e-03 7.0165567e-09 9.9027121e-01]\n",
      "9\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "probability_model = tf.keras.Sequential([model, \n",
    "                                         tf.keras.layers.Softmax()])\n",
    "\n",
    "predictions = probability_model.predict(test_images)\n",
    "\n",
    "print(predictions[0])\n",
    "print(np.argmax(predictions[0]))\n",
    "print(test_labels[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 44,
   "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": [
    "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": 45,
   "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": [
    "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": 46,
   "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 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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Add the image to a batch where it's the only member.\n",
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.9057201e-05 2.0292551e-13 9.9946171e-01 6.6823193e-11 4.3183391e-04\n",
      "  1.6587361e-10 8.7457207e-05 4.6298738e-18 5.0582948e-13 1.4529535e-17]]\n",
      "2\n"
     ]
    },
    {
     "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": [
    "predictions_single = probability_model.predict(img)\n",
    "\n",
    "print(predictions_single)\n",
    "\n",
    "plot_value_array(1, predictions_single[0], test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)\n",
    "\n",
    "print(np.argmax(predictions_single[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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": [
    "i = np.random.randint(len(test_labels))\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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:.conda-mlTest] *",
   "language": "python",
   "name": "conda-env-.conda-mlTest-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}