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))
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