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I'm a beginner in Keras. I've loaded MNIST dataset in Keras and checked it's dimension. The code is

from keras.datasets import mnist

# load data into train and test sets
(X_train, y_train), (X_test, y_test) = mnist.load_data()

print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
print("Shape: ", X_train[0].shape)

And the output is

(60000, 28, 28, 1)
(60000, 10, 2, 2, 2, 2)
(10000, 28, 28, 1)
(10000, 10, 2, 2)
Shape:  (28, 28, 1)

As X_train and X_test are already in the shape (#sample, width, height, #channel). Do we still need reshaping? Why? The tutorial I'm following use the following reshaping code:

X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')

My second question is that why is .astype('float32') is used in code?

Lastly, I could not understand the output of print(y_train.shape) and print(y_test.shape).

Please suggest. I've already read Reshaping of data for deep learning using Keras however still my doubts are unclear.

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Answer 1 The reason for reshaping is to ensure that the input data to the model is in the correct shape. But you can say it using reshape is a replication of effort.

Answer 2 The reason for converting to float so that later we could normalize image between the range of 0-1 without loss of information.

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  • $\begingroup$ 1. So, we can skip this step and just convert to float like X_train = X_train.astype('float32')? What do you say? $\endgroup$
    – Dr Nisha Arora
    Sep 13 '19 at 8:59
  • $\begingroup$ 2. If I'm not wrong, it means that if we don't convert integers to float there & then normalize it (or divide by 255), the resulting values would be coerced to be integer values so we may lose information. Please verify. $\endgroup$
    – Dr Nisha Arora
    Sep 13 '19 at 9:02
  • $\begingroup$ You can only omit 1 only when you intend to omit 2 as well. The only problem you would could face while omitting 1 and 2 would be slow convergence or maybe in some cases you would not converge. $\endgroup$
    – Syed Nauyan Rashid
    Sep 13 '19 at 10:25
  • $\begingroup$ Yes, that what I meant. As mentioned I just want to omit to reshape as it was redundant and instead using X_train = X_train.astype('float32') and X_test = X_test.astype('float32'). However, after re-running the same notebook this time I'm getting the shapes as mentioned by @from keras import michael. It's surpring for me too but those shape I was expecting earlier too, so finally I need reshaping as mentioned in the question. $\endgroup$
    – Dr Nisha Arora
    Sep 13 '19 at 10:33
  • $\begingroup$ Now, my concer is that datascience.stackexchange.com/questions/11704/… mentions the desired shape to be (nb_samples, nb_channels, width, height) and the course, I'm following is doing it like (nb_samples, width, height,nb_channels). Why is it different? $\endgroup$
    – Dr Nisha Arora
    Sep 13 '19 at 10:35
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I do not get those shapes. Using your code, I get:

(60000, 28, 28) (60000,) (10000, 28, 28) (10000,) Shape: (28, 28)

which makes more sense. For example, your output should not have 6 tensor dimensions: "(60000, 10, 2, 2, 2, 2)".

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  • $\begingroup$ Does it depend on version? I'm using keras 2.2.5; which one is yours? $\endgroup$
    – Dr Nisha Arora
    Sep 13 '19 at 8:57
  • $\begingroup$ I was also surprised to see 6 tensor dimensions like "(60000, 10, 2, 2, 2, 2)". What is more surprising is that without making any change, I've just re-run the script & this time I got the same shapes as that of yours [which was expected result earlier too. Anyways, thanks. $\endgroup$
    – Dr Nisha Arora
    Sep 13 '19 at 9:29

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