I am having a really hard time understanding 3D arrays. I can’t wrap my head around this image:
I can understand 2D arrays, but not this. And then we have the training set that says
Remember that train_set_x_orig is a numpy-array of shape (m_train, num_px, num_px, 3).
So the shape is (209, 64, 64, 3). This means it’s 209 rows of arrays that are 64x64? I just can’t, I am trying to understand this all day and I just can’t grasp it.
train_set_x_orig.reshape(train_set_x_orig.shape[0], -1).T
Also, this line doesn’t make sense to me. The shape of our train_set_x_orig is (209, 64, 64, 3). So we are reshaping this to have 209 rows, -1? What’s -1?
I feel a bit lost.
Yes, it’s pretty tricky to make the jump from 2D arrays to 4D arrays. You can take it one step at a time:
For a single image, it is a 3D array. In our case the images are 64 x 64 x 3, which means you can think of it as 64 x 64 pixels and at each location, you have 3 color values RGB that give you the exact color of the pixel at that location. So it’s 64 x 64 positions with 3 values at each point. Or think of it as three layers stacked behind each other that are 64 x 64: the red picture, the green picture and the blue picture.
Now when you take the next step up and handle multiple images at in a batch, what we do is add the first dimension for the “samples”. The number of samples m = 209 in this case, so think of it as 209 images in a list, each of which has 64 x 64 pixels and at each pixel location you have 3 color values. So it’s 209 x 64 x 64 x 3.
Now when we “unroll” or “flatten” the 4D array into a 2D array so that we can feed it to our neural network, we need to be careful how we do that. Here’s a thread which explains that process in detail.
So if I understood this correctly, this is my training set? Test set is the same with fewer examples.
Yes, the test set has different images, but they are the same size and shape. There are 50 images in the test set and 209 in the training set.
