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February 19, 2016 11:35
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Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,256 @@ { "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import tensorflow as tf\n", "from sklearn import datasets" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "iris = datasets.load_iris()\n", "num_labels = len(set(iris.target))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(150, 4) (150, 3)\n" ] } ], "source": [ "data = iris.data.astype(np.float32)\n", "labels = (np.arange(num_labels) == np.array(iris.target)[:,None]).astype(np.float32)\n", "print(data.shape, labels.shape)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def accuracy(predictions, labels):\n", " return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1))\n", " / predictions.shape[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "softmax regression:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_size = data.shape[1]\n", "\n", "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", " tf_train_dataset = tf.constant(data)\n", " tf_train_labels = tf.constant(labels)\n", "\n", " weights = tf.Variable(tf.truncated_normal([feature_size, num_labels]))\n", " biases = tf.Variable(tf.zeros([num_labels]))\n", "\n", " logits = tf.matmul(tf_train_dataset, weights) + biases\n", " loss = tf.reduce_mean(\n", " tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n", "\n", " optimizer = tf.train.AdamOptimizer(0.1).minimize(loss)\n", " train_prediction = tf.nn.softmax(logits)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step:0 loss:4.024884 accuracy: 66.67\n", "step:500 loss:0.072255 accuracy: 98.00\n", "step:1000 loss:0.054472 accuracy: 98.00\n", "step:1500 loss:0.048384 accuracy: 98.00\n", "step:2000 loss:0.045301 accuracy: 98.00\n", "step:2500 loss:0.043418 accuracy: 98.00\n", "step:3000 loss:0.042158 accuracy: 98.67\n", "step:3500 loss:0.041285 accuracy: 98.67\n", "step:4000 loss:0.040682 accuracy: 98.67\n", "step:4500 loss:0.040273 accuracy: 98.67\n", "step:5000 loss:0.040007 accuracy: 98.67\n", "step:5500 loss:0.039843 accuracy: 98.67\n", "step:6000 loss:0.039750 accuracy: 98.67\n", "step:6500 loss:0.039702 accuracy: 98.67\n", "step:7000 loss:0.039680 accuracy: 98.67\n", "step:7500 loss:0.039674 accuracy: 98.67\n", "step:8000 loss:0.039672 accuracy: 98.67\n", "step:8500 loss:0.039667 accuracy: 98.67\n", "step:9000 loss:0.039665 accuracy: 98.67\n", "step:9500 loss:0.039664 accuracy: 98.67\n", "step:10000 loss:0.039664 accuracy: 98.67\n" ] } ], "source": [ "with tf.Session(graph=graph) as session:\n", " tf.initialize_all_variables().run()\n", " for step in range(10001):\n", " _, l, predictions = session.run([optimizer, loss, train_prediction])\n", " if step % 500 == 0:\n", " print('step:{} loss:{:.6f} accuracy: {:.2f}'.format(\n", " step, l, accuracy(predictions, labels)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "linear multiclass SVM:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "feature_size = data.shape[1]\n", "delta = 1.0\n", "regulation_rate = 5e-4\n", "graph = tf.Graph()\n", "\n", "with graph.as_default():\n", " tf_train_dataset = tf.constant(data)\n", " tf_train_labels = tf.constant(labels)\n", " \n", " weights = tf.Variable(tf.truncated_normal([feature_size, num_labels]))\n", " biases = tf.Variable(tf.zeros([num_labels]))\n", "\n", " logits = tf.matmul(tf_train_dataset, weights) + biases\n", " # TODO better way as numpy's: np.choose(data.target, logits.T)\n", " y = tf.reduce_sum(logits * tf_train_labels, 1, keep_dims=True)\n", " loss = tf.reduce_mean(tf.reduce_sum(tf.maximum(0.0, logits - y + delta), 1)) - delta\n", " loss += regulation_rate * tf.nn.l2_loss(weights)\n", "\n", " optimizer = tf.train.AdamOptimizer(0.1).minimize(loss)\n", " train_prediction = tf.nn.softmax(logits)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "step:0 loss:7.959514 accuracy: 33.33\n", "step:500 loss:0.065034 accuracy: 97.33\n", "step:1000 loss:0.060172 accuracy: 98.00\n", "step:1500 loss:0.058802 accuracy: 98.00\n", "step:2000 loss:0.057681 accuracy: 98.00\n", "step:2500 loss:0.061486 accuracy: 98.00\n", "step:3000 loss:0.057971 accuracy: 98.00\n", "step:3500 loss:0.058048 accuracy: 98.00\n", "step:4000 loss:0.055821 accuracy: 98.00\n", "step:4500 loss:0.058429 accuracy: 98.00\n", "step:5000 loss:0.056925 accuracy: 98.00\n", "step:5500 loss:0.056828 accuracy: 98.00\n", "step:6000 loss:0.060888 accuracy: 98.00\n", "step:6500 loss:0.058099 accuracy: 98.00\n", "step:7000 loss:0.055918 accuracy: 98.00\n", "step:7500 loss:0.056860 accuracy: 98.67\n", "step:8000 loss:0.056827 accuracy: 98.00\n", "step:8500 loss:0.055365 accuracy: 98.00\n", "step:9000 loss:0.055880 accuracy: 98.00\n", "step:9500 loss:0.056300 accuracy: 98.67\n", "step:10000 loss:0.058815 accuracy: 98.00\n" ] } ], "source": [ "with tf.Session(graph=graph) as session:\n", " tf.initialize_all_variables().run()\n", " for step in range(10001):\n", " _, l, predictions = session.run([optimizer, loss, train_prediction])\n", " if step % 500 == 0:\n", " print('step:{} loss:{:.6f} accuracy: {:.2f}'.format(\n", " step, l, accuracy(predictions, labels)))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 0 }