How to properly use numpy in keras
Here is an explanation on how you can access your images.
X is four dimensional tensor. In mathematics tensors are generalization of vectors and metrics into higher dimensional arrays.
Assuming "channels last" data-format

1st Axis = Number of images 
2nd Axis = Number of rows in single image
3rd Axis = Number of columns in single row 
4th Axis = Number of channels of certain pixel  

Now you can access image,row,column, and channels using indexing as follows. 

x[0] Represents first image  
x[0][0] Represents First row of first image  
x[0][0][0] Represents First column of first row of first image  
x[0][0][0][0] Represents Red channel of First column of first row of first image
the question is:
In the keras tutorial it use an input x_train = np.random.random((100, 100, 100, 3)), it should means that there's 100 images each has size of [100,100,3] right?
So i thought that x_train[0][0] should represent the first channel of the first img (which should be [100, 100]), but x_train[0][0] in fact has a size of [100,3]... so i'm confused, how can keras take this [100,100,100,3] numpy array as a set of imgs? please help me out, thank in advance.
Another question is:
how can I construct a input like this ? Cause when I do np.array([[100,100],[100,100]]), it becomes to array of [2,100,100]