ANN  - Artificial Neural Networks.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "X = iris.data[:, (2, 3)]  # petal length, petal width\n",
    "y = (iris.target == 0).astype(np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import Perceptron\n",
    "\n",
    "per_clf = Perceptron(random_state=42)\n",
    "per_clf.fit(X, y)\n",
    "\n",
    "y_pred = per_clf.predict([[2, 2.5]])\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n",
      "Extracting data\\train-images-idx3-ubyte.gz\n",
      "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n",
      "Extracting data\\train-labels-idx1-ubyte.gz\n",
      "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n",
      "Extracting data\\t10k-images-idx3-ubyte.gz\n",
      "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n",
      "Extracting data\\t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train = mnist.train.images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_test = mnist.test.images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_train = mnist.train.labels.astype(\"int\")\n",
    "y_test = mnist.test.labels.astype(\"int\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[_RealValuedColumn(column_name='', dimension=784, default_value=None, dtype=tf.float32, normalizer=None)]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Using temporary folder as model directory: C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmpyezerqtr\n",
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_is_chief': True, '_tf_config': gpu_options {\n",
      "  per_process_gpu_memory_fraction: 1\n",
      "}\n",
      ", '_evaluation_master': '', '_tf_random_seed': None, '_master': '', '_save_checkpoints_secs': 600, '_task_type': None, '_save_checkpoints_steps': None, '_save_summary_steps': 100, '_keep_checkpoint_max': 5, '_num_ps_replicas': 0, '_environment': 'local', '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x000001FA172D7DD8>, '_keep_checkpoint_every_n_hours': 10000}\n"
     ]
    }
   ],
   "source": [
    "dnn_clf = tf.contrib.learn.DNNClassifier(hidden_units=[300, 100], n_classes=10,\n",
    "                                         feature_columns=feature_columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-15-996bf4946c0d>:1: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-15-996bf4946c0d>:1: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with y is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n",
      "WARNING:tensorflow:From <ipython-input-15-996bf4946c0d>:1: calling BaseEstimator.fit (from tensorflow.contrib.learn.python.learn.estimators.estimator) with batch_size is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ZekeLabs\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  equality = a == b\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Instructions for updating:\n",
      "Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmpyezerqtr\\model.ckpt.\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:global_step/sec: 61.5942\n",
      "INFO:tensorflow:loss = 0.000261911, step = 36301\n",
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      "INFO:tensorflow:loss = 0.000260192, step = 39301\n",
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      "INFO:tensorflow:loss = 0.000590993, step = 39401\n",
      "INFO:tensorflow:global_step/sec: 75.1753\n",
      "INFO:tensorflow:loss = 0.000220207, step = 39501\n",
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      "INFO:tensorflow:loss = 0.000665894, step = 39601\n",
      "INFO:tensorflow:global_step/sec: 66.128\n",
      "INFO:tensorflow:loss = 0.000151209, step = 39701\n",
      "INFO:tensorflow:global_step/sec: 70.7489\n",
      "INFO:tensorflow:loss = 0.000841704, step = 39801\n",
      "INFO:tensorflow:global_step/sec: 74.8379\n",
      "INFO:tensorflow:loss = 0.000961712, step = 39901\n",
      "INFO:tensorflow:Saving checkpoints for 40000 into C:\\Users\\ZekeLabs\\AppData\\Local\\Temp\\tmpyezerqtr\\model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 0.000520956.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "DNNClassifier(params={'optimizer': None, 'dropout': None, 'input_layer_min_slice_size': None, 'embedding_lr_multipliers': None, 'gradient_clip_norm': None, 'hidden_units': [300, 100], 'head': <tensorflow.contrib.learn.python.learn.estimators.head._MultiClassHead object at 0x000001FA1F5401D0>, 'activation_fn': <function relu at 0x000001FA1C7430D0>, 'feature_columns': (_RealValuedColumn(column_name='', dimension=784, default_value=None, dtype=tf.float32, normalizer=None),)})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dnn_clf.fit(x=X_train, y=y_train, batch_size=50, steps=40000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\ZekeLabs\\Anaconda3\\lib\\site-packages\\tensorflow\\contrib\\learn\\python\\learn\\estimators\\dnn.py:374: calling BaseEstimator.predict (from tensorflow.contrib.learn.python.learn.estimators.estimator) with x is deprecated and will be removed after 2016-12-01.\n",
      "Instructions for updating:\n",
      "Estimator is decoupled from Scikit Learn interface by moving into\n",
      "separate class SKCompat. Arguments x, y and batch_size are only\n",
      "available in the SKCompat class, Estimator will only accept input_fn.\n",
      "Example conversion:\n",
      "  est = Estimator(...) -> est = SKCompat(Estimator(...))\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ZekeLabs\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py:247: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.\n",
      "  equality = a == b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.98199999999999998"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "y_pred = list(dnn_clf.predict(X_test))\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
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    "version": 3
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