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97 lines
3.5 KiB
97 lines
3.5 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 svm_loss_naive(W, X, y, reg):
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"""
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Structured SVM 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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dW = np.zeros(W.shape) # initialize the gradient as zero
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# compute the loss and the gradient
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num_classes = W.shape[1]
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num_train = X.shape[0]
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loss = 0.0
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for i in range(num_train):
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scores = X[i].dot(W)
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correct_class_score = scores[y[i]]
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for j in range(num_classes):
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if j == y[i]:
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continue
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margin = scores[j] - correct_class_score + 1 # note delta = 1
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if margin > 0:
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loss += margin
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dW[:,j] += X[i] # dW计算
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dW[:,y[i]] += -X[i] # dW计算
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# Right now the loss is a sum over all training examples, but we want it
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# to be an average instead so we divide by num_train.
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loss /= num_train
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# Add regularization to the loss.
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loss += reg * np.sum(W * W)
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#############################################################################
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# TODO：
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# 计算损失函数的梯度并将其存储为dW。
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# 与其先计算损失再计算梯度，还不如在计算损失的同时计算梯度更简单。
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# 因此，您可能需要修改上面的一些代码来计算梯度。
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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 svm_loss_vectorized(W, X, y, reg):
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"""
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Structured SVM loss function, vectorized implementation.
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Inputs and outputs are the same as svm_loss_naive.
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"""
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loss = 0.0
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dW = np.zeros(W.shape) # initialize the gradient as zero
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#############################################################################
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# TODO:
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# 实现一个向量化SVM损失计算方法,并将结果存储到loss中
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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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#############################################################################
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# TODO:
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# 实现一个向量化的梯度计算方法,并将结果存储到dW中
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#
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# 提示:与其从头计算梯度,不如利用一些计算loss时的中间变量
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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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