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  /external/tensorflow/tensorflow/python/keras/engine/
training_arrays.py 263 batch_outs = f(actual_inputs)
297 if not isinstance(batch_outs, list):
298 batch_outs = [batch_outs]
301 batch_outs = distributed_training_utils._per_device_aggregate_batch(
302 batch_outs, model, mode)
306 aggregator.create(batch_outs)
307 aggregator.aggregate(batch_outs)
310 batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
352 batch_outs = f(ins_batch
    [all...]
training_generator.py 257 batch_outs = batch_function(*batch_data)
258 if not isinstance(batch_outs, list):
259 batch_outs = [batch_outs]
262 aggregator.create(batch_outs)
282 aggregator.aggregate(batch_outs)
285 batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)
training_utils.py 67 def create(self, batch_outs):
71 batch_outs: A list of batch-level outputs.
76 def aggregate(self, batch_outs, batch_start=None, batch_end=None):
80 batch_outs: A list of batch-level outputs.
97 def create(self, batch_outs):
98 self.results = [0.] * len(batch_outs)
100 def aggregate(self, batch_outs, batch_start=None, batch_end=None):
103 self.results[0] += batch_outs[0]
105 self.results[0] += batch_outs[0] * (batch_end - batch_start)
107 self.results[1:] = batch_outs[1:
    [all...]
training_distributed.py 536 _, batch_outs = K.batch_get_value([test_op, output_tensors])
551 outs[i] += batch_outs[label]
554 outs[i] = batch_outs[label]
682 _, batch_outs = K.batch_get_value([predict_op, output_tensors])
696 unconcatenated_outs[i].extend(batch_outs[label])
697 batch_logs = cbks.make_logs(model, batch_logs, batch_outs, mode)

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