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The shape of a tensor indicates the number of values in each dimension:
         i) The shape of the tensor of a scalar, such as 67, is [ ].
         ii) The shape of the first rank tensor of a vector, such as [1, 2, 3], is [3].
         iii) The second rank tensor would have a shape of [3, 3], for instance, for the matrix below:
             Matrix =
                           10 20 34
                           43 54 65
                           57 68 79

Two conventions for shapes of images tensors are:
         i) Channels-last convention (used by TensorFlow),
         ii) Channels-first convention (used by Theano).

The TensorFlow machine-learning framework places the color-depth axis at the end: (samples, height, width, color_depth). Theano places the color depth axis right after the batch axis: (samples, color_depth, height, width). The Keras framework provides support for both formats.

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