def randInitializeWeights(L_in, L_out): #初始化权重矩阵 """ randomly initializes the weights of a layer with L_in incoming connections and L_out outgoing connections. """ epi = (6**1/2) / (L_in + L_out)**1/2 W = np.random.rand(L_out,L_in +1) *(2*epi) -epi return W

这段代码实现了一个权重矩阵的随机初始化函数，其中L_in表示输入层的神经元数量，L_out表示输出层的神经元数量。初始化权重矩阵可以使神经网络更好地学习输入数据的特征。

具体来说，该函数首先计算了一个被称为"epsilon"的值，它是一个常量，用于控制权重矩阵中元素的范围。然后，使用Numpy库的random.rand函数生成一个大小为(L_out, L_in+1)的随机矩阵，每个元素的值都在[-epsilon, epsilon]之间。最后返回该权重矩阵W。值得注意的是，由于偏置项也需要被训练，因此在输入层上增加了一个偏置单元，因此矩阵的列数为L_in+1。

相关问题

def monty_choice(car, contestant): # this method simulates Monty opening an door behind which there is a goat # Parameters # car, which is either 1, 2 or 3, represents the door behind which is a car # contestant, which is either 1, 2 or 3, represents the door that contestant chooses # Returns # an integer, which is 1,2 or 3, represents the door that host chooses to open # write your code below this line

Here's one possible implementation of the `monty_choice` function in Python:

def monty_choice(car, contestant):
    # find the door that Monty can open
    options = [1, 2, 3]
    options.remove(car)
    if contestant in options:
        options.remove(contestant)
    door_to_open = random.choice(options)
    return door_to_open

This function takes in two parameters: `car` and `contestant`, which represent the door behind which is a car and the door that the contestant chooses, respectively. It returns an integer, which represents the door that Monty chooses to open.

The function first creates a list of all possible doors (`options`) and removes the door containing the car. If the contestant has not already chosen the car door, it also removes the contestant's chosen door. The function then randomly selects one of the remaining doors to open (`door_to_open`) and returns it.

x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.1, random_state = 999) # Reshape image in 3 dimensions (height = 100, width = 125 , canal = 3) x_train = x_train.reshape(x_train.shape[0], *(100, 125, 3)) x_test = x_test.reshape(x_test.shape[0], *(100, 125, 3)) x_validate = x_validate.reshape(x_validate.shape[0], *(100, 125, 3)) # With data augmentation to prevent overfitting datagen = ImageDataGenerator( featurewise_center=False, # set input mean to 0 over the dataset samplewise_center=False, # set each sample mean to 0 featurewise_std_normalization=False, # divide inputs by std of the dataset samplewise_std_normalization=False, # divide each input by its std zca_whitening=False, # apply ZCA whitening rotation_range=10, # randomly rotate images in the range (degrees, 0 to 180) zoom_range = 0.1, # Randomly zoom image width_shift_range=0.12, # randomly shift images horizontally (fraction of total width) height_shift_range=0.12, # randomly shift images vertically (fraction of total height) horizontal_flip=True, # randomly flip images vertical_flip=True) # randomly flip images datagen.fit(x_train)代码段一开始没有问题，但是多次执行后出现报错MemoryError: Unable to allocate 1.52 GiB for an array with shape (5423, 100, 125, 3) and data type float64

这个错误提示表明在程序执行过程中发生了内存不足的错误，无法为一个形状为(5423, 100, 125, 3)、数据类型为float64的数组分配1.52 GiB的内存空间。这通常是因为程序在多次执行后，内存占用逐渐增加，导致内存不足而出现的。有以下几种方法可以尝试解决这个问题：

1. 增加计算机的物理内存：可以增加计算机的物理内存，以提供更多的内存空间给程序使用。

2. 减少数据集大小：可以减少数据集的大小，以减少程序所需的内存空间。

3. 减少图像尺寸：可以尝试减小图像的尺寸，以减少图像所需的内存空间。

4. 减少数据增强的程度：可以减少数据增强的程度，以降低程序所需的内存空间。

5. 释放不再使用的内存：可以在程序执行过程中，释放不再使用的内存，以提高内存使用效率。可以使用Python内置的`gc`模块手动触发垃圾回收机制，回收不再使用的内存。