如何使用Lasagne框架構建卷積神經網絡

Lasagne是一個建立在Theano庫之上的輕量級深度學習庫，可以用來構建神經網絡模型。下面是一個使用Lasagne框架構建卷積神經網絡的簡單示例：

1. 首先，導入必要的庫：

import lasagne
import theano
import theano.tensor as T

2. 定義輸入變量：

input_var = T.tensor4('inputs')
target_var = T.ivector('targets')

3. 構建卷積神經網絡模型：

# 定義網絡結構
network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
network = lasagne.layers.Conv2DLayer(network, num_filters=32, filter_size=(5, 5), 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(network, num_units=256, nonlinearity=lasagne.nonlinearities.rectify)
network = lasagne.layers.DenseLayer(network, num_units=10, nonlinearity=lasagne.nonlinearities.softmax)

# 定義損失函數
prediction = lasagne.layers.get_output(network)
loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
loss = loss.mean()

# 定義更新規則
params = lasagne.layers.get_all_params(network, trainable=True)
updates = lasagne.updates.adam(loss, params)

# 編譯訓練函數
train_fn = theano.function([input_var, target_var], loss, updates=updates)

4. 進行訓練和預測：

# 訓練模型
for epoch in range(num_epochs):
    for batch in iterate_minibatches(train_data, train_labels, batch_size, shuffle=True):
        inputs, targets = batch
        train_fn(inputs, targets)

# 預測
test_prediction = lasagne.layers.get_output(network, deterministic=True)
test_fn = theano.function([input_var], T.argmax(test_prediction, axis=1))
predictions = test_fn(test_data)

通過以上步驟，您可以使用Lasagne框架構建卷積神經網絡，并進行訓練和預測。您可以根據自己的需求進行調整和優化模型。