X_train= X_train.reshape(X_train.shape[0],28*28)#需要reshape之后才可以放入CNN分类器中,转化成原来的28x28
时间: 2024-04-21 22:23:49 浏览: 32
对的,您在这段代码中使用了 `reshape` 函数来将训练集数据 `X_train` 的形状从原来的二维形状 `(样本数, 28, 28)` 转换为一维形状 `(样本数, 28*28)`。这是因为在将数据输入到卷积神经网络 (CNN) 分类器之前,需要将输入数据的形状调整为适合网络输入的形式。
通过调用 `reshape` 函数,您可以指定新的形状 `(样本数, 28*28)`,其中 `样本数` 是训练集数据的数量。这样一来,每个样本都被转换为一个长度为 `28*28` 的一维向量,以便能够作为输入传递给 CNN 分类器。
请注意,这里的 `reshape` 操作只是将数据的形状进行变换,并不改变数据本身。这个操作是非常常见的,在将数据输入到 CNN 或其他模型之前,通常需要将数据的形状调整为适合模型输入的形式。
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x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
这是将 x_train 数组的形状从 (n, 784) 转换为 (n, 28, 28, 1) 的代码,其中 n 是样本数。这个操作可以将每个样本的像素值重新排列成一个 28x28 的矩阵,并添加一个额外的维度,以便于在卷积神经网络中使用。
x_train = x_train.reshape((x_train.shape[0], x_train.shape[1], 1))
This line of code is reshaping the x_train dataset to have an additional dimension of size 1. It is being done using the reshape() method of the numpy library.
The first argument to reshape() specifies the new shape of the array, which is being set to (x_train.shape[0], x_train.shape[1], 1). This means that the new array will have the same number of rows as the original x_train array, the same number of columns as the original x_train array, and an additional dimension of size 1.
The second argument to reshape() is optional and specifies the order in which the elements of the original array should be arranged in the new array. In this case, it is not specified so the default value 'C' is used, which means that the elements will be arranged in row-major order.