Question

我正在尝试执行我在Yarin Gal的论文“深度学习的不确定性”中发现的贝叶斯神经网络。我在github上找到了这段代码：

import math
from scipy.misc import logsumexp
import numpy as np

from keras.regularizers import l2
from keras import Input
from keras.layers import Dropout
from keras.layers import Dense
from keras import Model

import time


class net:

    def __init__(self, X_train, y_train, n_hidden, n_epochs = 40,
        normalize = False, tau = 1.0, dropout = 0.05):

        """
            Constructor for the class implementing a Bayesian neural network
            trained with the probabilistic back propagation method.
            @param X_train      Matrix with the features for the training data.
            @param y_train      Vector with the target variables for the
                                training data.
            @param n_hidden     Vector with the number of neurons for each
                                hidden layer.
            @param n_epochs     Numer of epochs for which to train the
                                network. The recommended value 40 should be
                                enough.
            @param normalize    Whether to normalize the input features. This
                                is recommended unles the input vector is for
                                example formed by binary features (a
                                fingerprint). In that case we do not recommend
                                to normalize the features.
            @param tau          Tau value used for regularization
            @param dropout      Dropout rate for all the dropout layers in the
                                network.
        """

        # We normalize the training data to have zero mean and unit standard
        # deviation in the training set if necessary

        if normalize:
            self.std_X_train = np.std(X_train, 0)
            self.std_X_train[ self.std_X_train == 0 ] = 1
            self.mean_X_train = np.mean(X_train, 0)
        else:
            self.std_X_train = np.ones(X_train.shape[ 1 ])
            self.mean_X_train = np.zeros(X_train.shape[ 1 ])

        X_train = (X_train - np.full(X_train.shape, self.mean_X_train)) / \
            np.full(X_train.shape, self.std_X_train)

        self.mean_y_train = np.mean(y_train)
        self.std_y_train = np.std(y_train)

        y_train_normalized = (y_train - self.mean_y_train) / self.std_y_train
        y_train_normalized = np.array(y_train_normalized, ndmin = 2).T

        # We construct the network
        N = X_train.shape[0]
        batch_size = 128
        lengthscale = 1e-2
        reg = lengthscale**2 * (1 - dropout) / (2. * N * tau)

        inputs = Input(shape=(X_train.shape[1],))
        inter = Dropout(dropout)(inputs, training=True)
        inter = Dense(n_hidden[0], activation='relu', W_regularizer=l2(reg))(inter)
        for i in range(len(n_hidden) - 1):
            inter = Dropout(dropout)(inter, training=True)
            inter = Dense(n_hidden[i+1], activation='relu', W_regularizer=l2(reg))(inter)
        inter = Dropout(dropout)(inter, training=True)
        outputs = Dense(y_train_normalized.shape[1], W_regularizer=l2(reg))(inter)
        model = Model(inputs, outputs)

        model.compile(loss='mean_squared_error', optimizer='adam')

        # We iterate the learning process
        start_time = time.time()
        model.fit(X_train, y_train_normalized, batch_size=batch_size, nb_epoch=n_epochs, verbose=0)
        self.model = model
        self.tau = tau
        self.running_time = time.time() - start_time

        # We are done!

    def predict(self, X_test, y_test):

        """
            Function for making predictions with the Bayesian neural network.
            @param X_test   The matrix of features for the test data


            @return m       The predictive mean for the test target variables.
            @return v       The predictive variance for the test target
                            variables.
            @return v_noise The estimated variance for the additive noise.
        """

        X_test = np.array(X_test, ndmin = 2)
        y_test = np.array(y_test, ndmin = 2).T

        # We normalize the test set

        X_test = (X_test - np.full(X_test.shape, self.mean_X_train)) / \
            np.full(X_test.shape, self.std_X_train)

        # We compute the predictive mean and variance for the target variables
        # of the test data

        model = self.model
        standard_pred = model.predict(X_test, batch_size=500, verbose=1)
        standard_pred = standard_pred * self.std_y_train + self.mean_y_train
        rmse_standard_pred = np.mean((y_test.squeeze() - standard_pred.squeeze())**2.)**0.5

        T = 10000

        Yt_hat = np.array([model.predict(X_test, batch_size=500, verbose=0) for _ in range(T)])
        Yt_hat = Yt_hat * self.std_y_train + self.mean_y_train
        MC_pred = np.mean(Yt_hat, 0)
        rmse = np.mean((y_test.squeeze() - MC_pred.squeeze())**2.)**0.5

        # We compute the test log-likelihood
        ll = (logsumexp(-0.5 * self.tau * (y_test[None] - Yt_hat)**2., 0) - np.log(T) 
            - 0.5*np.log(2*np.pi) + 0.5*np.log(self.tau))
        test_ll = np.mean(ll)

        # We are done!
        return rmse_standard_pred, rmse, test_ll

我是编程的新手，所以我必须学习Python上的类才能理解代码。但是，当我尝试执行代码时，我的答案就到了，但是它询问了“带有每个隐藏层的神经元数量的向量”，而且我不知道如何创建此向量，这对于代码来说意味着什么。我尝试创建不同的向量，例如 vector = np.array([1, 2, 3])，但真诚的我不知道正确的答案。我仅有的是要素数据和目标数据。我希望你能帮助我。

Answer 1

该语法正确vector = np.array([1, 2, 3])。这就是在python的numpy中定义向量的方法。

一个神经网络可以具有任意数量的隐藏（内部）层。而且每一层都会有一定数量的神经元。

因此在此代码中，vector=np.array([100, 150, 100])表示网络应具有3个隐藏层（因为向量具有3个值），并且从输入到输出，这些隐藏层应具有100、150、100个神经元分别。

隐藏层的神经元数量

1 个答案: