load('result442','result452'); numSC = 64; % Batch size miniBatchSize = 5000; % Iteration maxEpochs = 50; % Sturcture inputSize = 71; numHiddenUnits = 128; numHiddenUnits2 = 64; numHiddenUnits3 = numSC; numClasses = 16; % DNN Layers layers = [ ... sequenceInputLayer(inputSize) lstmLayer(numHiddenUnits,'OutputMode','last') fullyConnectedLayer(numClasses) softmaxLayer sequenceOutputLayer]; % Training options options = trainingOptions('adam',... 'InitialLearnRate',0.01,... 'ExecutionEnvironment','auto', ... 'GradientThreshold',1, ... 'LearnRateDropFactor',0.1,... 'MaxEpochs',maxEpochs, ... 'MiniBatchSize',miniBatchSize, ... 'Shuffle','every-epoch', ... 'Verbose',1,... 'Plots','training-progress'); % Train the neural network tic; net07 = trainNetwork(result442,result452,layers,options); toc; save('NN08.mat','net6');帮我把这段代码改成python语言

好的，以下是将MATLAB代码转换为Python代码的结果：

import numpy as np
import tensorflow as tf
from tensorflow.keras import layers, models, optimizers

# Load data
result442 = np.load('result442.npy')
result452 = np.load('result452.npy')

# Set hyperparameters
numSC = 64
miniBatchSize = 5000
maxEpochs = 50
inputSize = 71
numHiddenUnits = 128
numHiddenUnits2 = 64
numHiddenUnits3 = numSC
numClasses = 16

# Define DNN model
model = models.Sequential([
    layers.Input(shape=(None, inputSize)),
    layers.LSTM(numHiddenUnits, return_sequences=False),
    layers.Dense(numClasses),
    layers.Softmax(),
])

# Compile the model
model.compile(optimizer=optimizers.Adam(lr=0.01),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# Train the model
history = model.fit(result442, result452,
                    epochs=maxEpochs,
                    batch_size=miniBatchSize,
                    shuffle=True)

# Save the model
model.save('NN08.h5')

请注意，Python中没有与MATLAB中的SequenceOutputLayer相对应的层。在上面的Python代码中，我们使用了return_sequences=False来指定LSTM层只返回最后一个时间步的输出，这相当于MATLAB中的'OutputMode'为'last'。另外，我们使用了Softmax激活函数来实现分类任务，这与MATLAB中的softmaxLayer层的功能相同。