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- def to_categorical(y, num_classes=None):
- """Converts a class vector (integers) to binary class matrix.
- E.g. for use with categorical_crossentropy.
- # Arguments
- y: class vector to be converted into a matrix
- (integers from 0 to num_classes).
- num_classes: total number of classes.
- # Returns
- A binary matrix representation of the input.
- """
- y = np.array(y, dtype='int').ravel()
- if not num_classes:
- num_classes = np.max(y) + 1
- n = y.shape[0]
- categorical = np.zeros((n, num_classes))
- categorical[np.arange(n), y] = 1
- return categorical
- def normalize(x, axis=-1, order=2):
- """Normalizes a Numpy array.
- # Arguments
- x: Numpy array to normalize.
- axis: axis along which to normalize.
- order: Normalization order (e.g. 2 for L2 norm).
- # Returns
- A normalized copy of the array.
- """
- l2 = np.atleast_1d(np.linalg.norm(x, order, axis))
- l2[l2 == 0] = 1
- return x / np.expand_dims(l2, axis)
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