def test_l2_loss_pytorch(self):
"""Test L2Loss."""
loss = losses.L2Loss()
outputs = torch.tensor([[0.1, 0.8], [0.4, 0.6]])
labels = torch.tensor([[0.0, 1.0], [1.0, 0.0]])
result = loss._create_pytorch_loss()(outputs, labels).numpy()
expected = [[0.1**2, 0.2**2], [0.6**2, 0.6**2]]
assert np.allclose(expected, result)
def test_l2_loss_tf(self):
"""Test L2Loss."""
loss = losses.L2Loss()
outputs = tf.constant([[0.1, 0.8], [0.4, 0.6]])
labels = tf.constant([[0.0, 1.0], [1.0, 0.0]])
result = loss._compute_tf_loss(outputs, labels).numpy()
expected = [[0.1**2, 0.2**2], [0.6**2, 0.6**2]]
assert np.allclose(expected, result)
def test_gat_classification():
# load datasets
featurizer = MolGraphConvFeaturizer()
tasks, dataset, transformers, metric = get_dataset('regression',
featurizer=featurizer)
# initialize models
n_tasks = len(tasks)
model = GATModel(n_tasks=n_tasks,
loss=losses.L2Loss(),
batch_size=4,
learning_rate=0.001)
# overfit test
model.fit(dataset, nb_epoch=100)
scores = model.evaluate(dataset, [metric], transformers)
# TODO: check this asseration is correct or not
assert scores['mean_absolute_error'] < 1.0
def test_cgcnn():
# load datasets
current_dir = path.dirname(path.abspath(__file__))
config = {
"reload": False,
"featurizer": CGCNNFeaturizer,
# disable transformer
"transformers": [],
# load 'deepchem/models/test/perovskite.tar.gz'
"data_dir": current_dir
}
tasks, datasets, transformers = load_perovskite(**config)
train, valid, test = datasets
# initialize models
n_tasks = 1
model = CGCNNModel(in_node_dim=92,
hidden_node_dim=64,
in_edge_dim=41,
num_conv=3,
predicator_hidden_feats=128,
n_tasks=n_tasks,
loss=losses.L2Loss(),
batch_size=32,
learning_rate=0.001)
# check train
model.fit(train, nb_epoch=50)
# check predict
valid_preds = model.predict_on_batch(valid.X)
assert valid_preds.shape == (10, n_tasks)
test_preds = model.predict(test)
assert test_preds.shape == (10, n_tasks)
# eval model on test
regression_metric = Metric(mae_score, n_tasks=n_tasks)
scores = model.evaluate(test, [regression_metric])
assert scores[regression_metric.name] < 1.0
if path.exists(path.join(current_dir, 'perovskite.json')):
remove(path.join(current_dir, 'perovskite.json'))
