def CreatRNN(cell_dim,
activation,
initial_state,
direction,
num_layers,
init=C.default_override_or(C.glorot_uniform()),
init_bias=C.default_override_or(0)):
if direction == 'bidirectional':
return C.layers.Sequential([
C.layers.For(range(num_layers), lambda i: [
(C.layers.Recurrence(C.layers.RNNStep(cell_dim,
activation = activation,
init = init,
init_bias = init_bias),
initial_state = initial_state,
return_full_state = False, go_backwards=False),
C.layers.Recurrence(C.layers.RNNStep(cell_dim, activation = activation,
init = init,
init_bias = init_bias),
initial_state = initial_state,
return_full_state = False, go_backwards=True)),
C.splice])])
else:
go_backward = False if direction == 'forward' else True
return C.layers.Sequential([
C.layers.For(range(num_layers), lambda i: [
C.layers.Recurrence(C.layers.RNNStep(cell_dim,
activation = activation,
init = init,
init_bias = init_bias),
initial_state = initial_state,
return_full_state = False, go_backwards=go_backward)])])
def CreatRNN(cell_dim,
activation,
initial_state,
direction,
num_layers,
init=C.default_override_or(C.glorot_uniform()),
init_bias=C.default_override_or(0)):
if direction == 'bidirectional':
return C.layers.Sequential([
C.layers.For(range(num_layers), lambda i: [
(C.layers.Recurrence(C.layers.RNNStep(cell_dim,
activation = activation,
init = init,
init_bias = init_bias),
initial_state = initial_state,
return_full_state = False, go_backwards=False),
C.layers.Recurrence(C.layers.RNNStep(cell_dim, activation = activation,
init = init,
init_bias = init_bias),
initial_state = initial_state,
return_full_state = False, go_backwards=True)),
C.splice])])
else:
go_backward = False if direction == 'forward' else True
return C.layers.Sequential([
C.layers.For(range(num_layers), lambda i: [
C.layers.Recurrence(C.layers.RNNStep(cell_dim,
activation = activation,
init = init,
init_bias = init_bias),
initial_state = initial_state,
return_full_state = False, go_backwards=go_backward)])])
def InstanceNormalization(
num_channel, initial_scale=1, initial_bias=0, epsilon=C.default_override_or(0.00001), name=''):
""" Instance Normalization (2016) """
epsilon = C.get_default_override(InstanceNormalization, epsilon=epsilon)
dtype = C.get_default_override(None, dtype=C.default_override_or(np.float32))
scale = C.Parameter(num_channel, init=initial_scale, name='scale')
bias = C.Parameter(num_channel, init=initial_bias, name='bias')
epsilon = np.asarray(epsilon, dtype=dtype)
@C.BlockFunction('InstanceNormalization', name)
def instance_normalization(x):
mean = C.reduce_mean(x, axis=(1, 2))
x0 = x - mean
std = C.sqrt(C.reduce_mean(x0 * x0, axis=(1, 2)))
if epsilon != 0:
std += epsilon
x_hat = x0 / std
return x_hat * C.reshape(scale, (-1, 1, 1)) + C.reshape(bias, (-1, 1, 1))
return instance_normalization
def Test(some_param=default_override_or(13)):
some_param = get_default_override(Test, some_param=some_param)
return some_param
def Test(some_param=default_override_or(13)):
some_param = get_default_override(Test, some_param=some_param)
return some_param
