What does the output of model.predict function from Keras mean?

The output of a neural network will never, by default, be binary - i.e. zeros or ones. The network works with continuous values (not discrete) in order to optimise the loss more freely in the framework of gradient descent.

Have a look here at a similar question that also shows some code.

Without any kind of tweaking and scaling, the output of your network is likely to fall somewhere in the range of your input, in terms of its nominal value. In your case, that seems to be roughly between 0 and 2.

You could now write a function that turns your values above into 0 or 1, based on some threshold. For example, scale the values to be in the range [0, 1], then if the value is below 0.5, return 0, if above 0.5, return 1.

If this is a classification problem you should change your network to have 2 output neurons.

You can convert labels to one-hot encoded vectors using

y_train_binary = keras.utils.to_categorical(y_train, num_classes) y_test_binary = keras.utils.to_categorical(y_test, num_classes) 

Then make sure that your output layer has two neurons with a softmax activation function.

model.add(Dense(num_classes, activation='softmax')) 

This will result in your model.predict(x_test_reshaped) to be an array of lists. Where the inner list is the probability of an instance belonging to each class. This will add up to 1 and evidently the decided label should be the output neuron with the highest probability.

Keras has this included in their library so you don't need to do this comparison yourself. You can get the class label directly by using model.predict_classes(x_test_reshaped).

The predictions are based on what you feed in as training outputs and the activation function.

For example, with 0-1 input and a sigmoid activation function for the output with a binary crossentropy loss, you would get the probability of a 1. Depending on the cost of getting the decision wrong in either direction you can then decide on how you deal with these probabilities (e.g. predict category "1", if the probability is >0.5 or perhaps already when it's >0.1).

From what you describe, you had 0-1 input and presumably assumed a linear activation for your output layer (perhaps with a mean-squared-error loss?). This means, you assumed a regression problem, where the output is directly a predicted number (that can be any real number from in $(-\infty, \infty$). I'd assume that was not what you intended and you might want what I mentioned in the first paragraph instead.

I faced the same problem and solved as:

seq_predictions=model.predict(test_sequences) print('Outputs shape') print(seq_predictions.shape) # prints (n,1) but need (n,) seq_predictions=np.transpose(seq_predictions)[0] # transformation to get (n,) print(seq_predictions.shape) # now the shape is (n,) # Applying transformation to get binary values predictions with 0.5 as thresold seq_predictions = list(map(lambda x: 0 if x<0.5 else 1, seq_predictions))