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
存在问题，import tensorflow.compat.v1因为 tf 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, digit):
output_sequence = []
all_samples = [i for i, d in enumerate(source[1]) if d == digit]
for i in range(0, len(all_samples)):
output_sequence.append({
'x': np.array(source[0][all_samples[i]].flatten() / 255.0,
dtype=np.float32),
'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, 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
BATCH_TYPE = tff.NamedTupleType([
('x', tff.TensorType(tf.float32, [None, 784])),
('y', tff.TensorType(tf.int32, [None]))])
MODEL_TYPE = tff.NamedTupleType([
('weights', tff.TensorType(tf.float32, [784, 10])),
('bias', tff.TensorType(tf.float32, [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, batch, 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, tff.SERVER, all_equal=True)
@tff.federated_computation(
SERVER_MODEL_TYPE, SERVER_FLOAT_TYPE, CLIENT_DATA_TYPE)
def federated_train(model, learning_rate, data):
l = tff.federated_map(
local_train,
[tff.federated_broadcast(model),
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__), "test_images1_.txt"), 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"), encoding="utf-8")
else:
f = open(os.path.join(os.path.dirname(__file__), "test_labels_.txt"), encoding="utf-8")
lines = f.readlines()
for line in lines:
tem_ret = []
p = line.replace("[", "").replace("]", "").replace("\n", "").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("*", "").replace("\n", "")))
else:
weights_line = line[1:785]
learning_rate_list.append(float(line[785].replace("*", "").replace("\n", "")))
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], 1/len(agent_list)), 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'], np.multiply(lr[0][i], 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__), "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 = np.asarray(w_paras, dtype=np.float32).reshape([784, 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, learning_rate, 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"), "a", 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)
print("-"*50,file=f)
f.close()
m_w = np.zeros([784, 10], 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], 1/NUM_AGENT), 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_['weights'][i]),
model_['learning_rate'][i])
gradient_bias = np.divide(np.subtract(model_['bias'][i - 1], model_['bias'][i]),
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）：文件“samOR.py”，第 331 行，在 local_models = federated_train(model, learning_rate, federated_train_data) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\ core\impl\utils\function_utils.py"，第 561 行，调用中 return context.invoke(self, arg) File "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py"，第 49 行，在 Wrapped_f 中 return Retrying(*dargs, **dkw).call(f , *args, **kw) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py”，第 206 行，调用返回尝试.get(self._wrap_exception) 文件“C:\Users \Aw\Anaconda3\lib\site-packages\retrying.py”，第 247 行，在获取 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 reraise raise 值文件 "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py"，第 200 行，在通话中尝试 = Attempt(fn(*args, **kwargs), attempt_number, False) 文件 "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\execution_context.py "，第 213 行，在调用 arg = event_loop.run_until_complete（文件“C:\Users\Aw\Anaconda3\lib\asyncio\base_events.py”，第 616 行，在 run_until_complete 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 await coro 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\ executors\execution_context.py”，第 99 行，在 _ingest ingested = await 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 return await executor.create_value(val, type_spec) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await 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 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 await cached_value.target_future 文件 "C: \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await fn(*fn_args, **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 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 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 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 结果 = await fn(在 _wrapped 返回 await coro 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await fn(在 _wrapped 返回 await coro 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await 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 await self._strategy.compute_federated_value （值，type_spec）文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federated_resolving_strategy.py”，第 272 行，compute_federated_value 结果 = await asyncio.gather([ 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await 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 281 行，在 create_value vals = await asyncio.gather（文件“C:\Users\Aw\Anaconda3 \lib\site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await 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 返回 ReferenceResolvingExecutorValue（等待文件“C：\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\caching_executor.py”，第 245 行，在 create_value 等待 cached_value.target_future 文件“C:\Users\Aw\Anaconda3\lib\ site-packages\tensorflow_federated\python\common_libs\tracing.py”，第 200 行，在 async_trace 结果 = await fn(*fn_args, **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 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 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 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 结果 = await 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 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，在第 200 行，在 async_trace 结果 = await fn(*fn_args, **fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py”，行464，在 create_value 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，在第 200 行，在 async_trace 结果 = await fn(*fn_args, **fn_kwargs) 文件“C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py”，行464，在 create_value 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py"，第 366 行，在py"，第 366 行，在init 文件“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 TypeError（TypeError：明显类型 float32[10]张量 [-0.9900856 -0.9902875 -0.99910086 -0.9972545 -0.99561495 -0.99766624 -0.9964327 -0.99897027 -0.99897027 -0.9960221 -0.99313617] 与预期类型不匹配，但它与预期类型不匹配 float32[784,1:1任务：<Task pending name='Task-7' coro=<trace..async_trace() running at C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py:200> wait_for=<未来挂起 cb=[_chain_future.._call_check_cancel() at C:\Users\Aw0000282F4DFE3D0>()]>