Using Tensorflow to predict labels on the Iris Dataset

skflow_iris_example.ipynb

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   "source": [
    "## Using Tensorflow to predict Iris Dataset"
   ]
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   "source": [
    "import tensorflow.contrib.learn as skflow\n",
    "import tensorflow as tf\n",
    "tf.logging.set_verbosity(tf.logging.ERROR)\n",
    "\n",
    "from sklearn import datasets, metrics"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "iris = datasets.load_iris()"
   ]
  },
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   "execution_count": null,
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   "source": [
    "feature_columns = [tf.contrib.layers.real_valued_column(\"\", dimension = 4)]"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "MODEL_PATH='/tmp/tf_examples/iris/'"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "classifier = skflow.DNNClassifier(feature_columns=feature_columns,\n",
    "                                  hidden_units=[10, 20, 10],\n",
    "                                  n_classes=3,\n",
    "                                  model_dir=MODEL_PATH)\n",
    "classifier.fit(x=iris.data, y=iris.target, steps=5000)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "y_pred = classifier.predict(iris.data)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "score = metrics.accuracy_score(iris.target, list(y_pred))\n",
    "print(\"Accuracy: %f\" % score)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`tensorboard --logdir='/tmp/tf_examples/iris/'`"
   ]
  }
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   "author": "Paul Balzer",
   "title": "Positioning"
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