way = -1 shot = None s_ver = 'V01' build = 'MyModelV2' db = 'mnist' n_cls = INFO[db]['n_cls'] shape = INFO[db]['shape'] rpt = Reporter(file_dir='./my_report.log') # %% Dataset loading data = load_data(db) X_train, X_test, y_train, y_test = get_fewshot(*data, shot, way) X_train, y_train = shuffle(X_train, y_train) X_test, y_test = shuffle(X_test, y_test) X_train = reshape(X_train / 255.0, shape) X_test = reshape(X_test / 255.0, shape) # %% Generator section traingen = MyTriplet(X_train, y_train, n_cls) validgen = MyTriplet(X_test, y_test, n_cls) # %% Schema creation schema = load_schema(s_ver) getattr(schema, 'build' + build)(shape, n_cls)
from sklearn.utils import shuffle
from sklearn.neighbors import KNeighborsClassifier
if __name__ == "__main__":
from __test_my_layer import MyLayerV1
e_len = 128
batch_size = 128
db = INFO['fashion']
shape = db['shape']
n_cls = db['n_cls']
X_train, X_test, y_train, y_test = get_fewshot(*load_data('fashion'),
shot=None)
X_train, y_train = shuffle(X_train, y_train)
X_test, y_test = shuffle(X_test, y_test)
X_train = reshape(X_train / 255.0, shape)
X_test = reshape(X_test / 255.0, shape)
# %% CNN My Dual Model
in_layer = layers.Input(shape=shape)
conv_01 = layers.Conv2D(32, (3, 3))(in_layer)
batch_01 = layers.BatchNormalization()(conv_01)
active_01 = layers.Activation('relu')(batch_01)
_01_out = MyLayerV1(8, 'sigmoid')(active_01)
rpt = Report()
for db, db_opt in DATASETS.items():
n_cls = INFORM[db]['n_cls']
shape = INFORM[db]['shape']
X_train, X_test, y_train, y_test = load_data(db)
X_train = reshape(X_train / 255.0, shape)
X_test = reshape(X_test / 255.0, shape)
X_train, X_valid, y_train, y_valid = train_test_split(X_train,
y_train,
test_size=0.25,
stratify=y_train)
plot_histogram(y_train, db + '_train')
for shot in db_opt['shots']:
_X_train, _y_train = get_fewshot(X_train, y_train, shot=shot)
data = (_X_train, X_valid, _y_train, y_valid)
for bld, bld_opt in METHODS.items():
# With Augmentation
rpt.write_dataset(db).write_shot(shot).flush()
Run(rpt, bld, n_cls, shape, db_opt, bld_opt, *data, X_test,
y_test, db, shot, True)
# Without Augmentation
rpt.write_dataset(db).write_shot(shot).flush()
Run(rpt, bld, n_cls, shape, db_opt, bld_opt, *data, X_test,
y_test, db, shot, False)
rpt.flush()
rpt.close()