def test_as_array_is_view(self):
b = Matrix(5, 4, 3)
a = b.as_array()
a[:] = 23.
self.assertEqual(b[0], 23.)
self.assertEqual(b[5], 23.)
self.assertEqual(b[12], 23.)
def test_gradient_finite_differences(self):
check_errors = []
for l, a in itertools.product(self.layer_types,
self.activation_functions):
net = self.build_network(l, a)
e, grad_calc, grad_approx = check_gradient(net,
n_batches=5,
n_timesteps=7,
rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(grad_approx)
print('$$$$$$$$$$$$ approx $$$$$$$$$$$')
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print('$$$$$$$$$$$$ calc $$$$$$$$$$$')
b = Matrix(grad_calc)
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print('$$$$$$$$$$$$ diff $$$$$$$$$$$')
b = Matrix(grad_approx - grad_calc)
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print('\n')
print("Checking Gradient of %s with %s = %0.4f" % (l(3), a, e))
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_construction_with_shape(self):
b = Matrix(2, 3, 4)
self.assertEqual(len(b), 2 * 3 * 4)
self.assertEqual(b.get_feature_size(), 4)
self.assertEqual(b.get_batch_size(), 3)
self.assertEqual(b.get_time_size(), 2)
self.assertEqual(b.shape(), (2, 3, 4))
def test_as_array_is_view(self):
b = Matrix(5, 4, 3)
a = b.as_array()
a[:] = 23.
self.assertEqual(b[0], 23.)
self.assertEqual(b[5], 23.)
self.assertEqual(b[12], 23.)
def test_gradient_finite_differences(self):
e, grad_calc, grad_approx = check_gradient(self.net,
n_batches=self.batch_size,
n_timesteps=self.timesteps,
rnd=rnd)
# construct a weight view and break down the differences
layer = self.net.layers.values()[1] # the only layer
a = Matrix(grad_approx)
b = Matrix(grad_approx - grad_calc)
c = Matrix(grad_calc)
diff = layer.create_param_view(b)
approx = layer.create_param_view(a)
calc = layer.create_param_view(c)
E = 0.0
for n, q in diff.items():
if n == 'Timing':
continue
print("====== %s ======" % n)
print("Calculated:")
print(calc[n])
print("Approx:")
print(approx[n])
print("Difference:")
print(q)
err = np.sum(q**2) / self.batch_size
print(err)
E += err
print("Checking Gradient of ClockworkLayer with sigmoid = %0.4f" % E)
self.assertTrue(E < 1e-6)
def test_memory_management1(self):
b = Matrix(10)
a = b.as_array()
del b
gc.collect()
a[1] = 1
del a
gc.collect()
def test_memory_management1(self):
b = Matrix(10)
a = b.as_array()
del b
gc.collect()
a[1] = 1
del a
gc.collect()
def test_slicing_3dview_has_correct_shapes(self):
b = Matrix(11, 9, 7)
self.assertEqual(b[1:3].shape(), (2, 9, 7))
self.assertEqual(b[3:11].shape(), (8, 9, 7))
self.assertEqual(b[:3].shape(), (3, 9, 7))
self.assertEqual(b[5:].shape(), (6, 9, 7))
self.assertEqual(b[:].shape(), (11, 9, 7))
def test_gradient_forked_architecture(self):
check_errors = []
in_layer = InputLayer(self.input_size)
out_layer = ForwardLayer(self.output_size)
in_layer >> ForwardLayer(3, name='A') >> out_layer
in_layer >> ForwardLayer(2, name='B') >> out_layer
net = build_net(out_layer)
net.initialize(Gaussian(0.1))
e, grad_calc, grad_approx = check_gradient(net,
n_batches=5,
n_timesteps=7,
rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(grad_approx - grad_calc)
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print("Checking Gradient of forked architecture = %0.4f" % e)
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_lstm_forward_pass_insensitive_to_fwd_state(self):
net = self.build_network(LstmLayer, "tanh")
out1 = net.forward_pass(self.X).copy()
net.fwd_state_manager.initialize_buffer(
Matrix(rnd.randn(net.fwd_state_manager.calculate_size())))
out2 = net.forward_pass(self.X).copy()
self.assertTrue(np.allclose(out1, out2))
def test_lstm_backward_pass_insensitive_to_bwd_state(self):
net = self.build_network(LstmLayer, "tanh")
net.clear_internal_state()
out1 = net.forward_pass(self.X).copy()
targets = create_targets_object(np.zeros_like(out1))
deltas1 = net.backward_pass(targets).copy()
bwstate1 = net.get_bwd_state_for('LstmLayer')
b1 = {}
for h in bwstate1.keys():
b1[h] = bwstate1[h].copy()
net.bwd_state_manager.initialize_buffer(
Matrix(rnd.randn(net.bwd_state_manager.calculate_size())))
net.forward_pass(self.X).copy()
deltas2 = net.backward_pass(targets).copy()
bwstate2 = net.get_bwd_state_for('LstmLayer')
b2 = {}
for h in bwstate2.keys():
b2[h] = bwstate2[h].copy()
for b in b2:
print(b)
print(b1[b] - b2[b])
self.assertTrue(np.allclose(deltas1, deltas2))
def test_item_access(self):
b = Matrix(3)
b[0] = 5
b[1] = 7
b[2] = 10
self.assertEqual(b[0], 5)
self.assertEqual(b[1], 7)
self.assertEqual(b[2], 10)
def test_slicing_view_has_correct_values(self):
b = Matrix(range(6))
v = b[1:3]
self.assertEqual(v[0], 1)
self.assertEqual(v[1], 2)
v = b[4:6]
self.assertEqual(v[0], 4)
self.assertEqual(v[1], 5)
def test_memory_management2(self):
a = np.array([[[1, 2, 3]]])
b = Matrix(a)
del a
gc.collect()
c = b[1]
del c
del b
gc.collect()
def test_slicing_gives_a_view(self):
b = Matrix(10)
v = b[1:2]
self.assertEqual(len(v), 1)
v = b[0:2]
self.assertEqual(len(v), 2)
v = b[0:10]
self.assertEqual(len(v), 10)
v = b[4:7]
self.assertEqual(len(v), 3)
def test_construction_with_shape(self):
b = Matrix(2, 3, 4)
self.assertEqual(len(b), 2 * 3 * 4)
self.assertEqual(b.get_feature_size(), 4)
self.assertEqual(b.get_batch_size(), 3)
self.assertEqual(b.get_time_size(), 2)
self.assertEqual(b.shape(), (2, 3, 4))
def test_gradient_finite_differences(self):
check_errors = []
for cfg in self.lstm_configs:
net = self.build_network(cfg)
e, grad_calc, grad_approx = check_gradient(net, n_batches=10,
n_timesteps=10, rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(grad_approx - grad_calc)
a = Matrix(grad_approx)
c = Matrix(grad_calc)
# appr = layer.create_param_view(a)
# calc = layer.create_param_view(c)
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
# print(appr[n])
# print(calc[n])
print(q)
print("Checking Gradient of Lstm97 with %s = %0.4f" % (cfg, e))
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_rforward_finite_differences(self):
check_errors = []
for cfg in self.lstm_configs:
net = self.build_network(cfg)
e, allerrors = check_rpass(net, n_batches=5, n_timesteps=2, rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(allerrors.copy())
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print("Checking RForward pass of Lstm97 with %s = %0.4g" % (cfg, e))
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_lwta_gradient_finite_differences(self):
check_errors = []
for a in self.activation_functions:
net = self.build_lwta_network(8, a)
e, grad_calc, grad_approx = check_gradient(net,
n_batches=5,
n_timesteps=7,
rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(grad_approx - grad_calc)
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print("Checking Gradient of %s with LWTA = %0.4f" % (a, e))
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_staticlstm_gradient_finite_differences(self):
t = 7
b = 5
check_errors = []
net = self.build_staticlstm_network(3, 'sigmoid')
e, grad_calc, grad_approx = check_gradient(net,
n_batches=b,
n_timesteps=t,
rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(grad_approx - grad_calc)
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
# print("Checking Gradient of %s with %s = %0.4f" % (l(3), a, e))
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_rforward_finite_differences_multilayer(self):
check_errors = []
for l, a in itertools.product(self.layer_types,
self.activation_functions):
net = self.build_network(l, a, layers=2)
e, allerrors = check_rpass(net,
n_batches=5,
n_timesteps=7,
rnd=rnd)
check_errors.append(e)
if e > 1e-4:
# construct a weight view and break down the differences
layer = net.layers.values()[1] # the only layer
b = Matrix(allerrors.copy())
diff = layer.create_param_view(b)
for n, q in diff.items():
print("====== %s ======" % n)
print(q)
print("Checking RForward pass of %s with %s = %0.4g" %
(l(3), a, e))
self.assertTrue(np.all(np.array(check_errors) < 1e-4))
def test_reshape_view_with_negative_values_raises_if_indivisible(self):
b = Matrix(12, 2, 1)
self.assertRaises(AssertionError, b.reshape, -1, 7, 2)
def test_reshape_view_has_correct_shape(self):
b = Matrix(12, 2, 1)
self.assertEqual(b.reshape(4, 3, 2).shape(), (4, 3, 2))
self.assertEqual(b.reshape(1, 1, 24).shape(), (1, 1, 24))
self.assertEqual(b.reshape(1, 6, 4).shape(), (1, 6, 4))
def test_reshape_view_with_negative_value_has_correct_shape(self):
b = Matrix(12, 2, 1)
self.assertEqual(b.reshape(-1, 3, 2).shape(), (4, 3, 2))
self.assertEqual(b.reshape(1, 1, -1).shape(), (1, 1, 24))
self.assertEqual(b.reshape(1, -1, 4).shape(), (1, 6, 4))
def test_as_array(self):
b = Matrix(5, 4, 3)
a = b.as_array()
self.assertEqual(a.shape, (5, 4, 3))
self.assertTrue(np.all(a == 0.))
def test_construction_with_size(self):
for i in range(1, 11):
b = Matrix(i)
self.assertEqual(len(b), i)
for j in range(i):
self.assertEqual(b[j], 0.0)
def test_construction_with_3d_nparray(self):
a = np.array([[[1, 2, 3], [4, 4, 4]], [[1, 2, 3], [4, 4, 4]]])
b = Matrix(a)
self.assertEqual(len(b), len(a.flatten()))
for i in range(len(b)):
self.assertEqual(b[i], a.flatten()[i])
def test_construction_with_1d_nparray(self):
a = np.array([1, 2, 3, 3, 2, 3])
b = Matrix(a)
self.assertEqual(len(b), len(a))
for i in range(len(a)):
self.assertEqual(b[i], a[i])
def test_reshape_view_has_correct_shape(self):
b = Matrix(12, 2, 1)
self.assertEqual(b.reshape(4, 3, 2).shape(), (4, 3, 2))
self.assertEqual(b.reshape(1, 1, 24).shape(), (1, 1, 24))
self.assertEqual(b.reshape(1, 6, 4).shape(), (1, 6, 4))
def test_reshape_view_with_negative_value_has_correct_shape(self):
b = Matrix(12, 2, 1)
self.assertEqual(b.reshape(-1, 3, 2).shape(), (4, 3, 2))
self.assertEqual(b.reshape(1, 1, -1).shape(), (1, 1, 24))
self.assertEqual(b.reshape(1, -1, 4).shape(), (1, 6, 4))
def test_reshape_view_with_multiple_negative_values_raises(self):
b = Matrix(12, 2, 1)
self.assertRaises(AssertionError, b.reshape, -1, -1, 1)
self.assertRaises(AssertionError, b.reshape, -1, 4, -1)
self.assertRaises(AssertionError, b.reshape, 6, -1, -1)
def test_as_array(self):
b = Matrix(5, 4, 3)
a = b.as_array()
self.assertEqual(a.shape, (5, 4, 3))
self.assertTrue(np.all(a == 0.))
