lab-06-2-softmax_zoo_classifier-eager.ipynb

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    "# Lab 06 Softmax Zoo_classifier-eager\n",
    "\n",
    "* Softmax를 사용하여 Zoo 데이터를 활용하여 분류를 진행합니다.\n",
    "\n",
    "### 기본 Library 선언 및 Tensorflow 버전 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "tf.enable_eager_execution()\n",
    "tf.set_random_seed(777)  # for reproducibility\n",
    "tfe = tf.contrib.eager"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(101, 16) (101, 7)\n"
     ]
    }
   ],
   "source": [
    "xy = np.loadtxt('data-04-zoo.csv', delimiter=',', dtype=np.int32) #tf1.13.1에서는 np.int32, 이전에는 np.float32\n",
    "x_data = xy[:, 0:-1]\n",
    "y_data = xy[:, [-1]]\n",
    "\n",
    "nb_classes = 7  # 0 ~ 6\n",
    "\n",
    "# Make Y data as onehot shape\n",
    "Y_one_hot = tf.one_hot(list(y_data), nb_classes)\n",
    "Y_one_hot = tf.reshape(Y_one_hot, [-1, nb_classes])\n",
    "\n",
    "print(x_data.shape, Y_one_hot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Weight and bias setting\n",
    "W = tfe.Variable(tf.random_normal([16, nb_classes]), name='weight')\n",
    "b = tfe.Variable(tf.random_normal([nb_classes]), name='bias')\n",
    "variables = [W, b]\n",
    "\n",
    "# tf.nn.softmax computes softmax activations\n",
    "# softmax = exp(logits) / reduce_sum(exp(logits), dim)\n",
    "def logit_fn(X):\n",
    "    return tf.matmul(X, W) + b\n",
    "\n",
    "def hypothesis(X):\n",
    "    return tf.nn.softmax(logit_fn(X))\n",
    "\n",
    "def cost_fn(X, Y):\n",
    "    logits = logit_fn(X)\n",
    "    cost_i = tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,\n",
    "                                                           labels=Y)\n",
    "    cost = tf.reduce_mean(cost_i)\n",
    "    \n",
    "    return cost\n",
    "\n",
    "def grad_fn(X, Y):\n",
    "    with tf.GradientTape() as tape:\n",
    "        loss = cost_fn(X, Y)\n",
    "        grads = tape.gradient(loss, variables)\n",
    "        return grads\n",
    "    \n",
    "def prediction(X, Y):\n",
    "    pred = tf.argmax(hypothesis(X), 1)\n",
    "    correct_prediction = tf.equal(pred, tf.argmax(Y, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "\n",
    "    return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Steps: 1 Loss: 3.635028839111328, Acc: 0.1683168262243271\n",
      "Steps: 100 Loss: 0.5194157958030701, Acc: 0.7920792102813721\n",
      "Steps: 200 Loss: 0.31850090622901917, Acc: 0.9108911156654358\n",
      "Steps: 300 Loss: 0.23534874618053436, Acc: 0.9405940771102905\n",
      "Steps: 400 Loss: 0.18872138857841492, Acc: 0.9504950642585754\n",
      "Steps: 500 Loss: 0.158460333943367, Acc: 0.9504950642585754\n",
      "Steps: 600 Loss: 0.13703754544258118, Acc: 0.9900990128517151\n",
      "Steps: 700 Loss: 0.1209898293018341, Acc: 0.9900990128517151\n",
      "Steps: 800 Loss: 0.10847964137792587, Acc: 1.0\n",
      "Steps: 900 Loss: 0.09843041747808456, Acc: 1.0\n",
      "Steps: 1000 Loss: 0.09016557037830353, Acc: 1.0\n"
     ]
    }
   ],
   "source": [
    "def fit(X, Y, epochs=1000, verbose=100):\n",
    "    optimizer =  tf.train.GradientDescentOptimizer(learning_rate=0.1)\n",
    "\n",
    "    for i in range(epochs):\n",
    "        grads = grad_fn(X, Y)\n",
    "        optimizer.apply_gradients(zip(grads, variables))\n",
    "        if (i==0) | ((i+1)%verbose==0):\n",
    "#             print('Loss at epoch %d: %f' %(i+1, cost_fn(X, Y).numpy()))\n",
    "            acc = prediction(X, Y).numpy()\n",
    "            loss = cost_fn(X, Y).numpy() \n",
    "            print('Steps: {} Loss: {}, Acc: {}'.format(i+1, loss, acc))\n",
    "\n",
    "fit(x_data, Y_one_hot)"
   ]
  }
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