问题描述
我正在关注此代码 https://github.com/BUAA-BDA/FedShapley/tree/master/TensorflowFL 并尝试运行文件 same_OR.py
import tensorflow.compat.v1 有问题，显示无法导入“tensorflow.compat.v1”文件“sameOR.py”
from __future__ import absolute_import,division,print_function
import tensorflow_federated as tff
import tensorflow.compat.v1 as tf
import numpy as np
import time
from scipy.special import comb,perm
import os
# tf.compat.v1.enable_v2_behavior()
# tf.compat.v1.enable_eager_execution()
# NUM_EXAMPLES_PER_USER = 1000
BATCH_SIZE = 100
NUM_AGENT = 5
def get_data_for_digit(source,digit):
output_sequence = []
all_samples = [i for i,d in enumerate(source[1]) if d == digit]
for i in range(0,len(all_samples),BATCH_SIZE):
batch_samples = all_samples[i:i + BATCH_SIZE]
output_sequence.append({
'x': np.array([source[0][i].flatten() / 255.0 for i in batch_samples],dtype=np.float32),'y': np.array([source[1][i] for i in batch_samples],dtype=np.int32)})
return output_sequence
def get_data_for_digit_test(source,len(all_samples)):
output_sequence.append({
'x': np.array(source[0][all_samples[i]].flatten() / 255.0,'y': np.array(source[1][all_samples[i]],dtype=np.int32)})
return output_sequence
def get_data_for_federated_agents(source,num):
output_sequence = []
Samples = []
for digit in range(0,10):
samples = [i for i,d in enumerate(source[1]) if d == digit]
samples = samples[0:5421]
Samples.append(samples)
all_samples = []
for sample in Samples:
for sample_index in range(int(num * (len(sample) / NUM_AGENT)),int((num + 1) * (len(sample) / NUM_AGENT))):
all_samples.append(sample[sample_index])
# all_samples = [i for i in range(int(num*(len(source[1])/NUM_AGENT)),int((num+1)*(len(source[1])/NUM_AGENT)))]
for i in range(0,dtype=np.int32)})
return output_sequence
BATCH_TYPE = tff.NamedTupleType([
('x',tff.TensorType(tf.float32,[None,784])),('y',tff.TensorType(tf.int32,[None]))])
MODEL_TYPE = tff.NamedTupleType([
('weights',[784,10])),('bias',[10]))])
@tff.tf_computation(MODEL_TYPE,BATCH_TYPE)
def batch_loss(model,batch):
predicted_y = tf.nn.softmax(tf.matmul(batch.x,model.weights) + model.bias)
return -tf.reduce_mean(tf.reduce_sum(
tf.one_hot(batch.y,10) * tf.log(predicted_y),axis=[1]))
@tff.tf_computation(MODEL_TYPE,BATCH_TYPE,tf.float32)
def batch_train(initial_model,batch,learning_rate):
# Define a group of model variables and set them to `initial_model`.
model_vars = tff.utils.create_variables('v',MODEL_TYPE)
init_model = tff.utils.assign(model_vars,initial_model)
# Perform one step of gradient descent using loss from `batch_loss`.
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
with tf.control_dependencies([init_model]):
train_model = optimizer.minimize(batch_loss(model_vars,batch))
# Return the model vars after performing this gradient descent step.
with tf.control_dependencies([train_model]):
return tff.utils.identity(model_vars)
LOCAL_DATA_TYPE = tff.SequenceType(BATCH_TYPE)
@tff.federated_computation(MODEL_TYPE,tf.float32,LOCAL_DATA_TYPE)
def local_train(initial_model,learning_rate,all_batches):
# Mapping function to apply to each batch.
@tff.federated_computation(MODEL_TYPE,BATCH_TYPE)
def batch_fn(model,batch):
return batch_train(model,learning_rate)
l = tff.sequence_reduce(all_batches,initial_model,batch_fn)
return l
@tff.federated_computation(MODEL_TYPE,LOCAL_DATA_TYPE)
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))
SERVER_MODEL_TYPE = tff.FederatedType(MODEL_TYPE,tff.SERVER,all_equal=True)
CLIENT_DATA_TYPE = tff.FederatedType(LOCAL_DATA_TYPE,tff.CLIENTS)
@tff.federated_computation(SERVER_MODEL_TYPE,CLIENT_DATA_TYPE)
def federated_eval(model,data):
return tff.federated_mean(
tff.federated_map(local_eval,[tff.federated_broadcast(model),data]))
SERVER_FLOAT_TYPE = tff.FederatedType(tf.float32,all_equal=True)
@tff.federated_computation(
SERVER_MODEL_TYPE,SERVER_FLOAT_TYPE,CLIENT_DATA_TYPE)
def federated_train(model,data):
l = tff.federated_map(
local_train,tff.federated_broadcast(learning_rate),data])
return l
# return tff.federated_mean()
def readTestimagesFromFile(distr_same):
ret = []
if distr_same:
f = open(os.path.join(os.path.dirname(__file__),"test_images1_.txt"),encoding="utf-8")
else:
f = open(os.path.join(os.path.dirname(__file__),encoding="utf-8")
lines = f.readlines()
for line in lines:
tem_ret = []
p = line.replace("[","").replace("]","").replace("\n","").split("\t")
for i in p:
if i != "":
tem_ret.append(float(i))
ret.append(tem_ret)
return np.asarray(ret)
def readTestLabelsFromFile(distr_same):
ret = []
if distr_same:
f = open(os.path.join(os.path.dirname(__file__),"test_labels_.txt"),"").split(" ")
for i in p:
if i!="":
tem_ret.append(float(i))
ret.append(tem_ret)
return np.asarray(ret)
def getParmsAndLearningRate(agent_no):
f = open(os.path.join(os.path.dirname(__file__),"weights_" + str(agent_no) + ".txt"))
content = f.read()
g_ = content.split("***\n--------------------------------------------------")
parm_local = []
learning_rate_list = []
for j in range(len(g_) - 1):
line = g_[j].split("\n")
if j == 0:
weights_line = line[0:784]
learning_rate_list.append(float(line[784].replace("*","")))
else:
weights_line = line[1:785]
learning_rate_list.append(float(line[785].replace("*","")))
valid_weights_line = []
for l in weights_line:
w_list = l.split("\t")
w_list = w_list[0:len(w_list) - 1]
w_list = [float(i) for i in w_list]
valid_weights_line.append(w_list)
parm_local.append(valid_weights_line)
f.close()
f = open(os.path.join(os.path.dirname(__file__),"bias_" + str(agent_no) + ".txt"))
content = f.read()
g_ = content.split("***\n--------------------------------------------------")
bias_local = []
for j in range(len(g_) - 1):
line = g_[j].split("\n")
if j == 0:
weights_line = line[0]
else:
weights_line = line[1]
b_list = weights_line.split("\t")
b_list = b_list[0:len(b_list) - 1]
b_list = [float(i) for i in b_list]
bias_local.append(b_list)
f.close()
ret = {
'weights': np.asarray(parm_local),'bias': np.asarray(bias_local),'learning_rate': np.asarray(learning_rate_list)
}
return ret
def train_with_gradient_and_valuation(agent_list,grad,bi,lr,distr_type):
f_ini_p = open(os.path.join(os.path.dirname(__file__),"initial_model_parameters.txt"),"r")
para_lines = f_ini_p.readlines()
w_paras = para_lines[0].split("\t")
w_paras = [float(i) for i in w_paras]
b_paras = para_lines[1].split("\t")
b_paras = [float(i) for i in b_paras]
w_initial_g = np.asarray(w_paras,dtype=np.float32).reshape([784,10])
b_initial_g = np.asarray(b_paras,dtype=np.float32).reshape([10])
f_ini_p.close()
model_g = {
'weights': w_initial_g,'bias': b_initial_g
}
for i in range(len(grad[0])):
# i->迭代轮数
gradient_w = np.zeros([784,10],dtype=np.float32)
gradient_b = np.zeros([10],dtype=np.float32)
for j in agent_list:
gradient_w = np.add(np.multiply(grad[j][i],1/len(agent_list)),gradient_w)
gradient_b = np.add(np.multiply(bi[j][i],gradient_b)
model_g['weights'] = np.subtract(model_g['weights'],np.multiply(lr[0][i],gradient_w))
model_g['bias'] = np.subtract(model_g['bias'],gradient_b))
test_images = readTestimagesFromFile(False)
test_labels_onehot = readTestLabelsFromFile(False)
m = np.dot(test_images,np.asarray(model_g['weights']))
test_result = m + np.asarray(model_g['bias'])
y = tf.nn.softmax(test_result)
correct_prediction = tf.equal(tf.argmax(y,1),tf.arg_max(test_labels_onehot,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
return accuracy.numpy()
def remove_list_indexed(removed_ele,original_l,ll):
new_original_l = []
for i in original_l:
new_original_l.append(i)
for i in new_original_l:
if i == removed_ele:
new_original_l.remove(i)
for i in range(len(ll)):
if set(ll[i]) == set(new_original_l):
return i
return -1
def shapley_list_indexed(original_l,ll):
for i in range(len(ll)):
if set(ll[i]) == set(original_l):
return i
return -1
def PowerSetsBinary(items):
N = len(items)
set_all = []
for i in range(2 ** N):
combo = []
for j in range(N):
if (i >> j) % 2 == 1:
combo.append(items[j])
set_all.append(combo)
return set_all
if __name__ == "__main__":
start_time = time.time()
#data_num = np.asarray([5923,6742,5958,6131,5842])
#agents_weights = np.divide(data_num,data_num.sum())
for index in range(NUM_AGENT):
f = open(os.path.join(os.path.dirname(__file__),"weights_"+str(index)+".txt"),"w")
f.close()
f = open(os.path.join(os.path.dirname(__file__),"bias_" + str(index) + ".txt"),"w")
f.close()
mnist_train,mnist_test = tf.keras.datasets.mnist.load_data()
disTRIBUTION_TYPE = "SAME"
federated_train_data_divide = None
federated_train_data = None
if disTRIBUTION_TYPE == "SAME":
federated_train_data_divide = [get_data_for_federated_agents(mnist_train,d) for d in range(NUM_AGENT)]
federated_train_data = federated_train_data_divide
f_ini_p = open(os.path.join(os.path.dirname(__file__),"r")
para_lines = f_ini_p.readlines()
w_paras = para_lines[0].split("\t")
w_paras = [float(i) for i in w_paras]
b_paras = para_lines[1].split("\t")
b_paras = [float(i) for i in b_paras]
w_initial = np.asarray(w_paras,10])
b_initial = np.asarray(b_paras,dtype=np.float32).reshape([10])
f_ini_p.close()
initial_model = {
'weights': w_initial,'bias': b_initial
}
model = initial_model
learning_rate = 0.1
for round_num in range(50):
local_models = federated_train(model,federated_train_data)
print("learning rate: ",learning_rate)
#print(local_models[0][0])#第0个agent的weights矩阵
#print(local_models[0][1])#第0个agent的bias矩阵
#print(len(local_models))
for local_index in range(len(local_models)):
f = open(os.path.join(os.path.dirname(__file__),"weights_"+str(local_index)+".txt"),"a",encoding="utf-8")
for i in local_models[local_index][0]:
line = ""
arr = list(i)
for j in arr:
line += (str(j)+"\t")
print(line,file=f)
print("***"+str(learning_rate)+"***",file=f)
print("-"*50,file=f)
f.close()
f = open(os.path.join(os.path.dirname(__file__),"bias_" + str(local_index) + ".txt"),encoding="utf-8")
line = ""
for i in local_models[local_index][1]:
line += (str(i) + "\t")
print(line,file=f)
print("***" + str(learning_rate) + "***",file=f)
f.close()
m_w = np.zeros([784,dtype=np.float32)
m_b = np.zeros([10],dtype=np.float32)
for local_model_index in range(len(local_models)):
m_w = np.add(np.multiply(local_models[local_model_index][0],1/NUM_AGENT),m_w)
m_b = np.add(np.multiply(local_models[local_model_index][1],m_b)
model = {
'weights': m_w,'bias': m_b
}
learning_rate = learning_rate * 0.9
loss = federated_eval(model,federated_train_data)
print('round {},loss={}'.format(round_num,loss))
print(time.time()-start_time)
gradient_weights = []
gradient_biases = []
gradient_lrs = []
for ij in range(NUM_AGENT):
model_ = getParmsAndLearningRate(ij)
gradient_weights_local = []
gradient_biases_local = []
learning_rate_local = []
for i in range(len(model_['learning_rate'])):
if i == 0:
gradient_weight = np.divide(np.subtract(initial_model['weights'],model_['weights'][i]),model_['learning_rate'][i])
gradient_bias = np.divide(np.subtract(initial_model['bias'],model_['bias'][i]),model_['learning_rate'][i])
else:
gradient_weight = np.divide(np.subtract(model_['weights'][i - 1],model_['learning_rate'][i])
gradient_bias = np.divide(np.subtract(model_['bias'][i - 1],model_['learning_rate'][i])
gradient_weights_local.append(gradient_weight)
gradient_biases_local.append(gradient_bias)
learning_rate_local.append(model_['learning_rate'][i])
gradient_weights.append(gradient_weights_local)
gradient_biases.append(gradient_biases_local)
gradient_lrs.append(learning_rate_local)
all_sets = PowerSetsBinary([i for i in range(NUM_AGENT)])
group_shapley_value = []
for s in all_sets:
group_shapley_value.append(
train_with_gradient_and_valuation(s,gradient_weights,gradient_biases,gradient_lrs,disTRIBUTION_TYPE))
print(str(s)+"\t"+str(group_shapley_value[len(group_shapley_value)-1]))
agent_shapley = []
for index in range(NUM_AGENT):
shapley = 0.0
for j in all_sets:
if index in j:
remove_list_index = remove_list_indexed(index,j,all_sets)
if remove_list_index != -1:
shapley += (group_shapley_value[shapley_list_indexed(j,all_sets)] - group_shapley_value[
remove_list_index]) / (comb(NUM_AGENT - 1,len(all_sets[remove_list_index])))
agent_shapley.append(shapley)
for ag_s in agent_shapley:
print(ag_s)
print("end_time",time.time()-start_time)
这些是错误列表..有人可以帮忙吗？
回溯（最近一次调用最后一次）：文件“samOR.py”，第 331 行，在 local_models = federated_train(model,federated_train_data) 文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\utils\function_utils.py",第 561 行，调用 返回 context.invoke(self,arg) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py”，第 49 行，在 包裹_f return retrying(*dargs,**dkw).call(f,*args,**kw) File "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py",line 206,in 称呼 返回尝试.get(self._wrap_exception)文件“C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py”，第247行，在 得到 6.reraise(self.value[0],self.value[1],self.value[2]) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\six.py”，第 703 行，在加注 提高值文件“C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py”，第 200 行，在 称呼 尝试=尝试（fn（*args，**kwargs），尝试编号，假）文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\execution_context.py",第 213 行，在调用中 arg = event_loop.run_until_complete( 文件“C:\Users\Aw\Anaconda3\lib\asyncio\base_events.py”，第 616 行，在 运行直到完成 返回 future.result() 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”， 第 388 行，在 _wrapped 中 返回等待 coro 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\execution_context.py”， 第 99 行，在 _摄取 摄取 = 等待 asyncio.gather(*ingested) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\execution_context.py”， 第 104 行，在 _ingest 中 return await executor.create_value(val,type_spec) File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",第 200 行，在 async_trace 中 结果 = 等待 fn(*fn_args,**fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py”， 第 286 行，在 create_value 中 return ReferenceResolvingExecutorValue(await File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\caching_executor.py",第 245 行，在 create_value 中 await cached_value.target_future File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",**fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\thread_delegating_executor.py”， 第 110 行，在 create_value 中 return await self._delegate( File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\thread_delegating_executor.py",第 105 行，在 _delegate 中 result_value = await _delegate_with_trace_ctx(coro,self._event_loop) 文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",第 388 行，在 _wrapped 中 返回等待 coro 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”， 第 200 行，在 async_trace 中 结果 = 等待 fn(fn_args,**fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federating_executor.py”， 第 383 行，在 create_value 中 返回等待 self._strategy.compute_federated_value(value,type_spec) 文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federated_resolving_strategy.py",第 272 行，在 compute_federated_value 中 result = await asyncio.gather([ File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",**fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py”， 第 281 行，在 create_value 中 vals = await asyncio.gather( 文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",第 388 行，在 _wrapped 中 返回等待 coro 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”， 第 200 行，在 async_trace 中 结果 = 等待 fn(*fn_args,**fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py”， 第 464 行，在 create_value 中 返回 EagerValue(value,self._tf_function_cache,type_spec,self._device) 文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",第 366 行，在 init 文件中 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",第 326 行，在 to_representation_for_type 中 引发类型错误（类型错误：张量的表观类型 float32[10] [-0.9900856 -0.9902875 -0.99910086 -0.9972545 -0.99561495 -0.99766624 -0.9964327 -0.99897027 -0.9960221 -0.99313617] 与预期的类型 float32[784,10] 不匹配。错误：asyncio：任务是 销毁，但它是待定的！任务： wait_for=()]>
解决方法
看起来这是张量形状不匹配的情况，特别是它期望形状为 float32[784,10]，但参数为 shapefloat32[10]。
在堆栈跟踪的末尾附近，关键行似乎是：
File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",line 366,in init
File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",line 326,in to_representation_for_type raise TypeError(
TypeError: The apparent type float32[10] of a tensor [-0.9900856 -0.9902875 -0.99910086 -0.9972545 -0.99561495 -0.99766624 -0.9964327 -0.99897027 -0.9960221 -0.99313617] does not match the expected type float32[784,10].
最常见的情况是将 dict（在旧版 Python 中无序）转换为 tff.StructType（在 TFF 中有序）。
代码中可能会这样做的一个地方是：
initial_model = {
'weights': w_initial,'bias': b_initial
}
相反，将其更改为 collections.OrderedDict 以保留键顺序可能会有所帮助。类似于（确保键与 MODEL_TYPE 中的顺序匹配）：
import collections
initial_model = collections.OrderedDict(
weights=w_initial,bias=b_initial)