python - Size mismatch in tensorflow_federated eager executor

Question

I am following this code https://github.com/BUAA-BDA/FedShapley/tree/master/TensorflowFL and trying to run the file same_OR.py

there is a problem in import tensorflow.compat.v1 as tf its show that unable to import " tensorflow.compat.v1" File "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("
", "").split("")
        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("
", "").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("***
--------------------------------------------------")
    parm_local = []
    learning_rate_list = []
    for j in range(len(g_) - 1):
        line = g_[j].split("
")
        if j == 0:
            weights_line = line[0:784]
            learning_rate_list.append(float(line[784].replace("*", "").replace("
", "")))
        else:
            weights_line = line[1:785]
            learning_rate_list.append(float(line[785].replace("*", "").replace("
", "")))
        valid_weights_line = []
        for l in weights_line:
            w_list = l.split("")
            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("***
--------------------------------------------------")
    bias_local = []
    for j in range(len(g_) - 1):
        line = g_[j].split("
")
        if j == 0:
            weights_line = line[0]
        else:
            weights_line = line[1]
        b_list = weights_line.split("")
        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("")
    w_paras = [float(i) for i in w_paras]
    b_paras = para_lines[1].split("")
    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 = "SAM

Answer 1

It looks like this is a case of mismatched tensor shapes, specifcially its expecting a shape of float32[784,10] but the argument is shapefloat32[10].

Near the end of the stack trace the key line appears to be:

File "C:UsersAwAnaconda3libsite-packagesensorflow_federatedpythoncoreimplexecutorseager_tf_executor.py", line 366, 
  in init 
File "C:UsersAwAnaconda3libsite-packagesensorflow_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].

The most common case this happens is converting dict (unordered in older versions of Python) to tff.StructType (ordered in TFF).

One place in the code that might be doing this is in:

  initial_model = {
      'weights': w_initial,
      'bias': b_initial
  }

Instead, changing this to a collections.OrderedDict to preserve the key ordering may help. Something like (ensuring the keys match the order in MODEL_TYPE):

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