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<!DOCTYPE html>
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<title>Machine Learning (2017, Fall)</title>
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# Problem 3: Analyze the Model by Confusion Matrix
Problem Description:
* put the prediction and true label in cofusion matrix of your splited validation data
* describe what you observed
Hint:
* you can pick up some images and record their probability distributions over 7 classes.
## 範例
**[Note] 請不要直接使用助教的圖來當成作業交上來**
## TA hour
假設已經訓練了一個不錯的模型,將其預測在validation data上。
<i class="fa fa-diamond"></i> Keywords: `sklearn.metrics.confusion_matrix`, `keras.load_model`
<div id="doc" class="markdown-body container-fluid" style="position: relative;"><h1 id="hw3-手把手-q3"><a class="anchor hidden-xs" href="#hw3-手把手-q3" title="hw3-手把手-q3"><span class="octicon octicon-link"></span></a>HW3 手把手 Q3</h1><h2 id="畫confusion-matrix"><a class="anchor hidden-xs" href="#畫confusion-matrix" title="畫confusion-matrix"><span class="octicon octicon-link"></span></a>畫Confusion Matrix</h2><pre><code class="python hljs"><span class="hljs-comment">#!/usr/bin/env python</span>
<span class="hljs-comment"># -- coding: utf-8 --</span>
<span class="hljs-keyword">from</span> keras.models <span class="hljs-keyword">import</span> load_model
<span class="hljs-keyword">from</span> sklearn.metrics <span class="hljs-keyword">import</span> confusion_matrix
<span class="hljs-keyword">from</span> utils <span class="hljs-keyword">import</span> *
<span class="hljs-keyword">import</span> itertools
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">plot_confusion_matrix</span><span class="hljs-params">(cm, classes,
title=<span class="hljs-string">'Confusion matrix'</span>,
cmap=plt.cm.jet)</span>:</span>
<span class="hljs-string">"""
This function prints and plots the confusion matrix.
"""</span>
cm = cm.astype(<span class="hljs-string">'float'</span>) / cm.sum(axis=<span class="hljs-number">1</span>)[:, np.newaxis]
plt.imshow(cm, interpolation=<span class="hljs-string">'nearest'</span>, cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=<span class="hljs-number">45</span>)
plt.yticks(tick_marks, classes)
thresh = cm.max() / <span class="hljs-number">2.</span>
<span class="hljs-keyword">for</span> i, j <span class="hljs-keyword">in</span> itertools.product(range(cm.shape[<span class="hljs-number">0</span>]), range(cm.shape[<span class="hljs-number">1</span>])):
plt.text(j, i, <span class="hljs-string">'{:.2f}'</span>.format(cm[i, j]), horizontalalignment=<span class="hljs-string">"center"</span>,
color=<span class="hljs-string">"white"</span> <span class="hljs-keyword">if</span> cm[i, j] > thresh <span class="hljs-keyword">else</span> <span class="hljs-string">"black"</span>)
plt.tight_layout()
plt.ylabel(<span class="hljs-string">'True label'</span>)
plt.xlabel(<span class="hljs-string">'Predicted label'</span>)
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">read_dataset</span><span class="hljs-params">(data_path)</span>:</span>
train_pixels = load_pickle(data_path)
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(len(train_pixels)):
train_pixels[i] = np.fromstring(train_pixels[i], dtype=float, sep=<span class="hljs-string">' '</span>).reshape((<span class="hljs-number">48</span>, <span class="hljs-number">48</span>, <span class="hljs-number">1</span>))
<span class="hljs-keyword">return</span> np.asarray(train_pixels)
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">get_labels</span><span class="hljs-params">(data_path)</span>:</span>
train_labels = load_pickle(data_path)
train = []
<span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> range(len(train_labels)):
train.append(int(train_labels[i]))
<span class="hljs-keyword">return</span> np.asarray(train)
<span class="hljs-function"><span class="hljs-keyword">def</span> <span class="hljs-title">main</span><span class="hljs-params">()</span>:</span>
model_path = <span class="hljs-string">'model/model-7.h5'</span>
emotion_classifier = load_model(model_path)
np.set_printoptions(precision=<span class="hljs-number">2</span>)
dev_feats = read_dataset(<span class="hljs-string">'../test_with_ans_pixels.pkl'</span>)
predictions = emotion_classifier.predict(dev_feats)
predictions = predictions.argmax(axis=<span class="hljs-number">-1</span>)
<span class="hljs-keyword">print</span> (predictions)
te_labels = get_labels(<span class="hljs-string">'../test_with_ans_labels.pkl'</span>)
<span class="hljs-keyword">print</span> (te_labels)
conf_mat = confusion_matrix(te_labels,predictions)
plt.figure()
plot_confusion_matrix(conf_mat, classes=[<span class="hljs-string">"Angry"</span>,<span class="hljs-string">"Disgust"</span>,<span class="hljs-string">"Fear"</span>,<span class="hljs-string">"Happy"</span>,<span class="hljs-string">"Sad"</span>,<span class="hljs-string">"Surprise"</span>,<span class="hljs-string">"Neutral"</span>])
plt.show()
<span class="hljs-keyword">if</span> __name__==<span class="hljs-string">'__main__'</span>:
main()
</code></pre><div class="resize-sensor" style="position: absolute; left: 0px; top: 0px; right: 0px; bottom: 0px; overflow: hidden; z-index: -1; visibility: hidden;"><div class="resize-sensor-expand" style="position: absolute; left: 0; top: 0; right: 0; bottom: 0; overflow: hidden; z-index: -1; visibility: hidden;"><div style="position: absolute; left: 0px; top: 0px; transition: 0s; width: 100000px; height: 100000px;"></div></div><div class="resize-sensor-shrink" style="position: absolute; left: 0; top: 0; right: 0; bottom: 0; overflow: hidden; z-index: -1; visibility: hidden;"><div style="position: absolute; left: 0; top: 0; transition: 0s; width: 200%; height: 200%"></div></div></div></div>
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