def test_train_models_on_samples_with_x_and_y(self):
"""
Model should be able to train using separated x and y values
"""
num_timesteps = 100
num_channels = 2
num_samples_train = 5
num_samples_val = 3
X_train = np.random.rand(num_samples_train, num_timesteps,
num_channels)
y_train = to_categorical(np.array([0, 0, 1, 1, 1]))
X_val = np.random.rand(num_samples_val, num_timesteps, num_channels)
y_val = to_categorical(np.array([0, 1, 1]))
batch_size = 20
custom_settings = get_default_settings()
model_type = CNN(X_train.shape, 2, **custom_settings)
hyperparams = model_type.generate_hyperparameters()
model = model_type.create_model(**hyperparams)
models = [(model, hyperparams, "CNN")]
histories, _, _ = \
find_architecture.train_models_on_samples(
X_train, y_train, X_val, y_val, models,
nr_epochs=1, subset_size=10, verbose=False,
outputfile=None, early_stopping_patience='auto',
batch_size=batch_size)
assert len(histories) == 1
def test_train_models_on_samples_with_dataset(self):
"""
Model should be able to train using a dataset as an input
"""
num_timesteps = 100
num_channels = 2
num_samples_train = 5
num_samples_val = 3
X_train = np.random.rand(num_samples_train, num_timesteps,
num_channels)
y_train = to_categorical(np.array([0, 0, 1, 1, 1]))
X_val = np.random.rand(num_samples_val, num_timesteps, num_channels)
y_val = to_categorical(np.array([0, 1, 1]))
batch_size = 20
data_train = tf.data.Dataset.from_tensor_slices(
(X_train, y_train)).batch(batch_size)
data_val = tf.data.Dataset.from_tensor_slices(
(X_val, y_val)).batch(batch_size)
custom_settings = get_default_settings()
model_type = CNN(X_train.shape, 2, **custom_settings)
hyperparams = model_type.generate_hyperparameters()
model = model_type.create_model(**hyperparams)
models = [(model, hyperparams, "CNN")]
histories, val_metrics, val_losses = \
find_architecture.train_models_on_samples(
data_train, None, data_val, None, models,
nr_epochs=1, subset_size=None, verbose=False,
outputfile=None, early_stopping_patience='auto',
batch_size=batch_size)
def test_train_models_on_samples_with_generators(self):
"""
Model should be able to train using a generator as an input
"""
num_timesteps = 100
num_channels = 2
num_samples_train = 5
num_samples_val = 3
X_train = np.random.rand(num_samples_train, num_timesteps,
num_channels)
y_train = to_categorical(np.array([0, 0, 1, 1, 1]))
X_val = np.random.rand(num_samples_val, num_timesteps, num_channels)
y_val = to_categorical(np.array([0, 1, 1]))
batch_size = 20
class DataGenerator(Sequence):
def __init__(self, x_set, y_set, batch_size):
self.x, self.y = x_set, y_set
self.batch_size = batch_size
def __len__(self):
return math.ceil(len(self.x) / self.batch_size)
def __getitem__(self, idx):
batch_x = self.x[idx * self.batch_size:(idx + 1) *
self.batch_size]
batch_y = self.y[idx * self.batch_size:(idx + 1) *
self.batch_size]
return batch_x, batch_y
data_train = DataGenerator(X_train, y_train, batch_size)
data_val = DataGenerator(X_val, y_val, batch_size)
custom_settings = get_default_settings()
model_type = CNN(X_train.shape, 2, **custom_settings)
hyperparams = model_type.generate_hyperparameters()
model = model_type.create_model(**hyperparams)
models = [(model, hyperparams, "CNN")]
histories, _, _ = \
find_architecture.train_models_on_samples(
data_train, None, data_val, None, models,
nr_epochs=1, subset_size=None, verbose=False,
outputfile=None, early_stopping_patience='auto',
batch_size=batch_size)
assert len(histories) == 1
