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