Initialize the loss

The loss is binary cross_entropy.

Cross-entropy loss, also called log loss, measures the performance of a model (classification model). The output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual value:  .

self.loss = loss or []

Initialize all internal variables for output:

 self._feed_outputs = []
 self._feed_output_names = []
 self._feed_output_shapes = []
 self._feed_loss_fns = []

Prepare the targets of the model:

self._feed_targets.append(target)
 self._feed_outputs.append(self.outputs[i])
 self._feed_output_names.append(name)
 self._feed_output_shapes.append(shape)
 self._feed_loss_fns.append(self.loss_functions[i])

Prepare sample weights:

Before compilation, the following values are assigned to sample weights and sample_weight_modes:

sample_weights = []
sample_weight_modes = []

After running through the code execution, it gets initialized to the following values:

Tensor("dense_3_sample_weights:0", shape=(?,), dtype=float32)

Prepare the metrics:

Next, we prepare metric names and metrics_tensors, which store the actual metrics:

self.metrics_names = ['loss']
self.metrics_tensors = []

Prepare total loss and metrics:

The loss is calculated and appended to self.metrics_tensors:

output_loss = weighted_loss(y_true, y_pred,
 sample_weight, mask)
...
self.metrics_tensors.append(output_loss)
self.metrics_names.append(self.output_names[i] + '_loss')

Next, we calculate nested metrics and nested_weighted_metrics:

nested_metrics = collect_metrics(metrics, self.output_names)
nested_weighted_metrics = collect_metrics(weighted_metrics, self.output_names)

Initialize the test, train, and predict functions:

These are initialized lazily:

self.train_function = None
self.test_function = None
self.predict_function = None

Sort trainable weights:

In the end, we initialize the trainable weights:

trainable_weights = self.trainable_weights
self._collected_trainable_weights = trainable_weights