def local_acc(model, all_batches):
acc = tff.sequence_sum(
tff.sequence_map(
tff.federated_computation(lambda b: batch_pre(model, b),
BATCH_TYPE), all_batches))
acc_l = tff.sequence_map(
tff.federated_computation(lambda b: batch_pre(model, b), BATCH_TYPE),
all_batches)
return acc
def local_eval(model, all_batches):
#
return tff.sequence_sum(
tff.sequence_map(
tff.federated_computation(
lambda b: batch_loss(model, b), BATCH_TYPE),
all_batches))
def local_eval(model, all_batchs):
#计算平均loss值时使用tff.sequence_average函数
return tff.sequence_sum(
#序列map函数，将all_batchs中的每个batch都进行loss值计算
#tff.sequence_map与tff.sequence_reduce的差别在于map是并行计算，reduce是串行计算
tff.sequence_map(
tff.federated_computation(lambda b: batch_loss(model, b),
BATCH_TYPE), all_batchs))
def local_eval(model, all_batches):
# tff.sequence_sum Replace with `tff.sequence_average()` once implemented.
return tff.sequence_sum(
tff.sequence_map(
#？？？？？
tff.federated_computation(lambda b: batch_loss(model, b),
BATCH_TYPE),
all_batches))
