Add dynamic trainability lightweight test

tests/test_dynamic_trainability.py

@@ -0,0 +1,114 @@
+from __future__ import absolute_import
+from __future__ import print_function
+import pytest
+
+from keras.utils.test_utils import keras_test
+from keras.models import Model, Sequential
+from keras.layers import Dense, Input
+
+
+@keras_test
+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 == []
+
+
+@keras_test
+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 == []
+
+
+@keras_test
+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__])