python - 预期 TensorFlow 计算，发现是内在的

Question

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有一些必要的改变

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 collections
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.StructType([
    ('x', tff.TensorType(tf.float32, [None, 784])),
    ('y', tff.TensorType(tf.int32, [None]))])

MODEL_TYPE = tff.StructType([
    ('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 =  collections.OrderedDict(
        '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)

File "SameOR-elb.py", line 352, in <module>
    local_models = federated_train(   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\utils\function_utils.py",
line 561, in __call__
    return context.invoke(self, arg)   File "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py", line 49, in
wrapped_f
    return Retrying(*dargs, **dkw).call(f, *args, **kw)   File "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py", line 206, in
call
    return attempt.get(self._wrap_exception)   File "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py", line 247, in
get
    six.reraise(self.value[0], self.value[1], self.value[2])   File "C:\Users\Aw\Anaconda3\lib\site-packages\six.py", line 703, in reraise
    raise value   File "C:\Users\Aw\Anaconda3\lib\site-packages\retrying.py", line 200, in
call
    attempt = Attempt(fn(*args, **kwargs), attempt_number, False)   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\execution_context.py",
line 217, in invoke
    return event_loop.run_until_complete(   File "C:\Users\Aw\Anaconda3\lib\asyncio\base_events.py", line 616, in
run_until_complete
    return future.result()   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 388, in _wrapped
    return await coro   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\execution_context.py",
line 123, in _invoke
    result = await executor.create_call(comp, arg)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 343, in create_call
    return await comp_repr.invoke(self, arg)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 155, in invoke
    return await executor._evaluate(comp_lambda.result, new_scope)  # pylint: disable=protected-access   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 513, in _evaluate
    return await self._evaluate_block(comp, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 477, in _evaluate_block
    value = await self._evaluate(loc.value, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 507, in _evaluate
    return await self._evaluate_call(comp, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 446, in _evaluate_call
    return await self.create_call(func, arg=arg)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 339, in create_call
    return ReferenceResolvingExecutorValue(await   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\caching_executor.py",
line 281, in create_call
    target_value = await cached_value.target_future   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\thread_delegating_executor.py",
line 120, in create_call
    return await self._delegate(self._target_executor.create_call(comp, arg))   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\thread_delegating_executor.py",
line 105, in _delegate
    result_value = await _delegate_with_trace_ctx(coro, self._event_loop)   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 388, in _wrapped
    return await coro   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federating_executor.py",
line 445, in create_call
    return await self._strategy.compute_federated_intrinsic(   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federating_executor.py",
line 139, in compute_federated_intrinsic
    return await fn(arg)  # pylint: disable=not-callable   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federated_resolving_strategy.py",
line 453, in compute_federated_map
    return await self._map(arg, all_equal=False)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\federated_resolving_strategy.py",
line 320, in _map
    results = await asyncio.gather(*[   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 343, in create_call
    return await comp_repr.invoke(self, arg)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 155, in invoke
    return await executor._evaluate(comp_lambda.result, new_scope)  # pylint: disable=protected-access   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 513, in _evaluate
    return await self._evaluate_block(comp, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 477, in _evaluate_block
    value = await self._evaluate(loc.value, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 507, in _evaluate
    return await self._evaluate_call(comp, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 445, in _evaluate_call
    func, arg = await asyncio.gather(func, get_arg())   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 501, in _evaluate
    return await self._evaluate_to_delegate(comp, scope)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\reference_resolving_executor.py",
line 410, in _evaluate_to_delegate
    await self._target_executor.create_value(   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\caching_executor.py",
line 245, in create_value
    await cached_value.target_future   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\thread_delegating_executor.py",
line 110, in create_value
    return await self._delegate(   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\thread_delegating_executor.py",
line 105, in _delegate
    result_value = await _delegate_with_trace_ctx(coro, self._event_loop)   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 388, in _wrapped
    return await coro   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\common_libs\tracing.py",
line 200, in async_trace
    result = await fn(*fn_args, **fn_kwargs)   File "C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",
line 464, in create_value
    return EagerValue(value, self._tf_function_cache, type_spec, self._device)   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",
line 366, in __init__
    self._value = to_representation_for_type(value, tf_function_cache,   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",
line 287, in to_representation_for_type
    embedded_fn = embed_tensorflow_computation(value, type_spec, device)   File
"C:\Users\Aw\Anaconda3\lib\site-packages\tensorflow_federated\python\core\impl\executors\eager_tf_executor.py",
line 153, in embed_tensorflow_computation
    raise TypeError('Expected a TensorFlow computation, found {}.'.format( TypeError: Expected a TensorFlow computation, found
intrinsic.

我收到了这些错误。我需要建议。
我正在使用 tf 2.2.1Python 3.8.3 版本

Answer 1

sequence_reduce的使用可能导致此问题； TFF 的高性能堆栈尚不支持它。
如果性能不重要，我们可以通过在调用我们的计算之前安装引用执行器来立即解决这个问题:

tff.backends.reference.set_reference_context()

如 tutorial which demonstrates sequence_reduce 中所述.
然而，这是一个残酷的错误。您介意检查一下您使用的 TFF 版本吗？如果您不在 0.17.0 ，有可能我们已经在这里生成了更好的错误消息。如果你是，你介意吗 filing a GitHub issue 错误 TFF 喷涌而出？