要安裝Lasagne框架，首先需要確保你的python環境已經安裝了pip包管理器。然后，你可以使用以下命令來安裝Lasagne：

pip install Lasagne

安裝完成后，你就可以在python腳本中使用Lasagne框架了。以下是一個使用Lasagne框架構建卷積神經網絡的例子：

import lasagne
from lasagne.layers import InputLayer, DenseLayer, Conv2DLayer, MaxPool2DLayer, FlattenLayer

# 創建神經網絡模型
def build_model(input_shape, num_classes):
    net = {}
    net['input'] = InputLayer(input_shape)
    net['conv1'] = Conv2DLayer(net['input'], num_filters=32, filter_size=(5, 5))
    net['pool1'] = MaxPool2DLayer(net['conv1'], pool_size=(2, 2))
    net['conv2'] = Conv2DLayer(net['pool1'], num_filters=64, filter_size=(3, 3))
    net['pool2'] = MaxPool2DLayer(net['conv2'], pool_size=(2, 2))
    net['flatten'] = FlattenLayer(net['pool2'])
    net['output'] = DenseLayer(net['flatten'], num_units=num_classes, nonlinearity=lasagne.nonlinearities.softmax)
    return net

# 使用模型進行訓練和預測
def train_model(model, X_train, y_train, X_val, y_val):
    # 編譯模型
    input_var = model['input'].input_var
    target_var = T.ivector('targets')
    prediction = lasagne.layers.get_output(model['output'])
    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)
    loss = loss.mean()
    params = lasagne.layers.get_all_params(model['output'], trainable=True)
    updates = lasagne.updates.nesterov_momentum(loss, params, learning_rate=0.01, momentum=0.9)
    train_fn = theano.function([input_var, target_var], loss, updates=updates)
    
    # 訓練模型
    num_epochs = 10
    batch_size = 32
    for epoch in range(num_epochs):
        for batch in iterate_minibatches(X_train, y_train, batch_size):
            inputs, targets = batch
            train_fn(inputs, targets)
        
        # 在驗證集上進行評估
        val_acc = evaluate_model(model, X_val, y_val)
        print("Epoch {}, validation accuracy: {}".format(epoch, val_acc))
    
    return model

# 評估模型在驗證集上的準確率
def evaluate_model(model, X_val, y_val):
    input_var = model['input'].input_var
    target_var = T.ivector('targets')
    test_prediction = lasagne.layers.get_output(model['output'], deterministic=True)
    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction, target_var)
    test_loss = test_loss.mean()
    test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var), dtype=theano.config.floatX)
    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])
    
    val_loss, val_acc = val_fn(X_val, y_val)
    return val_acc

# 定義輔助函數：生成小批量樣本
def iterate_minibatches(inputs, targets, batchsize):
    assert len(inputs) == len(targets)
    indices = np.arange(len(inputs))
    np.random.shuffle(indices)
    for start_idx in range(0, len(inputs) - batchsize + 1, batchsize):
        excerpt = indices[start_idx:start_idx + batchsize]
        yield inputs[excerpt], targets[excerpt]

# 示例：構建模型并訓練
input_shape = (None, 1, 28, 28)
num_classes = 10
model = build_model(input_shape, num_classes)
trained_model = train_model(model, X_train, y_train, X_val, y_val)

這只是一個簡單的例子，你可以根據自己的需求和數據進行模型設計和訓練。在使用Lasagne框架時，你可以參考官方文檔以獲取更多的信息和示例：https://lasagne.readthedocs.io/