def test_drop_dict(self):
model = keras.models.Sequential()
model.add(
DropConnect(keras.layers.Dense(units=5, activation='tanh'),
rate={'kernel': 0.2},
input_shape=(5, )))
model.add(
DropConnect(
keras.layers.Dense(units=2, activation='softmax'),
rate={'bias': 0.2},
))
self._test_fit_model(model)
def test_drop_batch_norm(self):
model = keras.models.Sequential()
model.add(
keras.layers.Dense(units=5, activation='tanh', input_shape=(5, )))
model.add(DropConnect(
keras.layers.BatchNormalization(),
rate=0.1,
))
model.add(keras.layers.Dense(units=2, activation='softmax'))
self._test_fit_model(model)
def test_drop_rnn(self):
model = keras.models.Sequential()
model.add(
keras.layers.Embedding(input_dim=10,
output_dim=5,
mask_zero=True,
input_shape=(10, )))
model.add(
DropConnect(
keras.layers.Bidirectional(keras.layers.GRU(units=2)),
rate=0.1,
))
model.add(keras.layers.Dense(units=2, activation='softmax'))
self._test_fit_model(model, generator=self._gen_embed_data)