02_logistic_regression.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "x_train = x_train / 255\n",
    "x_test = x_test / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.Flatten(),\n",
    "    tf.keras.layers.Dense(28*28),\n",
    "    tf.keras.layers.Dense(10)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "    optimizer=tf.keras.optimizers.SGD(lr=0.01),\n",
    "    loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 60000 samples\n",
      "Epoch 1/5\n",
      "60000/60000 [==============================] - 5s 76us/sample - loss: 2.3655 - accuracy: 0.1281\n",
      "Epoch 2/5\n",
      "60000/60000 [==============================] - 4s 71us/sample - loss: 2.3026 - accuracy: 0.1460\n",
      "Epoch 3/5\n",
      "60000/60000 [==============================] - 4s 71us/sample - loss: 2.3026 - accuracy: 0.1460\n",
      "Epoch 4/5\n",
      "60000/60000 [==============================] - 5s 81us/sample - loss: 2.3024 - accuracy: 0.1461\n",
      "Epoch 5/5\n",
      "60000/60000 [==============================] - 5s 82us/sample - loss: 2.2953 - accuracy: 0.1479\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(x_train, y_train, epochs=5)"
   ]
  }
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