25'ten fazla konu seçemezsiniz
Konular bir harf veya rakamla başlamalı, kısa çizgiler ('-') içerebilir ve en fazla 35 karakter uzunluğunda olabilir.
136 satır
5.3 KiB
136 satır
5.3 KiB
from __future__ import print_function
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from builtins import range
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from builtins import object
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import numpy as np
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from daseCV.classifiers.linear_svm import *
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from daseCV.classifiers.softmax import *
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from past.builtins import xrange
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class LinearClassifier(object):
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def __init__(self):
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self.W = None
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def train(self, X, y, learning_rate=1e-3, reg=1e-5, num_iters=100,
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batch_size=200, verbose=False):
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"""
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Train this linear classifier using stochastic gradient descent.
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Inputs:
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- X: A numpy array of shape (N, D) containing training data; there are N
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training samples each of dimension D.
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- y: A numpy array of shape (N,) containing training labels; y[i] = c
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means that X[i] has label 0 <= c < C for C classes.
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- learning_rate: (float) learning rate for optimization.
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- reg: (float) regularization strength.
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- num_iters: (integer) number of steps to take when optimizing
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- batch_size: (integer) number of training examples to use at each step.
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- verbose: (boolean) If true, print progress during optimization.
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Outputs:
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A list containing the value of the loss function at each training iteration.
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"""
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num_train, dim = X.shape
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num_classes = np.max(y) + 1 # assume y takes values 0...K-1 where K is number of classes
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if self.W is None:
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# lazily initialize W
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self.W = 0.001 * np.random.randn(dim, num_classes)
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# Run stochastic gradient descent to optimize W
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loss_history = []
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for it in range(num_iters):
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X_batch = None
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y_batch = None
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#########################################################################
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# TODO:
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# 从训练数据及其相应的标签中采样batch_size大小的样本，以用于本轮梯度下降。
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# 将数据存储在X_batch中，并将其相应的标签存储在y_batch中:
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# 采样后，X_batch的形状为(batch_size,dim)，y_batch的形状(batch_size,)
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#
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# 提示：使用np.random.choice生成索引。 可重复的采样比不可重复的采样要快一点。
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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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# evaluate loss and gradient
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loss, grad = self.loss(X_batch, y_batch, reg)
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loss_history.append(loss)
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# perform parameter update
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#########################################################################
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# TODO:
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# 使用梯度和学习率更新权重。
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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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if verbose and it % 100 == 0:
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print('iteration %d / %d: loss %f' % (it, num_iters, loss))
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return loss_history
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def predict(self, X):
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"""
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Use the trained weights of this linear classifier to predict labels for
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data points.
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Inputs:
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- X: A numpy array of shape (N, D) containing training data; there are N
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training samples each of dimension D.
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Returns:
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- y_pred: Predicted labels for the data in X. y_pred is a 1-dimensional
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array of length N, and each element is an integer giving the predicted
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class.
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"""
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y_pred = np.zeros(X.shape[0])
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###########################################################################
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# TODO:
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# 实现此方法。将预测的标签存储在y_pred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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return y_pred
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def loss(self, X_batch, y_batch, reg):
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"""
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Compute the loss function and its derivative.
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Subclasses will override this.
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Inputs:
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- X_batch: A numpy array of shape (N, D) containing a minibatch of N
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data points; each point has dimension D.
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- y_batch: A numpy array of shape (N,) containing labels for the minibatch.
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- reg: (float) regularization strength.
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Returns: A tuple containing:
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- loss as a single float
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- gradient with respect to self.W; an array of the same shape as W
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"""
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pass
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class LinearSVM(LinearClassifier):
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""" A subclass that uses the Multiclass SVM loss function """
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def loss(self, X_batch, y_batch, reg):
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return svm_loss_vectorized(self.W, X_batch, y_batch, reg)
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class Softmax(LinearClassifier):
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""" A subclass that uses the Softmax + Cross-entropy loss function """
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def loss(self, X_batch, y_batch, reg):
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return softmax_loss_vectorized(self.W, X_batch, y_batch, reg)
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