Why is the x variable tensor reshaped with -1 in the MNIST tutorial for tensorflow?

1) What does -1 mean in the reshaping vector

From the documentation of reshape:

If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant. In particular, a shape of [-1] flattens into 1-D. At most one component of shape can be -1.

this is a standard feature and is available in numpy as well. Basically it means - I do not have time to calculate all the dimensions, so infer the one for me. In your case because x * 28 * 28 * 1 = 784 so your -1 = 1

2) Why is x being reshaped

They are planning to use convolution for image classification. So they need to use some spatial information. Current data is 1 dimensional. So they transform it to 4 dimensions. I do not know the point of the forth dimension because in my opinion they might have used only (x, y, color). Or even (x, y). Try to modify their reshape and convolution and most probably you will get similar accuracy.

why 4 dimensions

TensorFlow’s convolutional conv2d operation expects a 4-dimensional tensor with dimensions corresponding to batch, width, height and channel.

[batch, in_height, in_width, in_channels]