wait_time no longer used.
@@ -33,7 +33,6 @@ def fit_generator(model,
shuffle=True,
initial_epoch=0):
"""See docstring for `Model.fit_generator`."""
- wait_time = 0.01 # in seconds
epoch = initial_epoch
do_validation = bool(validation_data)
@@ -158,8 +157,7 @@ def fit_generator(model,
else:
enqueuer = GeneratorEnqueuer(
generator,
- use_multiprocessing=use_multiprocessing,
- wait_time=wait_time)
+ use_multiprocessing=use_multiprocessing)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
@@ -284,7 +282,6 @@ def evaluate_generator(model, generator,
stateful_metric_indices = []
steps_done = 0
- wait_time = 0.01
outs_per_batch = []
batch_sizes = []
is_sequence = isinstance(generator, Sequence)
@@ -313,8 +310,7 @@ def evaluate_generator(model, generator,
else:
enqueuer = GeneratorEnqueuer(
generator,
- use_multiprocessing=use_multiprocessing,
- wait_time=wait_time)
+ use_multiprocessing=use_multiprocessing)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
@@ -390,7 +386,6 @@ def predict_generator(model, generator,
model._make_predict_function()
steps_done = 0
- wait_time = 0.01
all_outs = []
is_sequence = isinstance(generator, Sequence)
if not is_sequence and use_multiprocessing and workers > 1:
@@ -418,8 +413,7 @@ def predict_generator(model, generator,
else:
enqueuer = GeneratorEnqueuer(
generator,
- use_multiprocessing=use_multiprocessing,
- wait_time=wait_time)
+ use_multiprocessing=use_multiprocessing)
enqueuer.start(workers=workers, max_queue_size=max_queue_size)
output_generator = enqueuer.get()
else:
