def __init__(self, feature_data_file=FEATURE_DATA_FILE, label_coarse=False):
self.label_coarse = label_coarse
self.reader = DataSet("")
self.query_matcher = QueryMatcher(feature_data_file, label_coarse=label_coarse)
self.feature_db_flattened = self.query_matcher.features_flattened
self.feature_db_raw = self.query_matcher.features_raw
self.features_df_flat = pd.DataFrame(self.feature_db_flattened).set_index('name')
self.features_df_raw = pd.DataFrame(self.feature_db_raw).set_index('name')
self.mesh_classes_count = self.features_df_flat['label'].value_counts()
if self.label_coarse:
self.mesh_classes_count = self.features_df_flat['label_coarse'].value_counts().rename("label")
def train():
tr_config = {
'flag': True,
'rg': 25, # 7, 5
'wrg': 0.25, # 1, 3
'hrg': 0.25, # 1, 3
'zoom': 0.25 # 1, 1
}
callbacks = get_callbacks('mynet_v4_bias', patience=30)
paths, y = search_file('set1/segmented_set1')
paths, y = search_file('set2/segmented_set2', paths=paths, y=y)
ds = DataSet(nframe=30,
fstride=6,
name='UT interaction',
size=[224, 224, 3],
filepaths=paths,
y=y,
kernel_size=4)
ds.make_set(op='msqr', name='train')
ds.make_set(op='msqr', name='valid')
#opt = Adam(lr=1e-4, beta_1=0.9, beta_2=0.999, decay=0.1)
#opt = SGD(lr=2*1e-1, momentum=0.9, nesterov=True, decay=0.2)
opt = RMSprop(lr=0.001, rho=0.9, decay=0.01)
model = MobileNet(alpha=1.0, shape=[29, 56, 56, 1], nframe=29)
model.compile(optimizer=opt,
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
#model.load_weights('mynet_v4.h5')
model.fit_generator(generator=ds.train_gen(batch_size=5,
aug_config=tr_config),
steps_per_epoch=100,
epochs=300,
validation_data=ds.valid_gen(),
verbose=1,
validation_steps=ds.getVlen,
callbacks=callbacks)