Fashion_MNIST_TensorFlow_1.x.ipynb

{
 "cells": [
{
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#  Simple notebook to run fashion MNIST on TensorFlow 1.X.\n",
    "\n",
    "## How to start Session\n",
    "\n","\n",
    "First, click the `Sessions` menu  in the navigation bar on the left. Click the `Start` button in the upper right corner to create a new session.\n",
    "\n",
    "<div>\n<img src=\"https://user-images.githubusercontent.com/42667951/93175878-54884700-f76b-11ea-928f-d86a92cc5509.png\" width=700></div>\n",
    "\n",
    "Then you will see the following screen: Select **TensorFlow** for `Environments` and **1.15/ Python 3.6 / CUDA10** for `Version`. After that click `LAUNCH`\n",
    "\n",
    "<div>\n<img src=\"https://user-images.githubusercontent.com/42667951/93179383-91a30800-f770-11ea-88cd-9b2148ae2648.png\" width=300></div>\n",
    "\n",
    "If you launch the session, you can see this screen. Click `Jupyter Notebook`.\n",
    "<div>\n<img src=\"https://user-images.githubusercontent.com/42667951/93179627-e6468300-f770-11ea-8d55-455bc8c106e0.png\" width=700></div>\n",
    "\n",
    "\n",
    "Now, you can use Jupyter Notebook. You can create a new notebook in the `Tensorflow 1.15.3 on Python 3.6 (CUDA 10.0)` environment."
   ]
  },
  {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## fasion MNIST code",
      "\n",
      "This is a simple fasion MNIST code example."
     ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.13.1\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(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
    "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 0, 0, ..., 3, 0, 5], dtype=uint8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 28, 28)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images = train_images / 255.0\n",
    "\n",
    "test_images = test_images / 255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(128, activation='relu'),\n",
    "    keras.layers.Dense(10, activation='softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss='sparse_categorical_crossentropy',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 6s 94us/sample - loss: 0.5038 - acc: 0.8227\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.3768 - acc: 0.8641\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 5s 91us/sample - loss: 0.3352 - acc: 0.8788\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.3136 - acc: 0.8850\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 6s 92us/sample - loss: 0.2969 - acc: 0.8903\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fb11c2b7eb8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_images, train_labels, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - 0s - loss: 0.3523 - acc: 0.8759\n",
      "\n",
      "테스트 정확도: 0.8759\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n",
    "\n",
    "print('\\n테스트 정확도:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.2684131e-06, 1.4897198e-07, 1.3565152e-07, 6.4311814e-07,\n",
       "       1.1241173e-06, 2.0456117e-02, 1.7469181e-06, 2.0909908e-03,\n",
       "       2.7895006e-05, 9.7741801e-01], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model.predict(test_images)\n",
    "predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_labels[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_image(i, predictions_array, true_label, img):\n",
    "  predictions_array, true_label, img = predictions_array[i], 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[i], true_label[i]\n",
    "  plt.grid(False)\n",
    "  plt.xticks([])\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": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 0\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "i = 12\n",
    "plt.figure(figsize=(6,3))\n",
    "plt.subplot(1,2,1)\n",
    "plot_image(i, predictions, test_labels, test_images)\n",
    "plt.subplot(1,2,2)\n",
    "plot_value_array(i, predictions,  test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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, test_labels, test_images)\n",
    "  plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
    "  plot_value_array(i, predictions, test_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 28)\n"
     ]
    }
   ],
   "source": [
    "img = test_images[0]\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "img = (np.expand_dims(img,0))\n",
    "\n",
    "print(img.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.2684163e-06 1.4897198e-07 1.3565140e-07 6.4311934e-07 1.1241204e-06\n",
      "  2.0456117e-02 1.7469198e-06 2.0909898e-03 2.7895006e-05 9.7741801e-01]]\n"
     ]
    }
   ],
   "source": [
    "predictions_single = model.predict(img)\n",
    "\n",
    "print(predictions_single)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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_value_array(0, predictions_single, test_labels)\n",
    "_ = plt.xticks(range(10), class_names, rotation=45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(predictions_single[0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "TensorFlow 1.13.1 on Python 3.6 (CUDA 10)",
   "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}