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处理多类别分类任务时，可以使用Lasagne库中的NeuralNetwork类来构建神经网络模型。以下是一个示例代码，展示如何在Lasagne中处理多类别分类任务：
import lasagne import theano import theano.tensor as T # 定义输入数据和标签的符号变量 input_var = T.tensor4('inputs') target_var = T.ivector('targets') # 构建神经网络模型 network = lasagne.layers.InputLayer(shape=(None, num_channels, input_height, input_width), input_var=input_var) network = lasagne.layers.Conv2DLayer(network, num_filters=32, filter_size=(3,3), nonlinearity=lasagne.nonlinearities.rectify) network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2,2)) network = lasagne.layers.Conv2DLayer(network, num_filters=64, filter_size=(3,3), nonlinearity=lasagne.nonlinearities.rectify) network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2,2)) network = lasagne.layers.DenseLayer(lasagne.layers.dropout(network, p=0.5), num_units=256, nonlinearity=lasagne.nonlinearities.rectify) network = lasagne.layers.DenseLayer(lasagne.layers.dropout(network, p=0.5), num_units=num_classes, nonlinearity=lasagne.nonlinearities.softmax) # 定义损失函数和更新规则 prediction = lasagne.layers.get_output(network) loss = lasagne.objectives.categorical_crossentropy(prediction, target_var) loss = loss.mean() params = lasagne.layers.get_all_params(network, trainable=True) updates = lasagne.updates.adam(loss, params) # 编译训练函数和测试函数 train_fn = theano.function([input_var, target_var], loss, updates=updates) test_fn = theano.function([input_var, target_var], loss) # 训练模型 for epoch in range(num_epochs): train_loss = 0 for batch in iterate_minibatches(X_train, y_train, batch_size): inputs, targets = batch train_loss += train_fn(inputs, targets) train_loss /= len(X_train) test_loss = 0 for batch in iterate_minibatches(X_test, y_test, batch_size): inputs, targets = batch test_loss += test_fn(inputs, targets) test_loss /= len(X_test) print("Epoch {}, Train loss: {}, Test loss: {}".format(epoch, train_loss, test_loss)) 在上面的代码中，首先定义了输入数据和标签的符号变量，然后构建了一个包含卷积层、池化层和全连接层的神经网络模型。接着定义了损失函数和更新规则，以及编译了训练函数和测试函数。最后，在训练模型的循环中，通过调用训练函数和测试函数来训练和评估模型。
通过使用Lasagne库，可以方便地构建和训练深度神经网络模型，处理多类别分类任务。
