index.ipynb

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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "from six.moves.urllib.request import urlopen\n",
    "\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "# Data sets\n",
    "IRIS_TRAINING = \"iris_training.csv\"\n",
    "IRIS_TRAINING_URL = \"http://download.tensorflow.org/data/iris_training.csv\"\n",
    "\n",
    "IRIS_TEST = \"iris_test.csv\"\n",
    "IRIS_TEST_URL = \"http://download.tensorflow.org/data/iris_test.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# If the training and test sets aren't stored locally, download them.\n",
    "if not os.path.exists(IRIS_TRAINING):\n",
    "  raw = urlopen(IRIS_TRAINING_URL).read()\n",
    "  with open(IRIS_TRAINING, \"wb\") as f:\n",
    "    f.write(raw)\n",
    "\n",
    "if not os.path.exists(IRIS_TEST):\n",
    "  raw = urlopen(IRIS_TEST_URL).read()\n",
    "  with open(IRIS_TEST, \"wb\") as f:\n",
    "    f.write(raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load datasets.\n",
    "training_set = tf.contrib.learn.datasets.base.load_csv_with_header(\n",
    "  filename=IRIS_TRAINING,\n",
    "  target_dtype=np.int,\n",
    "  features_dtype=np.float32)\n",
    "test_set = tf.contrib.learn.datasets.base.load_csv_with_header(\n",
    "  filename=IRIS_TEST,\n",
    "  target_dtype=np.int,\n",
    "  features_dtype=np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Specify that all features have real-value data\n",
    "feature_columns = [tf.feature_column.numeric_column(\"x\", shape=[4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build 3 layer DNN with 10, 20, 10 units respectively.\n",
    "classifier = tf.estimator.DNNClassifier(feature_columns=feature_columns,\n",
    "                                      hidden_units=[10, 20, 10],\n",
    "                                      n_classes=3,\n",
    "                                      model_dir=\"/tmp/iris_model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the training inputs\n",
    "train_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "  x={\"x\": np.array(training_set.data)},\n",
    "  y=np.array(training_set.target),\n",
    "  num_epochs=None,\n",
    "  shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train model.\n",
    "classifier.train(input_fn=train_input_fn, steps=2000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Define the test inputs\n",
    "test_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "  x={\"x\": np.array(test_set.data)},\n",
    "  y=np.array(test_set.target),\n",
    "  num_epochs=1,\n",
    "  shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate accuracy.\n",
    "accuracy_score = classifier.evaluate(input_fn=test_input_fn)[\"accuracy\"]\n",
    "\n",
    "print(\"\\nTest Accuracy: {0:f}\\n\".format(accuracy_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Classify two new flower samples.\n",
    "new_samples = np.array(\n",
    "  [[6.4, 3.2, 4.5, 1.5],\n",
    "   [5.8, 3.1, 5.0, 1.7]], dtype=np.float32)\n",
    "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "  x={\"x\": new_samples},\n",
    "  num_epochs=1,\n",
    "  shuffle=False)\n",
    "\n",
    "predictions = list(classifier.predict(input_fn=predict_input_fn))\n",
    "predicted_classes = [p[\"classes\"] for p in predictions]\n",
    "\n",
    "print(\n",
    "  \"New Samples, Class Predictions:    {}\\n\"\n",
    "  .format(predicted_classes))"
   ]
  }
 ],
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