Why is accuracy from fit_generator different to that from evaluate_generator in Keras?

I now managed having the same evaluation metrics. I changed the following:

• I set seed in flow_from_directory() as suggested by @Anakin

def generate_data(path, imagesize, nBatches):
        datagen = ImageDataGenerator(rescale=1./255)
        generator = datagen.flow_from_directory(directory=path,     # path to the target directory
             target_size=(imagesize,imagesize),                     # dimensions to which all images found will be resize
             color_mode='rgb',                                      # whether the images will be converted to have 1, 3, or 4 channels
             classes=None,                                          # optional list of class subdirectories
             class_mode='categorical',                              # type of label arrays that are returned
             batch_size=nBatches,                                   # size of the batches of data
             shuffle=True,                                          # whether to shuffle the data
             seed=42)                                               # random seed for shuffling and transformations
        return generator

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• I set use_multiprocessing=False in fit_generator() according to the warning: use_multiprocessing=True and multiple workers may duplicate your data

history = model.fit_generator(generator=trainGenerator,
                                  steps_per_epoch=trainGenerator.samples//nBatches,     # total number of steps (batches of samples)
                                  epochs=nEpochs,                   # number of epochs to train the model
                                  verbose=2,                        # verbosity mode. 0 = silent, 1 = progress bar, 2 = one line per epoch
                                  callbacks=callback,               # keras.callbacks.Callback instances to apply during training
                                  validation_data=valGenerator,     # generator or tuple on which to evaluate the loss and any model metrics at the end of each epoch
                                  validation_steps=
                                  valGenerator.samples//nBatches,   # number of steps (batches of samples) to yield from validation_data generator before stopping at the end of every epoch
                                  class_weight=None,                # optional dictionary mapping class indices (integers) to a weight (float) value, used for weighting the loss function
                                  max_queue_size=10,                # maximum size for the generator queue
                                  workers=1,                        # maximum number of processes to spin up when using process-based threading
                                  use_multiprocessing=False,        # whether to use process-based threading
                                  shuffle=False,                    # whether to shuffle the order of the batches at the beginning of each epoch
                                  initial_epoch=0)                  # epoch at which to start training

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• I unified my python setup as suggested in the keras documentation on how to obtain reproducible results using Keras during development

import tensorflow as tf
import random as rn
from keras import backend as K

np.random.seed(42)
rn.seed(12345)
session_conf = tf.ConfigProto(intra_op_parallelism_threads=1,
                              inter_op_parallelism_threads=1)
tf.set_random_seed(1234)
sess = tf.Session(graph=tf.get_default_graph(), config=session_conf)
K.set_session(sess)

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• Instead of rescaling input images with datagen = ImageDataGenerator(rescale=1./255), I now generate my data with:

from keras.applications.resnet50 import preprocess_input
datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

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With this, I managed to have a similar accuracy and loss from fit_generator() and evaluate_generator(). Also, using the same data for training and testing now results in a similar metrics. Reasons for remaining differences are provided in the keras documentation.

Set use_multiprocessing=False at fit_generator level fixes the problem BUT at the cost of slowing down training significantly. A better but still imperfect workround would be to set use_multiprocessing=False for only the validation generator as the code below modified from keras' fit_generator function.

...
        try:
            if do_validation:
                if val_gen and workers > 0:
                    # Create an Enqueuer that can be reused
                    val_data = validation_data
                    if isinstance(val_data, Sequence):
                        val_enqueuer = OrderedEnqueuer(val_data,
                                                       **use_multiprocessing=False**)
                        validation_steps = len(val_data)
                    else:
                        val_enqueuer = GeneratorEnqueuer(val_data,
                                                         **use_multiprocessing=False**)
                    val_enqueuer.start(workers=workers,
                                       max_queue_size=max_queue_size)
                    val_enqueuer_gen = val_enqueuer.get()
...