038d7bb200
* add earlystopping callback * addressing comments * address comments * addressing comments * remove unused imports
71 lines
2.2 KiB
Python
71 lines
2.2 KiB
Python
import numpy as np
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from keras_core import backend
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from keras_core import operations as ops
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from keras_core.api_export import keras_core_export
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def l2_normalize(x, axis=0):
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epsilon = backend.epsilon()
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square_sum = ops.sum(ops.square(x), axis=axis, keepdims=True)
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l2_norm = ops.reciprocal(ops.sqrt(ops.maximum(square_sum, epsilon)))
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return ops.multiply(x, l2_norm)
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@keras_core_export("keras_core.utils.to_categorical")
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def to_categorical(x, num_classes=None):
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"""Converts a class vector (integers) to binary class matrix.
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E.g. for use with `categorical_crossentropy`.
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Args:
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x: Array-like with class values to be converted into a matrix
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(integers from 0 to `num_classes - 1`).
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num_classes: Total number of classes. If `None`, this would be inferred
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as `max(x) + 1`. Defaults to `None`.
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Returns:
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A binary matrix representation of the input as a NumPy array. The class
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axis is placed last.
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Example:
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>>> a = keras_core.utils.to_categorical([0, 1, 2, 3], num_classes=4)
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>>> print(a)
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[[1. 0. 0. 0.]
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[0. 1. 0. 0.]
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[0. 0. 1. 0.]
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[0. 0. 0. 1.]]
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>>> b = np.array([.9, .04, .03, .03,
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... .3, .45, .15, .13,
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... .04, .01, .94, .05,
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... .12, .21, .5, .17],
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... shape=[4, 4])
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>>> loss = keras_core.backend.categorical_crossentropy(a, b)
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>>> print(np.around(loss, 5))
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[0.10536 0.82807 0.1011 1.77196]
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>>> loss = keras_core.backend.categorical_crossentropy(a, a)
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>>> print(np.around(loss, 5))
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[0. 0. 0. 0.]
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"""
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if backend.is_tensor(x):
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return backend.nn.one_hot(x, num_classes)
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x = np.array(x, dtype="int64")
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input_shape = x.shape
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# Shrink the last dimension if the shape is (..., 1).
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if input_shape and input_shape[-1] == 1 and len(input_shape) > 1:
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input_shape = tuple(input_shape[:-1])
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x = x.reshape(-1)
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if not num_classes:
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num_classes = np.max(x) + 1
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batch_size = x.shape[0]
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categorical = np.zeros((batch_size, num_classes))
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categorical[np.arange(batch_size), x] = 1
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output_shape = input_shape + (num_classes,)
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categorical = np.reshape(categorical, output_shape)
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return categorical
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