使用鸢尾花数据集写含三层rbm的dbn代码

好的，以下是使用鸢尾花数据集构建含三层RBM的DBN的Python代码：

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import StandardScaler
from sklearn.utils import shuffle


class RBM:
    def __init__(self, num_visible, num_hidden, learning_rate=0.1, momentum=0.9, k=1):
        self.num_visible = num_visible
        self.num_hidden = num_hidden
        self.learning_rate = learning_rate
        self.momentum = momentum
        self.k = k
        self.weights = np.random.normal(scale=0.01, size=(num_visible, num_hidden))
        self.visible_bias = np.zeros(num_visible)
        self.hidden_bias = np.zeros(num_hidden)
        self.delta_weights = np.zeros_like(self.weights)
        self.delta_visible_bias = np.zeros_like(self.visible_bias)
        self.delta_hidden_bias = np.zeros_like(self.hidden_bias)

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def contrastive_divergence(self, x):
        # Positive phase
        hidden_prob = self.sigmoid(np.dot(x, self.weights) + self.hidden_bias)
        hidden_state = np.random.binomial(1, hidden_prob)
        positive_grad = np.dot(x.T, hidden_prob)

        # Negative phase
        visible_prob = self.sigmoid(np.dot(hidden_state, self.weights.T) + self.visible_bias)
        for i in range(self.k):
            hidden_prob = self.sigmoid(np.dot(visible_prob, self.weights) + self.hidden_bias)
            hidden_state = np.random.binomial(1, hidden_prob)
            visible_prob = self.sigmoid(np.dot(hidden_state, self.weights.T) + self.visible_bias)
        negative_grad = np.dot(visible_prob.T, hidden_prob)

        # Update weights and biases
        self.delta_weights = self.learning_rate * (positive_grad - negative_grad) / x.shape[0] + self.momentum * self.delta_weights
        self.weights += self.delta_weights
        self.delta_visible_bias = self.learning_rate * np.mean(x - visible_prob, axis=0) + self.momentum * self.delta_visible_bias
        self.visible_bias += self.delta_visible_bias
        self.delta_hidden_bias = self.learning_rate * np.mean(hidden_prob - hidden_state, axis=0) + self.momentum * self.delta_hidden_bias
        self.hidden_bias += self.delta_hidden_bias

    def transform(self, x):
        return self.sigmoid(np.dot(x, self.weights) + self.hidden_bias)


class DBN:
    def __init__(self, layers):
        self.layers = layers
        self.rbms = [RBM(layers[i], layers[i+1]) for i in range(len(layers)-1)]

    def pretrain(self, x, batch_size=32, epochs=100):
        for rbm in self.rbms:
            for epoch in range(epochs):
                x = shuffle(x)
                for i in range(0, x.shape[0], batch_size):
                    batch = x[i:i+batch_size]
                    rbm.contrastive_divergence(batch)
                print("Pretraining layer with shape {} epoch {} complete".format(rbm.weights.shape, epoch+1))
                x = rbm.transform(x)

    def finetune(self, x, y, batch_size=32, epochs=100, lr=0.1):
        scaler = StandardScaler()
        x = scaler.fit_transform(x)
        x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)
        for epoch in range(epochs):
            x_train = shuffle(x_train)
            for i in range(0, x_train.shape[0], batch_size):
                batch_x = x_train[i:i+batch_size]
                batch_y = y_train[i:i+batch_size]
                activations = self.feedforward(batch_x)
                activations[-1] = np.exp(activations[-1]) / np.sum(np.exp(activations[-1]), axis=1, keepdims=True)
                output_error = activations[-1] - batch_y
                delta = output_error
                for j in range(len(self.layers)-2, -1, -1):
                    delta = np.dot(delta, self.rbms[j+1].weights.T) * activations[j] * (1 - activations[j])
                    self.rbms[j].delta_weights = lr * np.dot(activations[j].T, delta) / batch_x.shape[0]
                    self.rbms[j].weights += self.rbms[j].delta_weights
                    self.rbms[j].delta_hidden_bias = lr * np.mean(delta, axis=0)
                    self.rbms[j].hidden_bias += self.rbms[j].delta_hidden_bias
            y_pred = self.predict(x_test)
            acc = accuracy_score(y_test, y_pred)
            print("Epoch {} complete, accuracy: {}".format(epoch+1, acc))

    def feedforward(self, x):
        activations = []
        for rbm in self.rbms:
            x = rbm.transform(x)
            activations.append(x)
        return activations

    def predict(self, x):
        activations = self.feedforward(x)
        activations[-1] = np.exp(activations[-1]) / np.sum(np.exp(activations[-1]), axis=1, keepdims=True)
        return np.argmax(activations[-1], axis=1)


if __name__ == '__main__':
    iris = load_iris()
    x = iris.data
    y = iris.target
    np.random.seed(42)
    dbn = DBN([x.shape[1], 64, 32, 3])
    dbn.pretrain(x)
    dbn.finetune(x, np.eye(3)[y])

这个代码使用了三层RBM，第一层有4个可见节点，第二层有64个隐藏节点，第三层有32个隐藏节点，最后一层有3个输出节点，对应鸢尾花数据集的三个类别。在预训练阶段，我们使用随机梯度下降对每层的RBM进行训练，每次迭代都会对整个数据集进行一次随机洗牌，并将数据分成大小为32的小批量进行训练。在微调阶段，我们使用标准缩放对输入数据进行预处理，将数据分成训练集和测试集，使用交叉熵损失函数和带动量的随机梯度下降对整个网络进行训练。