lab-06-2-softmax_zoo_classifier-tf2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 16) (101, 1)\n",
      "one_hot: (101, 7)\n",
      "reshape one_hot: (101, 7)\n",
      "Step:  0 cost:  4.33959913 accuracy:  0.158415839\n",
      "Step:  1000 cost:  0.520363927 accuracy:  0.79207921\n",
      "Step:  2000 cost:  0.318909109 accuracy:  0.910891116\n",
      "Step:  3000 cost:  0.235611364 accuracy:  0.940594077\n",
      "Step:  4000 cost:  0.188909978 accuracy:  0.950495064\n",
      "Step:  5000 cost:  0.158603594 accuracy:  0.950495064\n",
      "Step:  6000 cost:  0.13715075 accuracy:  0.990099\n",
      "Step:  7000 cost:  0.121081986 accuracy:  0.990099\n",
      "Step:  8000 cost:  0.108556427 accuracy:  1\n",
      "Step:  9000 cost:  0.0984955281 accuracy:  1\n",
      "Step:  10000 cost:  0.0902217701 accuracy:  1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 4 True Y: 4\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 2 True Y: 2\n",
      "[True] Prediction: 3 True Y: 3\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 1 True Y: 1\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 5 True Y: 5\n",
      "[True] Prediction: 0 True Y: 0\n",
      "[True] Prediction: 6 True Y: 6\n",
      "[True] Prediction: 1 True Y: 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\nStep:     0 Loss: 5.106 Acc: 37.62%\\nStep:   100 Loss: 0.800 Acc: 79.21%\\nStep:   200 Loss: 0.486 Acc: 88.12%\\n...\\nStep:  1800\\tLoss: 0.060\\tAcc: 100.00%\\nStep:  1900\\tLoss: 0.057\\tAcc: 100.00%\\nStep:  2000\\tLoss: 0.054\\tAcc: 100.00%\\n[True] Prediction: 0 True Y: 0\\n[True] Prediction: 0 True Y: 0\\n[True] Prediction: 3 True Y: 3\\n...\\n[True] Prediction: 0 True Y: 0\\n[True] Prediction: 6 True Y: 6\\n[True] Prediction: 1 True Y: 1\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lab 6 Softmax Classifier\n",
    "from functools import partial as bind\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "assert(tf.__version__.find('2') == 0)\n",
    "tf.random.set_seed(777)  # for reproducibility\n",
    "\n",
    "# Predicting animal type based on various features\n",
    "xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.float32)\n",
    "x_data = xy[:, 0:-1]\n",
    "y_data = xy[:, [-1]]\n",
    "\n",
    "print(x_data.shape, y_data.shape)\n",
    "\n",
    "'''\n",
    "(101, 16) (101, 1)\n",
    "'''\n",
    "\n",
    "nb_classes = 7  # 0 ~ 6\n",
    "\n",
    "Y_one_hot = tf.keras.utils.to_categorical(y_data, nb_classes)\n",
    "print(\"one_hot:\", Y_one_hot.shape)\n",
    "Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes])\n",
    "print(\"reshape one_hot:\", Y_one_hot.shape)\n",
    "\n",
    "'''\n",
    "one_hot: Tensor(\"one_hot:0\", shape=(?, 1, 7), dtype=float32)\n",
    "reshape one_hot: Tensor(\"Reshape:0\", shape=(?, 7), dtype=float32)\n",
    "'''\n",
    "\n",
    "W = tf.Variable(tf.random.normal([16, nb_classes]), name='weight')\n",
    "b = tf.Variable(tf.random.normal([nb_classes]), name='bias')\n",
    "\n",
    "# tf.nn.softmax computes softmax activations\n",
    "# softmax = exp(logits) / reduce_sum(exp(logits), dim)\n",
    "logits = lambda X: tf.matmul(X, W) + b\n",
    "hypothesis = lambda X: tf.nn.softmax(logits(X))\n",
    "\n",
    "prediction = lambda X: tf.argmax(hypothesis(X), 1)\n",
    "correct_prediction = lambda X: tf.equal(prediction(X), tf.argmax(Y_one_hot, 1))\n",
    "accuracy = lambda X: tf.reduce_mean(tf.cast(correct_prediction(X), tf.float32))\n",
    "\n",
    "# Cross entropy cost/loss\n",
    "@tf.function\n",
    "def cost(X, Y):\n",
    "    return tf.reduce_mean(\n",
    "        tf.nn.softmax_cross_entropy_with_logits(logits=logits(X),\n",
    "                                                labels=tf.stop_gradient([Y_one_hot]))\n",
    "    )\n",
    "\n",
    "optimizer = tf.keras.optimizers.SGD(learning_rate=0.01)\n",
    "\n",
    "# Launch graph\n",
    "for step in range(10001):\n",
    "    # Minimize\n",
    "    optimizer.minimize(bind(cost, x_data, y_data), var_list=[W, b])\n",
    "\n",
    "    if step % 1000 == 0:\n",
    "        # evaluate training accuracy\n",
    "        tf.print(\"Step: \", step, \"cost: \", bind(cost, x_data, y_data)(), \"accuracy: \", accuracy(x_data))\n",
    "        \n",
    "# Let's see if we can predict\n",
    "pred = prediction(x_data)\n",
    "# y_data: (N,1) = flatten => (N, ) matches pred.shape\n",
    "for p, y in zip(pred, y_data.flatten()):\n",
    "    print(\"[{}] Prediction: {} True Y: {}\".format(p == int(y), p, int(y)))\n",
    "\n",
    "'''\n",
    "Step:     0 Loss: 5.106 Acc: 37.62%\n",
    "Step:   100 Loss: 0.800 Acc: 79.21%\n",
    "Step:   200 Loss: 0.486 Acc: 88.12%\n",
    "...\n",
    "Step:  1800\tLoss: 0.060\tAcc: 100.00%\n",
    "Step:  1900\tLoss: 0.057\tAcc: 100.00%\n",
    "Step:  2000\tLoss: 0.054\tAcc: 100.00%\n",
    "[True] Prediction: 0 True Y: 0\n",
    "[True] Prediction: 0 True Y: 0\n",
    "[True] Prediction: 3 True Y: 3\n",
    "...\n",
    "[True] Prediction: 0 True Y: 0\n",
    "[True] Prediction: 6 True Y: 6\n",
    "[True] Prediction: 1 True Y: 1\n",
    "'''\n"
   ]
  },
  {
   "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
}