def test_keras_model_train():
model = Mock()
layer = Mock
criterion = Mock()
optimizer = Mock()
metrics = Mock()
sizes = [(1, 24, 24), (24, 10)]
l1 = layer()
l1.get_input_shape_at.return_value = sizes[0]
l2 = layer()
l2.get_output_shape_at.return_value = sizes[1]
model.layers = [l1, l2]
batch = 32
epoch = 2
kdpm = DeepLearningModel(model, criterion, optimizer, batch, epoch, metrics)
num_data = 30
data = np.array([np.random.rand(24, 24) for i in range(num_data)])
labels = np.array([np.zeros(10) for i in range(num_data)])
for l in labels:
l[np.random.randint(0, len(l))] = 1
kdpm.train(data, labels)
kdpm._model.fit.assert_called_once()
params = kdpm._model.fit.call_args_list[0][1]
assert np.array_equal(params['x'], data)
assert np.array_equal(params['y'], labels)
assert params['batch_size'] == batch
assert params['epochs'] == epoch
def test_train_wrong_data():
model = Mock()
layer = Mock
criterion = Mock()
optimizer = Mock()
metrics = Mock()
sizes = [(30, 24, 24), (24, 10)]
l1 = layer()
l1.get_input_shape_at.return_value = sizes[0]
l2 = layer()
l2.get_output_shape_at.return_value = sizes[1]
model.layers = [l1, l2]
batch = 32
epoch = 2
kdpl = DeepLearningModel(model, criterion, optimizer, batch, epoch, metrics)
num_data = 30
data = np.array([np.random.rand(16, 16) for i in range(num_data)])
label = np.array([np.zeros(10) for i in range(num_data)])
for l in label:
l[np.random.randint(0, len(l))] = 1
with pytest.raises(AssertionError):
kdpl.train(data, label)
data = np.array([np.random.rand(24, 24) for i in range(num_data)])
label = np.array([np.zeros(8) for i in range(num_data)])
for l in label:
l[np.random.randint(0, len(l))] = 1
with pytest.raises(AssertionError):
kdpl.train(data, label)