标签:touch scale typeahead pen pix bookmark apc ima tag
softmax
#!/usr/bin/env python # coding: utf-8 # In[1]: get_ipython().run_line_magic(‘matplotlib‘, ‘inline‘) import gluonbook as gb from mxnet import autograd,nd # In[2]: batch_size = 256 train_iter,test_iter = gb.load_data_fashion_mnist(batch_size) # In[3]: num_inputs = 784 num_outputs = 10 W = nd.random.normal(scale=0.01,shape=(num_inputs,num_outputs)) b = nd.zeros(num_outputs) # In[4]: W.attach_grad() b.attach_grad() # softmax运算 # In[5]: X = nd.array([[1,2,3],[4,5,6]]) X.sum(axis=0,keepdims=True) # In[6]: def softmax(X): X_exp = X.exp() partition = X_exp.sum(axis = 1,keepdims = True) return X_exp / partition # 例如 # In[7]: X = nd.random.normal(shape=(2,5)) X_prob = softmax(X) X_prob,X_prob.sum(axis=1) # 定义模型 # In[8]: def net(X): return softmax(nd.dot(X.reshape((-1,num_inputs)),W)+b) # 定义损失函数 # In[9]: y_hat = nd.array([[0.1,0.3,0.6],[0.3,0.2,0.5]]) y = nd.array([0,2]) nd.pick(y_hat,y) # 交叉熵损失函数 # In[10]: def cross_entropy(y_hat,y): return - nd.pick(y_hat,y).log() # 计算分类准确率 # In[11]: def accuracy(y_hat,y): return (y_hat.argmax(axis=1)==y.astype(‘float32‘)).mean().asscalar() # In[12]: accuracy(y_hat,y) # 评价 net 在 data_iter上的准确率 # In[13]: def evaluate_accuracy(data_iter,net): acc = 0 for X,y in data_iter: acc += accuracy(net(X),y) return acc / len(data_iter) # In[14]: evaluate_accuracy(test_iter,net) # 训练模型 # In[15]: num_epochs, lr = 5, 0.1 def train(net, train_iter, test_iter, loss, num_epochs, batch_size, params=None, lr=None, trainer=None): for epoch in range(num_epochs): train_l_sum = 0 train_acc_sum = 0 for X, y in train_iter: # 对每个样本预测 with autograd.record(): y_hat = net(X) # 计算预测值 XW+b l = loss(y_hat, y) # 计算交叉熵函数 l.backward() # 交叉熵函数求导 gb.sgd(params, lr, batch_size) # 修改参数 W,b train_l_sum += l.mean().asscalar() train_acc_sum += accuracy(y_hat, y) test_acc = evaluate_accuracy(test_iter, net) print(‘epoch %d, loss %.4f, train acc %.3f, test acc %.3f‘ % (epoch + 1, train_l_sum / len(train_iter), train_acc_sum / len(train_iter), test_acc)) train(net, train_iter, test_iter, cross_entropy, num_epochs,batch_size, [W, b], lr) # 预测 # In[16]: for X, y in test_iter: break true_labels = gb.get_fashion_mnist_labels(y.asnumpy()) pred_labels = gb.get_fashion_mnist_labels(net(X).argmax(axis=1).asnumpy()) titles = [true + ‘\n‘ + pred for true, pred in zip(true_labels, pred_labels)] gb.show_fashion_mnist(X[0:9], titles[0:9])
标签:touch scale typeahead pen pix bookmark apc ima tag
原文地址:https://www.cnblogs.com/TreeDream/p/10002918.html