def cnn_validate_base_model_creation(self, feature_model_name,
module_path):
#Arrange
base_model = BaseModel(feature_model_name, input_shape)
#Act & Assert
with mock_patch(module_path) as base_mock:
base_model._prepare_model = MagicMock()
base_model.cnn(dimensions)
base_mock.assert_called_once()
def cnn(base_model_name, input_shape, learning_rate, feature_dims):
"""It creates a convolutional network model using the input as a base model.
Arguments:
base_model_name {string} -- A string containing the name of a base model.
input_shape {(int, int, int))} -- A tuple to indicate the shape of inputs.
learning_rate {float} -- A float value to control speed of learning.
feature_dims {int} -- An integer indicating the dimensions of the feature vector.
Returns:
{A Model object} -- A keras model.
"""
#Base model
base_model = BaseModel(base_model_name, input_shape)
#Model
model = base_model.cnn(feature_dims)
#Create an optimizer object
adam_optimizer = Adam(lr = learning_rate)
#Compile the model
model.compile(loss = 'categorical_crossentropy', optimizer = adam_optimizer, metrics = ['categorical_accuracy'])
model.summary()
return model
def test_cnn_model(self):
#Arrange
base_model = BaseModel('inceptionv3', input_shape)
#Act
model = base_model.cnn(dimensions)
#Assert
self.assertIsNotNone(model)
self.assertTrue(model.layers[-1].name.startswith(
LayerSpecification.layer_prefixes[LayerType.Dense][1]))
self.assertTrue(model.layers[-2].name.startswith(
LayerSpecification.layer_prefixes[LayerType.Dropout][1]))