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You just have to set num_classes = 3 which will reduce the number of output classes for both the model output tensor and the separately defined placeholder y.

The number of weights, i.e. parameters, will be adapted accordingly when calling model = AlexNet(....

If you're using gamma, the right equation is actually result = gamma * (x - mean) / sqrt(var) for batch normalization, BUT mean and var are not always the same:

• During training (fit), they are mean_batch and var_batch calculated using the input values of the batch (they are just the mean and variance of your batch)), just as you're doing. In the meanwhile, a global moving_mean and moving_variance are learnt this way: moving_mean = alpha * moving_mean + (1-alpha) * mean_batch, with alpha is a kind of learning rate, in (0,1), usually above 0.9. moving_mean and moving_varianceare approximations of the real mean and variance of all your training data. Gamma is also learnt, by usual gradient descent, to best fit your output.

• During inference (predict), you just use the learnt values of moving_mean and moving_variance, not at all mean_batch and var_batch. You also use the learnt gamma.

So 0.05885344 is just an approximation of the mean of your random input data, 0.91000736 of its variance, and you're using these to normalize your new data [1, 5]. You can easily chack check that [0.79519051, 4.17485714]=gamma * ([1, 5] - moving_mean)/sqrt(moving_var)

edit: alpha is called momentum in keras, if you want to check it.

From the error message, it appears that the model you're using expects to be fed another value (whose node name in the graph is Placeholder_1 and the expected type is a float scalar tensor).

It seems you've customized your model (as opposed to following the article you linked to verbatim). That said, the article shows multiple placeholders that need to be fed, one for the image and another to control dropout. Defined in the article as: