深度學(xué)習(xí)cs231n作業(yè)softmax

1、num_classes=W.sh 1num_train=X.sh 0 shift_scores=scores-np.max(scores,axis=1).resh (-1,1) #先轉(zhuǎn)成廣播機(jī)softmax_output=np.exp(shift_scores)/np.sum(np.exp(shift_scores),axis=1).resh (-loss=-np.sum(np.log(softmax_outputrange(num_train),list(y) #softmax_outputrange(num_train),list(y)計(jì)算的是正確分類num_classes=W.sh 1n

2、um_train=X.sh 0 shift_scores=scores-np.max(scores,axis=1).resh (-1,1) #先轉(zhuǎn)成廣播機(jī)softmax_output=np.exp(shift_scores)/np.sum(np.exp(shift_scores),axis=1).resh (-loss=-np.sum(np.log(softmax_outputrange(num_train),list(y) #softmax_outputrange(num_train),list(y)計(jì)算的是正確分類y_i的損失 return 這份代碼被保存到了softmax.py中#1.2

3、 分類器的構(gòu)importnumpyasnp import sysfrom softmax.softport classLinearClassifier: def init self.W = deftrain(self,X,y,learning_rate=1e-3,reg=1e-5,num_iters=100, batch_size=200, ver e=False):num_train,dim = num_classes = np.max(y) + 1 # a me y takes values 0.K-1 where K is number of classes if self.W is N

4、one:# lazily initialize self.W = 0.001 * np.random.randn(dim, #RunstochasticgradientdescenttooptimizeW loss_history = for it in X_batch=None y_batch = batch_idx=np.random.choice(num_train,batch_size,replace=True) X_batch = Xbatch_idxy_batch = # oss and loss, grad = self.loss(X_batch, y_batch, self.W

5、 += -1 * learning_rate * if ver e and it % 100 = pr if ver e and it % 100 = pr (iteration %d / %d: loss %f % (it, num_iters, return def predict(self, y_pred=np.zeros(X.sh 1) scores = X.dot(self.W)y_pred=np.argmax(scores,axis=1) return y_preddef losf,X_batch,y_batch,reg): class A t uses the Softmax + Cross-entropy loss function def f, X_batch, y_batch, retu