dekker.one
Group work for a Monash Research Methods course
merged some changes
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mini_proj/Load_Images.py
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mini_proj/Load_Images.py
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print("ERROR: Data may not be completely saved")
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def __main__():
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if __name__ == "__main__":
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# Paths to the Waldo images
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waldo_path = 'waldo_data/64/waldo'
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n_waldo_path = 'waldo_data/64/notwaldo'
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gen_data(waldo_path, n_waldo_path)
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__main__()
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mini_proj/_image_classifier.py
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mini_proj/_image_classifier.py
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class ImageClassifier:
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"""Class to create an ImageClassifier from a regular classifier with 5
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methods that are common amongst classifiers.
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"""
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def __init__(self, clf, *args, **kwargs):
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self.clf = clf(*args, **kwargs)
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def fit(self, X, *args, **kwargs):
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X = X.reshape((len(X), -1))
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return self.clf.fit(X, *args, **kwargs)
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def predict(self, X, *args, **kwargs):
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X = X.reshape((len(X), -1))
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return self.clf.predict(X, *args, **kwargs)
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def score(self, X, *args, **kwargs):
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X = X.reshape((len(X), -1))
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return self.clf.score(X, *args, **kwargs)
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def get_params(self, *args, **kwargs):
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return self.clf.get_params(*args, **kwargs)
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def set_params(self, **params):
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return self.set_params(**params)
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if __name__ == '__main__':
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# Import datasets, classifiers and performance metrics
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from sklearn import datasets, svm, metrics
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# The digits dataset
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digits = datasets.load_digits()
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n_samples = len(digits.images)
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data = digits.images
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# Create a classifier: a support vector classifier
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classifier = ImageClassifier(svm.SVC, gamma=0.001)
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# We learn the digits on the first half of the digits
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classifier.fit(data[:n_samples // 2], digits.target[:n_samples // 2])
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# Now predict the value of the digit on the second half:
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expected = digits.target[n_samples // 2:]
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predicted = classifier.predict(data[n_samples // 2:])
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print("Classification report for classifier %s:\n%s\n"
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% (classifier, metrics.classification_report(expected, predicted)))
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print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
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mini_proj/waldo_model.py
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mini_proj/waldo_model.py
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from keras.layers import Dense, Dropout, Activation, Flatten, Input
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from keras.layers import Conv2D, MaxPooling2D, ZeroPadding2D
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from keras.models import Model
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from keras.layers.normalization import BatchNormalization
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from keras.utils import np_utils
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from sklearn import svm, tree, naive_bayes, ensemble
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from _image_classifier import ImageClassifier
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from keras.optimizers import Adadelta
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from keras.callbacks import ModelCheckpoint
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from keras import backend as K
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K.set_image_dim_ordering('th')
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np.random.seed(7)
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## Define model
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model = FCN()
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svm_iclf = ImageClassifier(svm.SVC)
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tree_iclf = ImageClassifier(tree.DecisionTreeClassifier)
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naive_bayes_iclf = ImageClassifier(naive_bayes.GaussianNBd)
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ensemble_iclf = ImageClassifier(ensemble.RandomForestClassifier)
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## Define training parameters
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epochs = 20 # an epoch is one forward pass and back propogation of all training data
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wk11/week11.tex
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wk11/week11.tex
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\text{otherwise }f(\_,\_,\_) & = & 0
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\end{eqnarray*}
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\begin{figure}[H]
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\includegraphics[scale=0.55]{plots}
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\begin{figure}[ht]
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\includegraphics[scale=0.6]{plots}
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\centering
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\captionsetup{width=0.80\textwidth}
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\caption{Plots of the execution of the cellular automata with the different
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updating methods. From top-left to top-right: Synchronous, Random
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Independent, Random Order. From bottom-left to bottom-right: Clocked,
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the graphs showing the energy released from the system over time to
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gauge how where the runaway reaction occurs.
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\begin{figure}[H]
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\begin{figure}[ht]
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\includegraphics[scale=0.70]{plots2}
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\centering
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\captionsetup{width=0.80\textwidth}
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\caption{Plots of energy released over time. Each plot corresponds a
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different density: 0\%, 5\%, 8\%, 10\%, 11\%, 12\%, 13\%, 15\%, 17\% and 20\%}
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\label{fig:plot2}