test_cases.append(centered_array)
test_digits.append(int(digit))
for index,i in enumerate(centered_array):
if i > 0:
x = index%28
y = index/28
plt.plot(x,y,"o",color="blue")
plt.ylim((28,0))
plt.xlim((0,28))
plt.show()
# plt.ylim((28,0))
# plt.xlim((0,28))
# fig = plt.figure()
# axes = fig.add_subplot(1, 1, 1)
# im = axes.imshow(digit_image)
# plt.show()
# digit_image.save("/home/greg/test.jpg")
# plt.imshow(digit_image)
# plt.show()
if done:
break
import neural_network
training_data, validation_data, test_data = neural_network.load_data_wrapper()
net = neural_network.Network([784, 100, 10])
net.SGD(training_data, 30, 10, 3.0, test_data=zip(test_cases,test_digits))
colors = plt.cm.Spectral(numpy.linspace(0, 1, len(unique_labels)))
def p(a):
for x in range(28):
for y in range(28):
if a[y * 28 + x] > 0:
plt.plot(x, y, "o", color="blue")
plt.xlim((-0.01, 28))
plt.ylim((28, -0.01))
plt.show()
training_data, validation_data, test_data = load_data_wrapper()
# p(test_data[0][0])
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = 'k'
class_member_mask = (labels == k)
xy = t[class_member_mask]
x_l, y_l = zip(*xy)
for x, y in xy:
plt.plot(x, -y, "o", color="blue")
unique_labels = set(labels)
colors = plt.cm.Spectral(numpy.linspace(0, 1, len(unique_labels)))
def p(a):
for x in range(28):
for y in range(28):
if a[y*28+x] > 0:
plt.plot(x,y,"o",color="blue")
plt.xlim((-0.01,28))
plt.ylim((28,-0.01))
plt.show()
training_data, validation_data, test_data = load_data_wrapper()
# p(test_data[0][0])
for k, col in zip(unique_labels, colors):
if k == -1:
# Black used for noise.
col = 'k'
class_member_mask = (labels == k)
xy = t[class_member_mask]
x_l,y_l = zip(*xy)
for x,y in xy:
plt.plot(x,-y,"o",color="blue")
(28**2, 1))
test_cases.append(centered_array)
test_digits.append(int(digit))
for index, i in enumerate(centered_array):
if i > 0:
x = index % 28
y = index / 28
plt.plot(x, y, "o", color="blue")
plt.ylim((28, 0))
plt.xlim((0, 28))
plt.show()
# plt.ylim((28,0))
# plt.xlim((0,28))
# fig = plt.figure()
# axes = fig.add_subplot(1, 1, 1)
# im = axes.imshow(digit_image)
# plt.show()
# digit_image.save("/home/greg/test.jpg")
# plt.imshow(digit_image)
# plt.show()
if done:
break
import neural_network
training_data, validation_data, test_data = neural_network.load_data_wrapper()
net = neural_network.Network([784, 100, 10])
net.SGD(training_data, 30, 10, 3.0, test_data=zip(test_cases, test_digits))
