用于基于矩阵的神经网络的数组元组中的 Numpy 数组

【问题标题】：Numpy arrays from tuples of arrays for matrix based neural networks用于基于矩阵的神经网络的数组元组中的 Numpy 数组
【发布时间】：2018-06-09 00:01:55
【问题描述】：

为了在神经网络中实现学习，我使用了随机梯度下降，其中小批量通过以下列表理解表示：

mini_batches = [training_data[j:j+mini_batch_size] for j in range(0,len(training_data),mini_batch_size)]

在列表理解中，sn-ps mini_batch_size 和 training_data 源于随机梯度下降法的输入，该方法是神经网络类的一部分。

training_data 由数组元组(x,y) 组成，其中第一个数组x 包含输入数据，第二个数组y 包含输出数据（分类）。数组x 的形状为(784,1)，数组y 的形状为(10,1)。长度len(training_data) 输出50,000。

我想构造两个 Numpy 数组，其中第一个数组是输入数据的矩阵，第二个数组是输出数据的矩阵。这两个阵列将用于学习算法的基于矩阵的方法中。但是，我不确定数组结构的简单实现。非常感谢您在这个方向上的帮助。

mini_batch_size 是一个整数，它指定training_data 的分区方式。切片后的训练数据，即training_data[0]，格式如下（元组的前半部分包含从0到1的784个浮点数，后半部分包含10个浮点数）：

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数据来自 MNIST 训练集。请注意，training_data = list(training_data) 是在最初加载数据时执行的——稍后会创建网络类。如果不使用list(foo)操作，则数据显示如下：<zip at 0x1d744027948>。

【问题讨论】：

标签： python arrays python-3.x numpy neural-network


【解决方案1】：

这是一个解决方案，为输入数据和目标数据提供一个数组：

input_data_array = np.asarray([input_data.ravel() for input_data, target_data in mini_batch]).T
target_data_array = np.asarray([target_data.ravel() for input_data, target_data in mini_batch]).T

代码使用来自 Numpy 的 ravel 和 asarray 的组合。

参考：training_data 和 mnist_loader 模块：

import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
training_data = list(training_data)

至于动机，矩阵形式的反向传播更快。基本的反向传播方程如下所示（来源：Michael Nielsen 的在线文本：Neural networks and deep learning）。内部带点的圆圈是 Hadamard 或 Schur 产品的符号：

【讨论】：

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