def test_meansquarederror():
confs = itertools.product(batch_sizes, n_ins)
for batch_size, n_in in confs:
print('MeanSquaredError: batch_size=%i, n_in=%i' % (batch_size, n_in))
x_shape = (batch_size, n_in)
x = np.random.normal(size=x_shape)
y = np.random.normal(size=x_shape)
loss = dp.MeanSquaredError()
loss._setup(x_shape)
assert loss.loss(ca.array(x), ca.array(y)).shape == x_shape[:1]
check_grad(loss, x, y)
weight_decay=0.004,
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Convolutional(
n_filters=64,
filter_shape=(5, 5),
border_mode='same',
weights=dp.Parameter(dp.NormalFiller(sigma=0.01),
weight_decay=0.004,
monitor=True),
),
dp.Activation('relu'),
dp.Pool(**pool_kwargs),
dp.Flatten(),
dp.FullyConnected(
n_output=64,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
weight_decay=0.004,
monitor=True),
),
dp.Activation('relu'),
dp.FullyConnected(
n_output=6,
weights=dp.Parameter(dp.NormalFiller(sigma=0.1),
weight_decay=0.004,
monitor=True),
),
dp.MeanSquaredError(),
], )