binary_crossentropy loss it isn' the only viable choiceyou could usefor instancemean_squared_error but crossentropy is usually the best choice when you're dealing with models that output probabilities crossentropy is quantity from the field of information theory that measures the distance between probability distributions orin this casebetween the ground-truth distribution and your predictions here' the step where you configure the model with the rmsprop optimizer and the binary_crossentropy loss function note that you'll also monitor accuracy during training listing compiling he model model compile(optimizer='rmsprop'loss='binary_crossentropy'metrics=['accuracy']you're passing your optimizerloss functionand metrics as stringswhich is possible because rmspropbinary_crossentropyand accuracy are packaged as part of keras sometimes you may want to configure the parameters of your optimizer or pass custom loss function or metric function the former can be done by passing an optimizer class instance as the optimizer argumentas shown in listing the latter can be done by passing function objects as the loss and/or metrics argumentsas shown in listing listing configuring the opt imizer from keras import optimizers model compile(optimizer=optimizers rmsprop(lr= )loss='binary_crossentropy'metrics=['accuracy']listing using cust om losses and met rics from keras import losses from keras import metrics model compile(optimizer=optimizers rmsprop(lr= )loss=losses binary_crossentropymetrics=[metrics binary_accuracy]validating your approach in order to monitor during training the accuracy of the model on data it has never seen beforeyou'll create validation set by setting apart , samples from the original training data listing set ing aside validat ion set x_val x_train[: partial_x_train x_train[ :licensed to