diff --git a/test/test_model_modules/test_abstract_model_class.py b/test/test_model_modules/test_abstract_model_class.py
index a1ec4c63a2b3b44c26bbf722a3d4d84aec112bec..2a1578aa28c061fce40be2e3f2f2a29306663463 100644
--- a/test/test_model_modules/test_abstract_model_class.py
+++ b/test/test_model_modules/test_abstract_model_class.py
@@ -147,16 +147,16 @@ class TestAbstractModelClass:
with pytest.raises(ValueError) as einfo:
amc.compile_options = {"optimizer": keras.optimizers.Adam()}
assert "Got different values or arguments for same argument: self.optimizer=<class" \
- " 'tensorflow.python.keras.optimizer_v2.gradient_descent.SGD'> and " \
- "'optimizer': <class 'tensorflow.python.keras.optimizer_v2.adam.Adam'>" in str(einfo.value)
+ " 'keras.optimizer_v2.gradient_descent.SGD'> and " \
+ "'optimizer': <class 'keras.optimizer_v2.adam.Adam'>" in str(einfo.value)
def test_compile_options_setter_as_mix_attr_dict_invalid_duplicates_same_optimizer_other_args(self, amc):
amc.optimizer = keras.optimizers.SGD(lr=0.1)
with pytest.raises(ValueError) as einfo:
amc.compile_options = {"optimizer": keras.optimizers.SGD(lr=0.001)}
assert "Got different values or arguments for same argument: self.optimizer=<class" \
- " 'tensorflow.python.keras.optimizer_v2.gradient_descent.SGD'> and " \
- "'optimizer': <class 'tensorflow.python.keras.optimizer_v2.gradient_descent.SGD'>" in str(einfo.value)
+ " 'keras.optimizer_v2.gradient_descent.SGD'> and " \
+ "'optimizer': <class 'keras.optimizer_v2.gradient_descent.SGD'>" in str(einfo.value)
def test_compile_options_setter_as_dict_invalid_keys(self, amc):
with pytest.raises(ValueError) as einfo:
diff --git a/test/test_model_modules/test_flatten_tail.py b/test/test_model_modules/test_flatten_tail.py
index 83861be561fbe164d09048f1b748b51977b2fc27..8a858b438357ff3b39834fb0aea01967df8f2f9c 100644
--- a/test/test_model_modules/test_flatten_tail.py
+++ b/test/test_model_modules/test_flatten_tail.py
@@ -27,7 +27,7 @@ class TestGetActivation:
def test_layer_act(self, model_input):
x_in = get_activation(model_input, activation=ELU, name='adv_layer')
act = x_in._keras_history[0]
- assert act.name == 'adv_layer'
+ assert act.name == 'tf.nn.elu'
def test_layer_act_invalid(self, model_input):
with pytest.raises(TypeError) as einfo:
@@ -62,8 +62,8 @@ class TestFlattenTail:
assert final_dense.units == 2
assert final_dense.kernel_regularizer is None
inner_act = self.step_in(final_dense)
- assert inner_act.name == 'Main_tail_act'
- assert inner_act.__class__.__name__ == 'ELU'
+ assert inner_act.name == 'tf.nn.elu'
+ assert inner_act.__class__.__name__ == 'TFOpLambda'
inner_dense = self.step_in(inner_act)
assert inner_dense.name == 'Main_tail_inner_Dense'
assert inner_dense.units == 64
@@ -112,9 +112,8 @@ class TestFlattenTail:
'dtype': 'float32', 'data_format': 'channels_last'}
reduc_act = self.step_in(flatten)
- assert reduc_act.get_config() == {'name': 'Main_tail_all_conv_act', 'trainable': True,
- 'dtype': 'float32', 'alpha': 1.0}
-
+ assert reduc_act.get_config() == {'name': 'tf.nn.elu_2', 'trainable': True, 'function': 'nn.elu',
+ 'dtype': 'float32'}
reduc_conv = self.step_in(reduc_act)
assert reduc_conv.kernel_size == (1, 1)
diff --git a/test/test_model_modules/test_inception_model.py b/test/test_model_modules/test_inception_model.py
index 0ed975d054841d9d4cfb8b4c964fa0cd2d4e2667..0a0dd38fa9d354c1243127df1ae9e079a6ca88e9 100644
--- a/test/test_model_modules/test_inception_model.py
+++ b/test/test_model_modules/test_inception_model.py
@@ -43,7 +43,7 @@ class TestInceptionModelBase:
assert base.part_of_block == 1
assert tower.name == 'Block_0a_act_2/Relu:0'
act_layer = tower._keras_history[0]
- assert isinstance(act_layer, ReLU)
+ assert isinstance(act_layer, keras.layers.ReLU)
assert act_layer.name == "Block_0a_act_2"
# check previous element of tower (conv2D)
conv_layer = self.step_in(act_layer)
@@ -60,7 +60,7 @@ class TestInceptionModelBase:
assert pad_layer.name == 'Block_0a_Pad'
# check previous element of tower (activation)
act_layer2 = self.step_in(pad_layer)
- assert isinstance(act_layer2, ReLU)
+ assert isinstance(act_layer2, keras.layers.ReLU)
assert act_layer2.name == "Block_0a_act_1"
# check previous element of tower (conv2D)
conv_layer2 = self.step_in(act_layer2)
@@ -80,7 +80,7 @@ class TestInceptionModelBase:
# assert tower.name == 'Block_0a_act_2/Relu:0'
assert tower.name == 'Block_0a_act_2/Relu:0'
act_layer = tower._keras_history[0]
- assert isinstance(act_layer, ReLU)
+ assert isinstance(act_layer, keras.layers.ReLU)
assert act_layer.name == "Block_0a_act_2"
# check previous element of tower (batch_normal)
batch_layer = self.step_in(act_layer)
@@ -101,7 +101,7 @@ class TestInceptionModelBase:
assert pad_layer.name == 'Block_0a_Pad'
# check previous element of tower (activation)
act_layer2 = self.step_in(pad_layer)
- assert isinstance(act_layer2, ReLU)
+ assert isinstance(act_layer2, keras.layers.ReLU)
assert act_layer2.name == "Block_0a_act_1"
# check previous element of tower (conv2D)
conv_layer2 = self.step_in(act_layer2)
@@ -124,7 +124,7 @@ class TestInceptionModelBase:
tower = base.create_conv_tower(activation=keras.layers.LeakyReLU, **opts)
assert tower.name == 'Block_0b_act_2/LeakyRelu:0'
act_layer = tower._keras_history[0]
- assert isinstance(act_layer, LeakyReLU)
+ assert isinstance(act_layer, keras.layers.LeakyReLU)
assert act_layer.name == "Block_0b_act_2"
def test_create_conv_tower_1x1(self, base, input_x):
@@ -134,7 +134,7 @@ class TestInceptionModelBase:
assert base.part_of_block == 1
assert tower.name == 'Block_0a_act_1/Relu:0'
act_layer = tower._keras_history[0]
- assert isinstance(act_layer, ReLU)
+ assert isinstance(act_layer, keras.layers.ReLU)
assert act_layer.name == "Block_0a_act_1"
# check previous element of tower (conv2D)
conv_layer = self.step_in(act_layer)
@@ -160,7 +160,7 @@ class TestInceptionModelBase:
assert base.part_of_block == 1
assert tower.name == 'Block_0a_act_1/Relu:0'
act_layer = tower._keras_history[0]
- assert isinstance(act_layer, ReLU)
+ assert isinstance(act_layer, keras.layers.ReLU)
assert act_layer.name == "Block_0a_act_1"
# check previous element of tower (conv2D)
conv_layer = self.step_in(act_layer)