从零开始

前面了解了多层感知机的原理，我们来实现一个多层感知机。

# -*- coding: utf-8 -*-from mxnet import initfrom mxnet import ndarray as ndfrom mxnet.gluon import loss as glossimport gb# 定义数据源batch_size = 256train_iter, test_iter = gb.load_data_fashion_mnist(batch_size)# 定义模型参数num_inputs = 784num_outputs = 10num_hiddens = 256W1 = nd.random.normal(scale=0.01, shape=(num_inputs, num_hiddens))b1 = nd.zeros(num_hiddens)W2 = nd.random.normal(scale=0.01, shape=(num_hiddens, num_outputs))b2 = nd.zeros(num_outputs)params = [W1, b1, W2, b2]for param in params: ???param.attach_grad()# 定义激活函数def relu(X): ???return nd.maximum(X, 0)# 定义模型def net(X): ???X = X.reshape((-1, num_inputs)) ???H = relu(nd.dot(X, W1) + b1) ???return nd.dot(H, W2) + b2# 定义损失函数loss = gloss.SoftmaxCrossEntropyLoss()# 训练模型num_epochs = 5lr = 0.5gb.train_cpu(net, train_iter, test_iter, loss, num_epochs, batch_size, ????????????params, lr)

添加隐层后，模型的性能大幅提升

# outputepoch 1, loss 0.5029, train acc 0.852, test acc 0.934epoch 2, loss 0.2000, train acc 0.943, test acc 0.956epoch 3, loss 0.1431, train acc 0.959, test acc 0.964epoch 4, loss 0.1138, train acc 0.967, test acc 0.968epoch 5, loss 0.0939, train acc 0.973, test acc 0.973

在定义模型参数和定义模型步骤，仍然有一些繁琐。

使用Gluon

# -*- coding: utf-8 -*-from mxnet import initfrom mxnet import ndarray as ndfrom mxnet.gluon import loss as glossimport gb# 定义数据源batch_size = 256train_iter, test_iter = gb.load_data_fashion_mnist(batch_size)# 定义模型from mxnet.gluon import nnnet = nn.Sequential()net.add(nn.Dense(256, activation='relu'))net.add(nn.Dense(10))net.add(nn.Dense(10))net.initialize(init.Normal(sigma=0.01))# 定义损失函数loss = gloss.SoftmaxCrossEntropyLoss()# 训练模型from mxnet import gluontrainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.5})num_epochs = 5gb.train_cpu(net, train_iter, test_iter, loss, num_epochs, batch_size, ????????????None, None, trainer)# outputepoch 1, loss 1.3065, train acc 0.525, test acc 0.814epoch 2, loss 0.2480, train acc 0.928, test acc 0.950epoch 3, loss 0.1442, train acc 0.958, test acc 0.961epoch 4, loss 0.1060, train acc 0.969, test acc 0.971epoch 5, loss 0.0807, train acc 0.976, test acc 0.973

MXNET：多层感知机

原文地址：https://www.cnblogs.com/houkai/p/9520970.html