Quoted from evaluate() method documentation:
Returns
Scalar test loss (if the model has a single output and no metrics) or
list of scalars (if the model has multiple outputs and/or metrics).
The attribute model.metrics_names will give you the display labels
for the scalar outputs.
Therefore, you can use metrics_names property of your model to find out what each of those values corresponds to. For example:
from keras import layers
from keras import models
import numpy as np
input_data = layers.Input(shape=(100,))
out_1 = layers.Dense(1)(input_data)
out_2 = layers.Dense(1)(input_data)
model = models.Model(input_data, [out_1, out_2])
model.compile(loss='mse', optimizer='adam', metrics=['mae'])
print(model.metrics_names)
outputs the following:
['loss', 'dense_1_loss', 'dense_2_loss', 'dense_1_mean_absolute_error', 'dense_2_mean_absolute_error']
which indicates what each of those numbers you see in the output of evaluate method corresponds to.
Further, if you have many layers then those dense_1 and dense_2 names might be a bit ambiguous. To resolve this ambiguity, you can assign names to your layers using name argument of layers (not necessarily on all of them but only on the input and output layers):
# ...
out_1 = layers.Dense(1, name='output_1')(input_data)
out_2 = layers.Dense(1, name='output_2')(input_data)
# ...
print(model.metrics_names)
which outputs a more clear description:
['loss', 'output_1_loss', 'output_2_loss', 'output_1_mean_absolute_error', 'output_2_mean_absolute_error']
