用Lasagne包实现DBN分类：Python代码示例

以下是使用Lasagne包实现DBN分类的Python代码示例：

import numpy as np
import theano
import theano.tensor as T
import lasagne
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split

# 加载数据集
X, y = fetch_openml('mnist_784', version=1, return_X_y=True, as_frame=False)
X = X / 255.0  # 将像素值缩放到0-1之间
y = y.astype(np.int32)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 定义DBN模型
class DBN(object):
    def __init__(self, num_features, hidden_units, num_classes):
        self.num_features = num_features
        self.hidden_units = hidden_units
        self.num_classes = num_classes
        self.sigmoid_layers = []
        self.rbm_layers = []
        self.params = []

        # 定义RBM层
        for i, output_size in enumerate(hidden_units):
            if i == 0:
                input_size = num_features
            else:
                input_size = hidden_units[i-1]

            rbm_layer = RBM(input_size, output_size)
            self.rbm_layers.append(rbm_layer)
            self.params.extend(rbm_layer.params)

            sigmoid_layer = lasagne.layers.DenseLayer(
                lasagne.layers.InputLayer((None, input_size)),
                num_units=output_size,
                nonlinearity=lasagne.nonlinearities.sigmoid
            )
            self.sigmoid_layers.append(sigmoid_layer)

        # 定义输出层
        self.output_layer = lasagne.layers.DenseLayer(
            lasagne.layers.InputLayer((None, hidden_units[-1])), 
            num_units=num_classes,
            nonlinearity=lasagne.nonlinearities.softmax
        )
        self.params.extend(self.output_layer.params)

    def pretrain(self, X_train, batch_size=10, learning_rate=0.1, num_epochs=10):
        index = T.lscalar('index')
        x = T.matrix('x')

        for rbm in self.rbm_layers:
            cost, updates = rbm.get_cost_updates(x, learning_rate)
            train_rbm = theano.function(
                inputs=[index],
                outputs=cost,
                updates=updates,
                givens={
                    x: X_train[index * batch_size: (index + 1) * batch_size]
                }
            )

            # 进行RBM预训练
            num_batches = X_train.shape[0] // batch_size
            for epoch in range(num_epochs):
                for batch_index in range(num_batches):
                    train_rbm(batch_index)

    def finetune(self, X_train, y_train, batch_size=10, learning_rate=0.1, num_epochs=10):
        index = T.lscalar('index')
        x = T.matrix('x')
        y = T.ivector('y')

        # 定义输出层的代价函数和更新规则
        prediction = lasagne.layers.get_output(self.output_layer, x)
        cost = lasagne.objectives.categorical_crossentropy(prediction, y)
        cost = cost.mean()
        updates = lasagne.updates.nesterov_momentum(cost, self.params, learning_rate)

        # 定义训练函数
        train_fn = theano.function(
            inputs=[index],
            outputs=cost,
            updates=updates,
            givens={
                x: X_train[index * batch_size: (index + 1) * batch_size],
                y: y_train[index * batch_size: (index + 1) * batch_size]
            }
        )

        # 进行DBN微调
        num_batches = X_train.shape[0] // batch_size
        for epoch in range(num_epochs):
            for batch_index in range(num_batches):
                train_fn(batch_index)

    def predict(self, X_test):
        x = T.matrix('x')
        prediction = lasagne.layers.get_output(self.output_layer, x, deterministic=True)
        predict_fn = theano.function([x], T.argmax(prediction, axis=1))
        return predict_fn(X_test)

# 定义RBM层
class RBM(object):
    def __init__(self, input_size, output_size):
        self.input_size = input_size
        self.output_size = output_size
        self.W = theano.shared(
            value=np.zeros((input_size, output_size), dtype=theano.config.floatX),
            name='W',
            borrow=True
        )
        self.b_h = theano.shared(
            value=np.zeros((output_size,), dtype=theano.config.floatX),
            name='b_h',
            borrow=True
        )
        self.b_v = theano.shared(
            value=np.zeros((input_size,), dtype=theano.config.floatX),
            name='b_v',
            borrow=True
        )
        self.params = [self.W, self.b_h, self.b_v]

    def propup(self, x):
        return T.nnet.sigmoid(T.dot(x, self.W) + self.b_h)

    def propdown(self, x):
        return T.nnet.sigmoid(T.dot(x, self.W.T) + self.b_v)

    def sample_h_given_v(self, v0_sample):
        h1_mean = self.propup(v0_sample)
        h1_sample = self.theano_rng.binomial(size=h1_mean.shape, n=1, p=h1_mean, dtype=theano.config.floatX)
        return h1_mean, h1_sample

    def sample_v_given_h(self, h0_sample):
        v1_mean = self.propdown(h0_sample)
        v1_sample = self.theano_rng.binomial(size=v1_mean.shape, n=1, p=v1_mean, dtype=theano.config.floatX)
        return v1_mean, v1_sample

    def get_cost_updates(self, x, learning_rate):
        h0_mean, h0_sample = self.sample_h_given_v(x)
        v1_mean, v1_sample = self.sample_v_given_h(h0_sample)
        h1_mean, h1_sample = self.sample_h_given_v(v1_sample)

        # 计算代价函数
        cost = T.mean(self.free_energy(x)) - T.mean(self.free_energy(v1_sample))

        # 计算参数更新规则
        gparams = T.grad(cost, self.params, consider_constant=[v1_sample])
        updates = [
            (param, param - learning_rate * gparam) for param, gparam in zip(self.params, gparams)
        ]

        return cost, updates

    def free_energy(self, v_sample):
        wx_b = T.dot(v_sample, self.W) + self.b_h
        vbias_term = T.dot(v_sample, self.b_v)
        hidden_term = T.sum(T.log(1 + T.exp(wx_b)), axis=1)
        return -hidden_term - vbias_term

# 定义DBN分类器
num_features = X.shape[1]
hidden_units = [500, 500]  # 隐藏层单元数
num_classes = len(np.unique(y))
dbn = DBN(num_features, hidden_units, num_classes)

# 进行预训练
dbn.pretrain(X_train)

# 进行微调
dbn.finetune(X_train, y_train)

# 进行预测
y_pred = dbn.predict(X_test)

# 计算准确率
accuracy = np.mean(y_pred == y_test)
print('Accuracy:', accuracy)

请注意，这只是一个简单的示例代码，可能需要根据具体情况进行修改和优化。此外，这个示例中使用的是MNIST数据集，如果要使用其他数据集，请根据数据集的特点进行相应的调整。