def test_to_float(self):
"""Test that ToFloat can be invoked."""
batch_size = 10
n_features = 5
in_tensor = np.random.rand(batch_size, n_features)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = ToFloat()(in_tensor)
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_features)
def test_to_float(self):
"""Test that ToFloat can be invoked."""
batch_size = 10
n_features = 5
in_tensor = np.random.rand(batch_size, n_features)
with self.session() as sess:
in_tensor = tf.convert_to_tensor(in_tensor, dtype=tf.float32)
out_tensor = ToFloat()(in_tensor)
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_features)
def test_neighbor_list_vina(self):
"""Test under conditions closer to Vina usage."""
N_atoms = 5
M_nbrs = 2
ndim = 3
start = 0
stop = 4
nbr_cutoff = 1
X = NumpyDataset(start + np.random.rand(N_atoms, ndim) * (stop - start))
coords = Feature(shape=(N_atoms, ndim))
# Now an (N, M) shape
nbr_list = NeighborList(
N_atoms, M_nbrs, ndim, nbr_cutoff, start, stop, in_layers=[coords])
nbr_list = ToFloat(in_layers=[nbr_list])
flattened = Flatten(in_layers=[nbr_list])
dense = Dense(out_channels=1, in_layers=[flattened])
output = ReduceSum(in_layers=[dense])
tg = dc.models.TensorGraph(learning_rate=0.1, use_queue=False)
tg.set_loss(output)
databag = Databag({coords: X})
tg.fit_generator(databag.iterbatches(epochs=1))
def test_neighbor_list_simple(self):
"""Test that neighbor lists can be constructed."""
N_atoms = 10
start = 0
stop = 12
nbr_cutoff = 3
ndim = 3
M = 6
X = np.random.rand(N_atoms, ndim)
y = np.random.rand(N_atoms, 1)
dataset = NumpyDataset(X, y)
features = Feature(shape=(N_atoms, ndim))
labels = Label(shape=(N_atoms, ))
nbr_list = NeighborList(N_atoms,
M,
ndim,
nbr_cutoff,
start,
stop,
in_layers=[features])
nbr_list = ToFloat(in_layers=[nbr_list])
# This isn't a meaningful loss, but just for test
loss = ReduceSum(in_layers=[nbr_list])
tg = dc.models.TensorGraph(use_queue=False)
tg.add_output(nbr_list)
tg.set_loss(loss)
tg.build()
def test_ToFloat_pickle(): tg = TensorGraph() feature = Feature(shape=(tg.batch_size, 1)) layer = ToFloat(in_layers=[feature]) tg.add_output(layer) tg.set_loss(layer) tg.build() tg.save()