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65 rivejä
2.2 KiB
65 rivejä
2.2 KiB
from builtins import range
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import numpy as np
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from random import shuffle
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from past.builtins import xrange
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def softmax_loss_naive(W, X, y, reg):
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"""
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Softmax loss function, naive implementation (with loops)
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Inputs have dimension D, there are C classes, and we operate on minibatches
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of N examples.
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Inputs:
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- W: A numpy array of shape (D, C) containing weights.
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- X: A numpy array of shape (N, D) containing a minibatch of data.
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- y: A numpy array of shape (N,) containing training labels; y[i] = c means
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that X[i] has label c, where 0 <= c < C.
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- reg: (float) regularization strength
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Returns a tuple of:
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- loss as single float
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- gradient with respect to weights W; an array of same shape as W
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"""
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# Initialize the loss and gradient to zero.
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loss = 0.0
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dW = np.zeros_like(W)
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#############################################################################
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# TODO: 使用显式循环计算softmax损失及其梯度。
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# 将损失和梯度分别保存在loss和dW中。
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# 如果你不小心，很容易遇到数值不稳定的情况。
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# 不要忘了正则化！
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return loss, dW
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def softmax_loss_vectorized(W, X, y, reg):
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"""
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Softmax loss function, vectorized version.
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Inputs and outputs are the same as softmax_loss_naive.
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"""
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# Initialize the loss and gradient to zero.
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loss = 0.0
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dW = np.zeros_like(W)
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#############################################################################
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# TODO: 不使用显式循环计算softmax损失及其梯度。
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# 将损失和梯度分别保存在loss和dW中。
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# 如果你不小心，很容易遇到数值不稳定的情况。
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# 不要忘了正则化！
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return loss, dW
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