In [105]:
import numpy as np
k_w = 28
k_h = 28
def read_images(n):
"""
Read images from the mnist handwritten digits dataset (60.000 images)
"""
with open('/home/ch/ev3dev/digit-recognition-training-data/train-images.idx3-ubyte', 'r') as f:
fstr = np.fromfile(f, np.dtype('>i4'), 2)
sz = np.fromfile(f, np.dtype('>i4'), 2)
a = [np.fromfile(f, np.dtype('>u1'), k_w * k_h).astype(np.float64) / 255.0 * 2 -1 for i in xrange(0, n)]
f.close()
return np.asarray(a)
def read_labels(n):
"""
Read images from the mnist handwritten digits dataset (60.000 images)
"""
with open('/home/ch/ev3dev/digit-recognition-training-data/train-labels.idx1-ubyte', 'r') as f:
sz = np.fromfile(f, np.dtype('>i4'), 2)
a = [np.fromfile(f, np.dtype('>u1'), 1)[0] for i in xrange(0, n)]
f.close()
return np.asarray(a)
In [106]:
images = read_images(3000)
labels = read_labels(3000)
len(images)
len(labels)
Out[106]:
In [107]:
import matplotlib.pyplot as plt
%matplotlib inline
In [108]:
img = images[0]
plt.imshow((255 - img).reshape(28, 28), cmap=plt.cm.gray, interpolation='none')
Out[108]:
In [109]:
In [110]:
# Standard scientific Python imports
import matplotlib.pyplot as plt
# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, metrics
# Create a classifier: a support vector classifier
classifier = svm.SVC(kernel="rbf", C=2.8, gamma=.0073)
n_samples = len(images)
# We learn the digits on the first half of the digits
classifier.fit(images[:n_samples / 2], labels[:n_samples / 2])
# Now predict the value of the digit on the second half:
expected = labels[n_samples / 2:]
predicted = classifier.predict(images[n_samples / 2:])
print("Classification report for classifier %s:\n%s\n"
% (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
images_and_predictions = list(zip([image.reshape(28, 28) for image in images[n_samples / 2:]], predicted, expected))
for index, (image, prediction, expected) in enumerate(images_and_predictions[:4]):
plt.subplot(2, 4, index + 5)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Pred: %i, %i' % (prediction, expected))
plt.show()
In [79]:
In [82]:
# Original
#
# Author: Gael Varoquaux <gael dot varoquaux at normalesup dot org>
# License: BSD 3 clause
# Standard scientific Python imports
import matplotlib.pyplot as plt
# Import datasets, classifiers and performance metrics
from sklearn import datasets, svm, metrics
# The digits dataset
digits = datasets.load_digits()
# The data that we are interested in is made of 8x8 images of digits, let's
# have a look at the first 3 images, stored in the `images` attribute of the
# dataset. If we were working from image files, we could load them using
# pylab.imread. Note that each image must have the same size. For these
# images, we know which digit they represent: it is given in the 'target' of
# the dataset.
images_and_labels = list(zip(digits.images, digits.target))
for index, (image, label) in enumerate(images_and_labels[:4]):
plt.subplot(2, 4, index + 1)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Training: %i' % label)
# To apply a classifier on this data, we need to flatten the image, to
# turn the data in a (samples, feature) matrix:
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
# Create a classifier: a support vector classifier
classifier = svm.SVC(gamma=0.001)
# We learn the digits on the first half of the digits
classifier.fit(data[:n_samples / 2], digits.target[:n_samples / 2])
# Now predict the value of the digit on the second half:
expected = digits.target[n_samples / 2:]
predicted = classifier.predict(data[n_samples / 2:])
print("Classification report for classifier %s:\n%s\n"
% (classifier, metrics.classification_report(expected, predicted)))
print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
images_and_predictions = list(zip(digits.images[n_samples / 2:], predicted))
for index, (image, prediction) in enumerate(images_and_predictions[:4]):
plt.subplot(2, 4, index + 5)
plt.axis('off')
plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
plt.title('Prediction: %i' % prediction)
plt.show()
In [100]:
print type(digits.images[:1][0][0])
print type(images[:1][0][0])
In [85]:
len(data)
Out[85]:
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