3(Testing). Restoring Naval Mine Identifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(166, 60)\n",
      "(166, 2)\n",
      "(42, 60)\n",
      "(42, 2)\n",
      "(208, 2)\n"
     ]
    }
   ],
   "source": [
    "def read_dataset():\n",
    "    df = pd.read_csv(\"Naval Mine/dataset_40_sonar.csv\")\n",
    "    X = df[df.columns[0:60]].values\n",
    "    Y = df[df.columns[60]]\n",
    "    encode = LabelEncoder()\n",
    "    encode.fit(Y)\n",
    "    Y = encode.transform(Y)\n",
    "    Y = one_hot_encode(Y)\n",
    "    return (X,Y)\n",
    "\n",
    "def one_hot_encode(labels):\n",
    "    n_labels=len(labels)\n",
    "    n_unique_labels=len(np.unique(labels))\n",
    "    one_hot_encode=np.zeros((n_labels,n_unique_labels))\n",
    "    one_hot_encode[np.arange(n_labels), labels]=1\n",
    "    return one_hot_encode\n",
    "\n",
    "X, Y=read_dataset()\n",
    "X, Y=shuffle(X, Y,random_state=1)\n",
    "train_x,test_x,train_y,test_y = train_test_split(X, Y,test_size=0.20,random_state=415)\n",
    "\n",
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)\n",
    "print(Y.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "n_dim=  60\n",
      "WARNING:tensorflow:From <ipython-input-5-de84702ad991>:62: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "\n",
      "Future major versions of TensorFlow will allow gradients to flow\n",
      "into the labels input on backprop by default.\n",
      "\n",
      "See @{tf.nn.softmax_cross_entropy_with_logits_v2}.\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from Naval Mine/NMI\n"
     ]
    }
   ],
   "source": [
    "# Important parameter and variables to work with tensors\n",
    "learning_rate=0.3\n",
    "training_epochs=1000\n",
    "cost_history = np.empty(shape=[1], dtype=float)\n",
    "n_dim = X.shape[1]\n",
    "print(\"n_dim= \",n_dim)\n",
    "n_class = 2\n",
    "model_path = \"Naval Mine/NMI\"\n",
    "\n",
    "# Define the number of layers and number of neurons for each layer\n",
    "\n",
    "n_hidden_1 = 60\n",
    "n_hidden_2 = 60\n",
    "n_hidden_3 = 60\n",
    "n_hidden_4 = 60\n",
    "\n",
    "x = tf.placeholder(tf.float32,[None,n_dim])\n",
    "y_ = tf.placeholder(tf.float32,[None,n_class])\n",
    "w = tf.Variable(tf.zeros([n_dim,n_class]))\n",
    "b = tf.Variable(tf.zeros(n_class))\n",
    "\n",
    "\n",
    "weigths = {\n",
    "    'h1': tf.Variable(tf.truncated_normal([n_dim, n_hidden_1])),\n",
    "    'h2': tf.Variable(tf.truncated_normal([n_hidden_1, n_hidden_2])),\n",
    "    'h3': tf.Variable(tf.truncated_normal([n_hidden_2, n_hidden_3])),\n",
    "    'h4': tf.Variable(tf.truncated_normal([n_hidden_3, n_hidden_4])),\n",
    "    'out': tf.Variable(tf.truncated_normal([n_hidden_4, n_class]))\n",
    "}\n",
    "biases = {\n",
    "    'b1': tf.Variable(tf.truncated_normal([n_hidden_1])),\n",
    "    'b2': tf.Variable(tf.truncated_normal([n_hidden_2])),\n",
    "    'b3': tf.Variable(tf.truncated_normal([n_hidden_3])),\n",
    "    'b4': tf.Variable(tf.truncated_normal([n_hidden_4])),\n",
    "    'out': tf.Variable(tf.truncated_normal([n_class]))\n",
    "}\n",
    "\n",
    "#define our model\n",
    "def multilayer_perceptron(x,weigths,biases):\n",
    "    \n",
    "    layer_1 = tf.add(tf.matmul(x, weigths['h1']), biases['b1'])\n",
    "    layer_1 = tf.nn.sigmoid(layer_1)\n",
    "    \n",
    "    layer_2 = tf.add(tf.matmul(layer_1, weigths['h2']), biases['b2'])\n",
    "    layer_2 = tf.nn.sigmoid(layer_2)\n",
    "    \n",
    "    layer_3 = tf.add(tf.matmul(layer_2, weigths['h3']), biases['b3'])\n",
    "    layer_3 = tf.nn.sigmoid(layer_3)\n",
    "    \n",
    "    layer_4 = tf.add(tf.matmul(layer_3, weigths['h4']), biases['b4'])\n",
    "    layer_4 = tf.nn.relu(layer_4)\n",
    "    \n",
    "    out_layer = tf.matmul(layer_4, weigths['out']) + biases['out']\n",
    "    return out_layer\n",
    "\n",
    "init = tf.global_variables_initializer()\n",
    "\n",
    "saver = tf.train.Saver()\n",
    "\n",
    "y = multilayer_perceptron(x,weigths,biases)\n",
    "\n",
    "cost_function = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y,labels=y_))\n",
    "training_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost_function)\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(init)\n",
    "saver.restore(sess, model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = tf.argmax(y, 1)\n",
    "correct_prediction = tf.equal(prediction, tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy=  0.8333333\n"
     ]
    }
   ],
   "source": [
    "print('Test Accuracy= ',(sess.run(accuracy, feed_dict={x: test_x, y_: test_y})))"
   ]
  },
  {
   "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}