tensorflow_federated急切执行器中的大小不匹配

Question

我正在执行以下代码https://github.com/BUAA-BDA/FedShapley/tree/master/TensorflowFL并尝试运行文件same_OR.py

在CCD_ 1中存在问题；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。。有人能帮忙吗？

Traceback(最近调用last)：文件；samOR.py"；，第331行，inlocal_models=federated_train(模型，学习率，federated_train_data)文件"C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\utils\function_utils.py"，第561行，在调用返回上下文。invoke(self，arg)File"；C： \Users\Aw\Anaconda3\lib\site-packages\retrying.py"；，第49行，in包装_freturn重试(*dargs，**dkw).call(f，*args，**kw)File"C： \Users\Aw\Anaconda3\lib\site-packages\retrying.py"；，第206行，in呼叫return try.get(self.wrap_exception)文件"C： \Users\Aw\Anaconda3\lib\site-packages\retrying.py"；，第247行，in收到6.reraise(self.value[0]，self.value[1]，self.value[2])File"；C： \Users\Aw\Anaconda3\lib\site-packages\six.py"；，703号线提升值文件"；C： \Users\Aw\Anaconda3\lib\site-packages\retrying.py"；，第200行，in呼叫attempt=尝试(fn(*args，**kwargs)，attempt_number，False)文件"C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\execution_context.py"，第213行，调用中arg=event_loop.run_until_complete(文件"C:\Users\Aw\Anaconda3\lib\asyncio\base_events.py"，第616行，位于run_until_completereturn future.result()文件"C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\common_libs\tracing.py"，第388行，in _wrapped返回等待coro文件"；C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\execution_context.py"，第99行，in_摄取intaked＝等待asyncio.gate(*intaked)File"；C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\execution_context.py"，第104行，在_ingestreturn 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result＝等待fn(*fn_args，**fn_kwargs)文件"；C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\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\pimpl\executors\caching_executor.py；，第245行，在create_value中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中result＝等待fn(*fn_args，**fn_kwargs)文件"；C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\thread_delegation_executor.py"，第110行，在create_value中归来等待自己_delegate(文件"C:\Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\thread_delegation_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，in _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中result=wait fn(fn_args，**fn_kwargs)File"C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\federating_executor.py"，第383行，在create_value中归来等待自己_strategy.compute_federated_value(value，type_spec)文件"C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\federated_solution_strategy.py"，第272行，compute_federated_valueresult=await asyncio.gate([File"C:\Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\common_libs\tracing.py"；，第200行，在async_trace中result＝等待fn(*fn_args，**fn_kwargs)文件"；C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\reference_resolving_executor.py"，第281行，在create_value中vals=await asyncio.gate(文件"C:\Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\common_libs\tracing.py"；，第200行，在async_trace中result＝等待fn(*fn_args，**fn_kwargs)文件"；C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\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\pimpl\executors\caching_executor.py；，第245行，在create_value中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中result＝等待fn(*fn_args，**fn_kwargs)文件"；C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\thread_delegation_executor.py"，第110行，在create_value中归来等待自己_delegate(文件"C:\Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\core\impl\executers\thread_delegation_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，in _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中result＝等待fn(*fn_args，**fn_kwargs)文件"；C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\eager_tf_executor.py"，第464行，在create_value中return EagleValue(value，self._tf_function_cache，type_spec，self._device)文件"C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\eager_tf_executor.py"，init文件中的第366行"C： \Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executers\eager_tf_executor.py"，第326行，在to_representation_for_type中raise TypeError(TypeError：张量的表观类型float32[10][-9900856-0.9902875-0.99910086-0.9972545-0.99561495-0.99766624-0.9964327-0.99897027-0.9960221-0.99313617]与预期类型float32[784，10]不匹配。错误：asyncio:任务为已销毁，但尚未处理！任务：<任务挂起名称="任务-7"coro=<查出async_trace()运行于C： \Users\Aw\Anaconda3\lib\site packages\tensorflow_federated\python\common_libs\tracing.py:200>wait_for=<未来待定cb=[_chain_future.._call_check_cancel()C： \Users\Aw0000282F4DFE3D0>()]>

Answer 1

看起来这是一个张量形状不匹配的情况，特别是它期望float32[784,10]的形状，但参数是形状float32[10]。

在堆栈轨迹的末尾，关键行显示为：

File "C:UsersAwAnaconda3libsite-packagestensorflow_federatedpythoncoreimplexecutorseager_tf_executor.py", line 366, 
in init 
File "C:UsersAwAnaconda3libsite-packagestensorflow_federatedpythoncoreimplexecutorseager_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(在旧版本的Python中是无序的)转换为tff.StructType(在TFF中是有序的)。

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以保留密钥排序可能会有所帮助。类似(确保密钥与MODEL_TYPE中的顺序匹配)：

import collections

initial_model = collections.OrderedDict(
weights=w_initial,
bias=b_initial)