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from __future__ import absolute_import
from __future__ import print_function
import pytest
from keras.models import Model, Sequential
from keras.layers import Dense, Input
def test_layer_trainability_switch():
# with constructor argument, in Sequential
model = Sequential()
model.add(Dense(2, trainable=False, input_dim=1))
assert model.trainable_weights == []
# by setting the `trainable` argument, in Sequential
model = Sequential()
layer = Dense(2, input_dim=1)
model.add(layer)
assert model.trainable_weights == layer.trainable_weights
layer.trainable = False
assert model.trainable_weights == []
# with constructor argument, in Model
x = Input(shape=(1,))
y = Dense(2, trainable=False)(x)
model = Model(x, y)
assert model.trainable_weights == []
# by setting the `trainable` argument, in Model
x = Input(shape=(1,))
layer = Dense(2)
y = layer(x)
model = Model(x, y)
assert model.trainable_weights == layer.trainable_weights
layer.trainable = False
assert model.trainable_weights == []
def test_model_trainability_switch():
# a non-trainable model has no trainable weights
x = Input(shape=(1,))
y = Dense(2)(x)
model = Model(x, y)
model.trainable = False
assert model.trainable_weights == []
# same for Sequential
model = Sequential()
model.add(Dense(2, input_dim=1))
model.trainable = False
assert model.trainable_weights == []
def test_nested_model_trainability():
# a Sequential inside a Model
inner_model = Sequential()
inner_model.add(Dense(2, input_dim=1))
x = Input(shape=(1,))
y = inner_model(x)
outer_model = Model(x, y)
assert outer_model.trainable_weights == inner_model.trainable_weights
inner_model.trainable = False
assert outer_model.trainable_weights == []
inner_model.trainable = True
inner_model.layers[-1].trainable = False
assert outer_model.trainable_weights == []
# a Sequential inside a Sequential
inner_model = Sequential()
inner_model.add(Dense(2, input_dim=1))
outer_model = Sequential()
outer_model.add(inner_model)
assert outer_model.trainable_weights == inner_model.trainable_weights
inner_model.trainable = False
assert outer_model.trainable_weights == []
inner_model.trainable = True
inner_model.layers[-1].trainable = False
assert outer_model.trainable_weights == []
# a Model inside a Model
x = Input(shape=(1,))
y = Dense(2)(x)
inner_model = Model(x, y)
x = Input(shape=(1,))
y = inner_model(x)
outer_model = Model(x, y)
assert outer_model.trainable_weights == inner_model.trainable_weights
inner_model.trainable = False
assert outer_model.trainable_weights == []
inner_model.trainable = True
inner_model.layers[-1].trainable = False
assert outer_model.trainable_weights == []
# a Model inside a Sequential
x = Input(shape=(1,))
y = Dense(2)(x)
inner_model = Model(x, y)
outer_model = Sequential()
outer_model.add(inner_model)
assert outer_model.trainable_weights == inner_model.trainable_weights
inner_model.trainable = False
assert outer_model.trainable_weights == []
inner_model.trainable = True
inner_model.layers[-1].trainable = False
assert outer_model.trainable_weights == []
if __name__ == '__main__':
pytest.main([__file__])
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