context = tensor.tensor3('upsampled')
mlp_context = MLP(activations=activations_context, dims=dims_context)
mlp_x = MLP(activations=activations_x, dims=dims_x)
feedback = DeepTransitionFeedback(mlp=mlp_x)
transition = LSTM(dim=hidden_size_recurrent)
mlp_theta = MLP(activations=activations_theta, dims=dims_theta)
mlp_gmm = GMMMLP(mlp=mlp_theta, dim=target_size, k=k, const=0.00001)
emitter = GMMEmitter(gmmmlp=mlp_gmm,
output_size=frame_size,
k=k,
name="emitter")
source_names = ['states']
readout = Readout(readout_dim=hidden_size_recurrent,
source_names=source_names,
emitter=emitter,
feedback_brick=feedback,
name="readout")
attention = SimpleSequenceAttention(state_names=source_names,
state_dims=[hidden_size_recurrent],
attended_dim=context_size)
generator = SequenceGenerator(readout=readout,
transition=transition,
def __init__(self,
batch_size,
frame_size,
k,
depth,
size,
**kwargs):
super(PyramidLayer, self).__init__(**kwargs)
target_size = frame_size * k
depth_x = depth
hidden_size_mlp_x = 32*size
depth_transition = depth-1
depth_theta = depth
hidden_size_mlp_theta = 32*size
hidden_size_recurrent = 32*size*3
depth_context = depth
hidden_size_mlp_context = 32*size
context_size = 32*size
activations_x = [Rectifier()]*depth_x
dims_x = [frame_size] + [hidden_size_mlp_x]*(depth_x-1) + \
[4*hidden_size_recurrent]
activations_theta = [Rectifier()]*depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
activations_context = [Rectifier()]*depth_context
dims_context = [frame_size] + [hidden_size_mlp_context]*(depth_context-1) + \
[context_size]
mlp_x = MLP(activations = activations_x,
dims = dims_x,
name = "mlp_x")
feedback = DeepTransitionFeedback(mlp = mlp_x)
transition = [GatedRecurrent(dim=hidden_size_recurrent,
use_bias = True,
name = "gru_{}".format(i) ) for i in range(depth_transition)]
transition = RecurrentStack( transition,
name="transition", skip_connections = True)
self.transition = transition
mlp_theta = MLP( activations = activations_theta,
dims = dims_theta,
name = "mlp_theta")
mlp_gmm = GMMMLP(mlp = mlp_theta,
dim = target_size,
k = k,
const = 0.00001,
name = "gmm_wrap")
gmm_emitter = GMMEmitter(gmmmlp = mlp_gmm,
output_size = frame_size, k = k)
source_names = [name for name in transition.apply.states if 'states' in name]
attention = SimpleSequenceAttention(
state_names = source_names,
state_dims = [hidden_size_recurrent],
attended_dim = context_size,
name = "attention")
#ipdb.set_trace()
# Verify source names
readout = Readout(
readout_dim = hidden_size_recurrent,
source_names =source_names + ['feedback'] + ['glimpses'],
emitter=gmm_emitter,
feedback_brick = feedback,
name="readout")
self.generator = SequenceGenerator(readout=readout,
transition=transition,
attention = attention,
name = "generator")
self.mlp_context = MLP(activations = activations_context,
dims = dims_context)
self.children = [self.generator, self.mlp_context]
self.final_states = []
transition = RecurrentStack(transition,
name="transition",
skip_connections=True)
mlp_theta = MLP(activations=activations_theta,
dims=dims_theta,
name="mlp_theta")
mlp_gmm = GMMMLP(mlp=mlp_theta,
dim=target_size,
k=k,
const=0.00001,
name="gmm_wrap")
gmm_emitter = GMMEmitter(gmmmlp=mlp_gmm, output_size=frame_size, k=k)
bricks = [mlp_x, transition, gmm_emitter]
for brick in bricks:
brick.weights_init = IsotropicGaussian(0.01)
brick.biases_init = Constant(0.)
brick.initialize()
##############
# Test model
##############
x_g = mlp_x.apply(x)
h = transition.apply(x_g)
mu, sigma, coeff = mlp_gmm.apply(h[-2])
def __init__(self,
batch_size,
frame_size,
k,
depth,
size,
**kwargs):
super(SimplePyramidLayer, self).__init__(**kwargs)
target_size = frame_size * k
depth_x = depth
hidden_size_mlp_x = 32*size
depth_transition = depth-1
depth_theta = depth
hidden_size_mlp_theta = 32*size
hidden_size_recurrent = 32*size*3
activations_x = [Rectifier()]*depth_x
dims_x = [frame_size] + [hidden_size_mlp_x]*(depth_x-1) + \
[4*hidden_size_recurrent]
activations_theta = [Rectifier()]*depth_theta
dims_theta = [hidden_size_recurrent] + \
[hidden_size_mlp_theta]*depth_theta
self.mlp_x = MLP(activations = activations_x,
dims = dims_x,
name = "mlp_x")
transition = [GatedRecurrent(dim=hidden_size_recurrent,
use_bias = True,
name = "gru_{}".format(i) ) for i in range(depth_transition)]
self.transition = RecurrentStack( transition,
name="transition", skip_connections = True)
mlp_theta = MLP( activations = activations_theta,
dims = dims_theta,
name = "mlp_theta")
mlp_gmm = GMMMLP(mlp = mlp_theta,
dim = target_size,
k = k,
const = 0.00001,
name = "gmm_wrap")
self.gmm_emitter = GMMEmitter(gmmmlp = mlp_gmm,
output_size = frame_size, k = k)
normal_inputs = [name for name in self.transition.apply.sequences
if 'mask' not in name]
self.fork = Fork(normal_inputs,
input_dim = 4*hidden_size_recurrent,
output_dims = self.transition.get_dims(normal_inputs))
self.children = [self.mlp_x, self.transition,
self.gmm_emitter, self.fork]