def test_weighted_linear_combo(self):
"""Test that WeightedLinearCombo can be invoked."""
batch_size = 10
n_features = 5
in_tensor_1 = np.random.rand(batch_size, n_features)
in_tensor_2 = np.random.rand(batch_size, n_features)
with self.session() as sess:
in_tensor_1 = tf.convert_to_tensor(in_tensor_1, dtype=tf.float32)
in_tensor_2 = tf.convert_to_tensor(in_tensor_2, dtype=tf.float32)
out_tensor = WeightedLinearCombo()(in_tensor_1, in_tensor_2)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_features)
def test_weighted_linear_combo(self):
"""Test that WeightedLinearCombo can be invoked."""
batch_size = 10
n_features = 5
in_tensor_1 = np.random.rand(batch_size, n_features)
in_tensor_2 = np.random.rand(batch_size, n_features)
with self.session() as sess:
in_tensor_1 = tf.convert_to_tensor(in_tensor_1, dtype=tf.float32)
in_tensor_2 = tf.convert_to_tensor(in_tensor_2, dtype=tf.float32)
out_tensor = WeightedLinearCombo()(in_tensor_1, in_tensor_2)
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_features)
def test_weighted_combo(self):
"""Tests that weighted linear combinations can be built"""
N = 10
n_features = 5
X1 = NumpyDataset(np.random.rand(N, n_features))
X2 = NumpyDataset(np.random.rand(N, n_features))
y = NumpyDataset(np.random.rand(N))
features_1 = Feature(shape=(None, n_features))
features_2 = Feature(shape=(None, n_features))
labels = Label(shape=(None,))
combo = WeightedLinearCombo(in_layers=[features_1, features_2])
out = ReduceSum(in_layers=[combo], axis=1)
loss = ReduceSquareDifference(in_layers=[out, labels])
databag = Databag({features_1: X1, features_2: X2, labels: y})
tg = dc.models.TensorGraph(learning_rate=0.1, use_queue=False)
tg.set_loss(loss)
tg.fit_generator(databag.iterbatches(epochs=1))