了解神经网络原理的同学们应该都知道，隐藏层越多，最终预测结果的准确度越高，但是计算量也越大，在的基础上，我们手动添加一个隐藏层，代码如下（主要参考自）：
from mxnet import gluonfrom mxnet import ndarray as ndimport matplotlib.pyplot as pltimport mxnet as mxfrom mxnet import autograd def transform(data, label): return data.astype('float32')/255, label.astype('float32') mnist_train = gluon.data.vision.FashionMNIST(train=True, transform=transform)mnist_test = gluon.data.vision.FashionMNIST(train=False, transform=transform) def show_images(images): n = images.shape[0] _, figs = plt.subplots(1, n, figsize=(15, 15)) for i in range(n): figs[i].imshow(images[i].reshape((28, 28)).asnumpy()) figs[i].axes.get_xaxis().set_visible(False) figs[i].axes.get_yaxis().set_visible(False) plt.show()def get_text_labels(label): text_labels = [ 'T 恤', '长 裤', '套头衫', '裙 子', '外 套', '凉 鞋', '衬 衣', '运动鞋', '包 包', '短 靴' ] return [text_labels[int(i)] for i in label] data, label = mnist_train[0:10] print('example shape: ', data.shape, 'label:', label)show_images(data)print(get_text_labels(label)) batch_size = 256train_data = gluon.data.DataLoader(mnist_train, batch_size, shuffle=True)test_data = gluon.data.DataLoader(mnist_test, batch_size, shuffle=False) num_inputs = 784num_outputs = 10 #增加一层包含256个节点的隐藏层num_hidden = 256weight_scale = .01 #输入层的参数W1 = nd.random_normal(shape=(num_inputs, num_hidden), scale=weight_scale)b1 = nd.zeros(num_hidden) #隐藏层的参数W2 = nd.random_normal(shape=(num_hidden, num_outputs), scale=weight_scale)b2 = nd.zeros(num_outputs) #参数变多了params = [W1, b1, W2, b2] for param in params: param.attach_grad() #激活函数def relu(X): return nd.maximum(X, 0) #计算模型def net(X): X = X.reshape((-1, num_inputs)) #先计算到隐藏层的输出 h1 = relu(nd.dot(X, W1) + b1) #再利用隐藏层计算最终的输出 output = nd.dot(h1, W2) + b2 return output #Softmax和交叉熵损失函数softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()#梯度下降法def SGD(params, lr): for param in params: param[:] = param - lr * param.grad def accuracy(output, label): return nd.mean(output.argmax(axis=1) == label).asscalar() def _get_batch(batch): if isinstance(batch, mx.io.DataBatch): data = batch.data[0] label = batch.label[0] else: data, label = batch return data, label def evaluate_accuracy(data_iterator, net): acc = 0. if isinstance(data_iterator, mx.io.MXDataIter): data_iterator.reset() for i, batch in enumerate(data_iterator): data, label = _get_batch(batch) output = net(data) acc += accuracy(output, label) return acc / (i+1) learning_rate = .5 for epoch in range(5): train_loss = 0. train_acc = 0. for data, label in train_data: with autograd.record(): output = net(data) #使用Softmax和交叉熵损失函数 loss = softmax_cross_entropy(output, label) loss.backward() SGD(params, learning_rate / batch_size) train_loss += nd.mean(loss).asscalar() train_acc += accuracy(output, label) test_acc = evaluate_accuracy(test_data, net) print("Epoch %d. Loss: %f, Train acc %f, Test acc %f" % ( epoch, train_loss / len(train_data), train_acc / len(train_data), test_acc))data, label = mnist_test[0:10]show_images(data)print('true labels')print(get_text_labels(label)) predicted_labels = net(data).argmax(axis=1)print('predicted labels')print(get_text_labels(predicted_labels.asnumpy())) 有变化的地方，都加了注释，主要改动点有5个：
1. 手动添加了1个隐藏层，该层有256个节点
2. 多了一层，所以参数也变多了
3. 计算y=wx+b模型时，就要一层层来算了
4. 将softmax与交叉熵CrossEntropy合并了(这样避免了单独对softmax求导，理论上讲更稳定些）
5. 另外激活函数换成了收敛速度更快的relu（参考： ）
运行效果：
相对，准确率提升了不少！
tips：类似的思路，我们可以再手动添加第2层隐藏层，关键代码参考下面
...#增加一层包含256个节点的隐藏层num_hidden1 = 256weight_scale1 = .01#再增加一层包含512个节点的隐藏层num_hidden2 = 512weight_scale2 = .01 #输入层的参数W1 = nd.random_normal(shape=(num_inputs, num_hidden1), scale=weight_scale1)b1 = nd.zeros(num_hidden1) #隐藏层的参数W2 = nd.random_normal(shape=(num_hidden1, num_hidden2), scale=weight_scale1)b2 = nd.zeros(num_hidden2)W3 = nd.random_normal(shape=(num_hidden2, num_outputs), scale=weight_scale2)b3 = nd.zeros(num_outputs) #参数变多了params = [W1, b1, W2, b2, W3, b3]...#计算模型def net(X): X = X.reshape((-1, num_inputs)) #先计算到隐藏层的输出 h1 = relu(nd.dot(X, W1) + b1) h2 = relu(nd.dot(h1,W2) + b2) #再利用隐藏层计算最终的输出 output = nd.dot(h2, W3) + b3 return output