def test_IRVLayer_pickle():
n_tasks = 10
K = 10
V = Feature(shape=(None, 200))
irv_layer = IRVLayer(n_tasks, K, in_layers=[V])
irv_reg = IRVRegularize(irv_layer, in_layers=[irv_layer])
tg = TensorGraph()
tg.add_output(irv_layer)
tg.add_output(irv_reg)
tg.set_loss(irv_reg)
tg.build()
tg.save()
def test_IRV(self):
"""Test that IRVLayer and IRVRegularize can be invoked."""
batch_size = 10
n_tasks = 5
K = 10
n_features = 2 * K * n_tasks
test_tensor_input = np.random.rand(batch_size, n_features)
with self.session() as sess:
test_tensor = tf.convert_to_tensor(test_tensor_input, dtype=tf.float32)
irv_layer = IRVLayer(n_tasks, K)
irv_layer.create_tensor(in_layers=[test_tensor])
out_tensor = irv_layer.out_tensor
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_tasks)
irv_reg = IRVRegularize(irv_layer, 1.)()
assert irv_reg.eval() >= 0
def test_IRV(self):
"""Test that IRVLayer and IRVRegularize can be invoked."""
batch_size = 10
n_tasks = 5
K = 10
n_features = 2 * K * n_tasks
test_tensor_input = np.random.rand(batch_size, n_features)
with self.session() as sess:
test_tensor = tf.convert_to_tensor(test_tensor_input,
dtype=tf.float32)
irv_layer = IRVLayer(n_tasks, K)
irv_layer.create_tensor(in_layers=[test_tensor])
out_tensor = irv_layer.out_tensor
sess.run(tf.global_variables_initializer())
out_tensor = out_tensor.eval()
assert out_tensor.shape == (batch_size, n_tasks)
irv_reg = IRVRegularize(irv_layer, 1.)()
assert irv_reg.eval() >= 0