def evaluate(self, x, y, verbose=2):
metrics_value = self.model.evaluate(x=x, y=y, verbose=verbose)
metrics_names = self.model.metrics_names
if isiterable(metrics_value):
return list(zip(metrics_names, metrics_value))
else:
return metrics_names[0], metrics_value
def evaluate(self, x, y, n_cls, verbose=2):
in_1, in_2, out = self._get_batch(x, y, n_cls, 128 * 100)
metrics_value = self.model.evaluate([in_1, in_2], out, verbose=verbose)
metrics_names = self.model.metrics_names
if isiterable(metrics_value):
return list(zip(metrics_names, metrics_value))
else:
return metrics_names[0], metrics_value
def _print_evaluate(self, data, scalar_values):
metrics_names = self.model.metrics_names
if isiterable(scalar_values):
for item in zip(metrics_names, scalar_values):
print(data + ': ' + '\t'.join((item[0], str(item[1]))))
else:
print(data + ': ' +
'\t'.join((metrics_names[0], str(scalar_values))))
def evaluate(self, x, y, n_cls):
anchor, positive, negetive = self._get_batch(
x, y, n_cls, 128*100)
metrics_value = self.model.evaluate([anchor, positive, negetive],
anchor)
metrics_names = self.model.metrics_names
if isiterable(metrics_value):
return list(zip(metrics_names, metrics_value))
else:
return metrics_names[0], metrics_value