def test_layer_neunet_builder_fail_not_enough_layers(self):
l_nodes = [1]
layer_types = ["relu"]
with pytest.raises(NameError):
LayerNeuNetBuilder().set_weights_and_bias(l_nodes).set_layers(
layer_types).build()
def test_set_weitghts_and_bias(self):
l_nodes = [1, 2, 3, 4]
test_layerbuilder = LayerNeuNetBuilder().set_weights_and_bias(l_nodes)
assert test_layerbuilder.net.weights is not None
assert test_layerbuilder.net.bias is not None
assert test_layerbuilder.net.l_nodes is not None
assert test_layerbuilder.net.layers is not None
def _test_layernet(l_nodes=[10, 7, 5, 3],
layer_types="relu",
cost_type="exponentialcuadratic"):
if isinstance(layer_types, list):
layer_types_list = layer_types
else:
layer_types_list = [layer_types] * len(l_nodes)
test_layernet = LayerNeuNetBuilder() \
.set_layers(layer_types_list) \
.set_cost(cost_type) \
.set_weights_and_bias(l_nodes) \
.build()
return test_layernet
def test_build_success(self):
l_nodes = [1, 2, 3, 4]
cost_type = "cuadratic"
layer_types = ["relu"] * len(l_nodes)
test_layernet = LayerNeuNetBuilder(). \
set_weights_and_bias(l_nodes). \
set_layers(layer_types). \
set_cost(cost_type). \
build()
assert test_layernet.weights is not None
assert test_layernet.bias is not None
assert test_layernet.l_nodes is not None
assert test_layernet.layers is not None
assert test_layernet.layer_types is not None
assert test_layernet.cost_layer is not None
def test_layernet_constructor_not_defined(self):
with pytest.raises(NameError):
LayerNeuNetBuilder().build()
def test_set_exponentialcuadratic_cost(self):
cost_type = "exponentialcuadratic"
test_layernetbuilder = LayerNeuNetBuilder().set_cost(cost_type)
assert test_layernetbuilder.net.cost_layer is not None
def test_set_layers_success(self):
layer_types = ["relu", "sigmoid"]
test_layerbuilder = LayerNeuNetBuilder().set_layers(layer_types)
assert test_layerbuilder.net.layer_types is not None