def run_train(params: dict) -> Tuple[threading.Thread, threading.Thread]:
"""Train a network on a data generator.
params -> dictionary.
Required fields:
* model_name
* generator_name
* dataset_dir
* tile_size
* clf_name
* checkpoints_dir
* summaries_dir
Returns prefetch thread & model.fit thread"""
assert 'model_name' in params
assert 'generator_name' in params
Model = ModelFactory.get_model(params['model_name'])
Generator = GeneratorFactory.get_generator(params['generator_name'])
model = Model(**params)
feed = Generator(**params)
pf = PreFetch(feed)
t1 = threading.Thread(target=pf.fetch)
t2 = threading.Thread(target=model.fit, args=(pf,))
t1.start()
t2.start()
return t1,t2
from callback import MultipleClassAUROC, MultiGPUModelCheckpoint
from model import ModelFactory
import json
output_dir = "C:\\Users\\yanqing.yqh\\code\\wly-chexnet-keras\\modeltrain\\output"
train_dir = (
"C:\\Users\\yanqing.yqh\\code\\wly-chexnet-keras\\cats_and_dogs_small\\train"
)
validation_dir = (
"C:\\Users\\yanqing.yqh\\code\\wly-chexnet-keras\\cats_and_dogs_small\\validation"
)
output_weights_path = os.path.join(output_dir, "weight.h5")
class_names = ["dog", "cat"]
model_factory = ModelFactory()
model = model_factory.get_model(class_names)
print(model.summary())
print(len(model.layers))
train_datagen = ImageDataGenerator(
samplewise_center=True,
samplewise_std_normalization=True,
horizontal_flip=True,
vertical_flip=False,
height_shift_range=0.05,
width_shift_range=0.1,
rotation_range=5,
shear_range=0.1,
fill_mode="reflect",
zoom_range=0.15,
rescale=1.0 / 255,