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101 lines
3.9 KiB
Python
101 lines
3.9 KiB
Python
4 years ago
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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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# 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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# Compute the gradient of the loss function and store it dW. #
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# Rather than first computing the loss and then computing the derivative, #
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# it may be simpler to compute the derivative at the same time that the #
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# loss is being computed. As a result you may need to modify some of the #
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# code above to compute the gradient. #
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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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# Implement a vectorized version of the structured SVM loss, storing the #
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# result in loss. #
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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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# Implement a vectorized version of the gradient for the structured SVM #
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# loss, storing the result in dW. #
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# #
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# Hint: Instead of computing the gradient from scratch, it may be easier #
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# to reuse some of the intermediate values that you used to compute the #
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# loss. #
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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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