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correct : typo _change_ont_hot_label() -> _change_one_hot_label() #2

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correct : typo _change_ont_hot_label() -> _change_one_hot_label() #2

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5e16679
correct : typo _change_ont_hot_label() -> _change_one_hot_label()
heons Mar 15, 2017
28b06ff
fix : Input to numerical_gradient() in main()
heons Mar 17, 2017
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3 changes: 2 additions & 1 deletion ch04/gradient_2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -59,7 +59,8 @@ def tangent_line(f, x):
X = X.flatten()
Y = Y.flatten()

grad = numerical_gradient(function_2, np.array([X, Y]) )
grad = numerical_gradient(function_2, np.array([X, Y]).T)
grad = grad.T

plt.figure()
plt.quiver(X, Y, -grad[0], -grad[1], angles="xy",color="#666666")#,headwidth=10,scale=40,color="#444444")
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6 changes: 3 additions & 3 deletions dataset/mnist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -79,7 +79,7 @@ def init_mnist():
pickle.dump(dataset, f, -1)
print("Done!")

def _change_ont_hot_label(X):
def _change_one_hot_label(X):
T = np.zeros((X.size, 10))
for idx, row in enumerate(T):
row[X[idx]] = 1
Expand Down Expand Up @@ -114,8 +114,8 @@ def load_mnist(normalize=True, flatten=True, one_hot_label=False):
dataset[key] /= 255.0

if one_hot_label:
dataset['train_label'] = _change_ont_hot_label(dataset['train_label'])
dataset['test_label'] = _change_ont_hot_label(dataset['test_label'])
dataset['train_label'] = _change_one_hot_label(dataset['train_label'])
dataset['test_label'] = _change_one_hot_label(dataset['test_label'])

if not flatten:
for key in ('train_img', 'test_img'):
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