机器学习笔记(5)：多类逻辑回归-手动添加隐藏层

from mxnet import gluon
from mxnet import ndarray as nd
import matplotlib.pyplot as plt
import mxnet as mx
from 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 = 256
train_data = gluon.data.DataLoader(mnist_train, batch_size, shuffle=True)
test_data = gluon.data.DataLoader(mnist_test, batch_size, shuffle=False)

num_inputs = 784
num_outputs = 10

#增加一层包含256个节点的隐藏层
num_hidden = 256
weight_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()

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()))