notebooks/09-deep-learning-tf-hw-digit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist = tf.keras.datasets.mnist \n",
    "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ITtABQRB2IAjCDgRB2IEgCDsQBGEHgiDsQBD/C09Ib10qaFHQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(x_train[5], cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize the train dataset\n",
    "x_train = tf.keras.utils.normalize(x_train, axis=1)\n",
    "# Normalize the test dataset\n",
    "x_test = tf.keras.utils.normalize(x_test, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build the model object\n",
    "model = tf.keras.models.Sequential()\n",
    "# Add the Flatten Layer\n",
    "model.add(tf.keras.layers.Flatten())\n",
    "# Build the input and the hidden layers\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu))\n",
    "# Build the output layer\n",
    "model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1875/1875 [==============================] - 4s 1ms/step - loss: 0.4641 - accuracy: 0.8674\n",
      "Epoch 2/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.1098 - accuracy: 0.9663\n",
      "Epoch 3/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0686 - accuracy: 0.9790\n",
      "Epoch 4/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0515 - accuracy: 0.9832\n",
      "Epoch 5/5\n",
      "1875/1875 [==============================] - 2s 1ms/step - loss: 0.0361 - accuracy: 0.9881\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7f1fa3572710>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# train model\n",
    "model.fit(x=x_train, y=y_train, epochs=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 838us/step - loss: 0.1045 - accuracy: 0.9703\n",
      "\n",
      "Test accuracy: 0.970300018787384\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model performance\n",
    "test_loss, test_acc = model.evaluate(x=x_test, y=y_test)\n",
    "# Print out the model accuracy \n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(x_test) # Make prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "print(np.argmax(predictions[1000]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": [
    "plt.imshow(x_test[1000], cmap=\"gray\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# keras expects muliple inputs\n",
    "np.argmax(model.predict(x_test[0:1, :, :]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if we have only one input\n",
    "np.argmax(model.predict(np.expand_dims(x_test[0], axis=0)))"
   ]
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}