Lasagne怎么處理多類別分類任務(wù)

處理多類別分類任務(wù)時，可以使用Lasagne庫中的NeuralNetwork類來構(gòu)建神經(jīng)網(wǎng)絡(luò)模型。以下是一個示例代碼，展示如何在Lasagne中處理多類別分類任務(wù)：

import lasagne
import theano
import theano.tensor as T

# 定義輸入數(shù)據(jù)和標簽的符號變量
input_var = T.tensor4('inputs')
target_var = T.ivector('targets')

# 構(gòu)建神經(jīng)網(wǎng)絡(luò)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)

# 定義損失函數(shù)和更新規(guī)則
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)

# 編譯訓(xùn)練函數(shù)和測試函數(shù)
train_fn = theano.function([input_var, target_var], loss, updates=updates)
test_fn = theano.function([input_var, target_var], loss)

# 訓(xùn)練模型
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))

在上面的代碼中，首先定義了輸入數(shù)據(jù)和標簽的符號變量，然后構(gòu)建了一個包含卷積層、池化層和全連接層的神經(jīng)網(wǎng)絡(luò)模型。接著定義了損失函數(shù)和更新規(guī)則，以及編譯了訓(xùn)練函數(shù)和測試函數(shù)。最后，在訓(xùn)練模型的循環(huán)中，通過調(diào)用訓(xùn)練函數(shù)和測試函數(shù)來訓(xùn)練和評估模型。

通過使用Lasagne庫，可以方便地構(gòu)建和訓(xùn)練深度神經(jīng)網(wǎng)絡(luò)模型，處理多類別分類任務(wù)。