def __init__(self, dim, activation=None, depth=2, name=None,
lstm_name=None, **kwargs):
super(LSTMstack, self).__init__(name=name, **kwargs)
# use the name allready processed by superclass
name = self.name
self.dim = dim
self.children = []
self.depth = depth
for d in range(self.depth):
layer_node = LSTM(dim, activation, name=lstm_name)
layer_node.name = '%s_%s_%d'%(name, layer_node.name, d)
if d > 0:
# convert states of previous layer to inputs of new layer
layer_name = '%s_%d_%d'%(name, d-1, d)
input_dim = layer_node.get_dim('inputs')
self.children.append(Linear(dim, input_dim,
use_bias=True,
name=layer_name))
self.children.append(layer_node)
class Model(Initializable):
@lazy()
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, 2], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
@application
def initial_state(self, *args, **kwargs):
return self.rec.initial_state(*args, **kwargs)
@recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'], sequences=['latitude', 'longitude', 'latitude_mask'])
def predict_all(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude - data.train_gps_mean[1]) / data.train_gps_std[1]
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
latitude = tensor.shape_padright(latitude)
longitude = tensor.shape_padright(longitude)
itr = self.input_to_rec.apply(tensor.concatenate(pre_emb + (latitude, longitude), axis=1))
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=latitude_mask, iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1))
rto = (rto * data.train_gps_std) + data.train_gps_mean
return (rto, next_states, next_cells)
@predict_all.property('contexts')
def predict_all_inputs(self):
return self.context
@application(outputs=['destination'])
def predict(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
return res[-1]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(latitude.shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
ce = ce.reshape(latitude.shape)
return ce * latitude_mask
@cost_matrix.property('inputs')
def cost_matrix_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def cost(self, latitude_mask, **kwargs):
return self.cost_matrix(latitude_mask=latitude_mask, **kwargs).sum() / latitude_mask.sum()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def valid_cost(self, **kwargs):
# Only works when batch_size is 1.
return self.cost_matrix(**kwargs)[-1,0]
@valid_cost.property('inputs')
def valid_cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
class Model(Initializable):
@lazy()
def __init__(self, config, **kwargs):
super(Model, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(
config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(
config.post_embedder, name='post_context_embedder')
in1 = 2 + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()],
dims=[in1, config.hidden_state_dim],
name='input_to_rec')
self.rec = LSTM(dim=config.hidden_state_dim, name='recurrent')
in2 = config.hidden_state_dim + sum(
x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()],
dims=[in2, 2],
name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [
self.pre_context_embedder, self.post_context_embedder,
self.input_to_rec, self.rec, self.rec_to_output
]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim, ),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim, ),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
@application
def initial_state(self, *args, **kwargs):
return self.rec.initial_state(*args, **kwargs)
@recurrent(states=['states', 'cells'],
outputs=['destination', 'states', 'cells'],
sequences=['latitude', 'longitude', 'latitude_mask'])
def predict_all(self, latitude, longitude, latitude_mask, **kwargs):
latitude = (latitude - data.train_gps_mean[0]) / data.train_gps_std[0]
longitude = (longitude -
data.train_gps_mean[1]) / data.train_gps_std[1]
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
latitude = tensor.shape_padright(latitude)
longitude = tensor.shape_padright(longitude)
itr = self.input_to_rec.apply(
tensor.concatenate(pre_emb + (latitude, longitude), axis=1))
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr,
kwargs['states'],
kwargs['cells'],
mask=latitude_mask,
iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(
tensor.concatenate(post_emb + (next_states, ), axis=1))
rto = (rto * data.train_gps_std) + data.train_gps_mean
return (rto, next_states, next_cells)
@predict_all.property('contexts')
def predict_all_inputs(self):
return self.context
@application(outputs=['destination'])
def predict(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
return res[-1]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, latitude, longitude, latitude_mask, **kwargs):
latitude = latitude.T
longitude = longitude.T
latitude_mask = latitude_mask.T
res = self.predict_all(latitude, longitude, latitude_mask, **kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(latitude.shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
ce = ce.reshape(latitude.shape)
return ce * latitude_mask
@cost_matrix.property('inputs')
def cost_matrix_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def cost(self, latitude_mask, **kwargs):
return self.cost_matrix(latitude_mask=latitude_mask, **
kwargs).sum() / latitude_mask.sum()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def valid_cost(self, **kwargs):
# Only works when batch_size is 1.
return self.cost_matrix(**kwargs)[-1, 0]
@valid_cost.property('inputs')
def valid_cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
class RNN(Initializable):
@lazy()
def __init__(self, config, rec_input_len=2, output_dim=2, **kwargs):
super(RNN, self).__init__(**kwargs)
self.config = config
self.pre_context_embedder = ContextEmbedder(config.pre_embedder, name='pre_context_embedder')
self.post_context_embedder = ContextEmbedder(config.post_embedder, name='post_context_embedder')
in1 = rec_input_len + sum(x[2] for x in config.pre_embedder.dim_embeddings)
self.input_to_rec = MLP(activations=[Tanh()], dims=[in1, config.hidden_state_dim], name='input_to_rec')
self.rec = LSTM(
dim = config.hidden_state_dim,
name = 'recurrent'
)
in2 = config.hidden_state_dim + sum(x[2] for x in config.post_embedder.dim_embeddings)
self.rec_to_output = MLP(activations=[Tanh()], dims=[in2, output_dim], name='rec_to_output')
self.sequences = ['latitude', 'latitude_mask', 'longitude']
self.context = self.pre_context_embedder.inputs + self.post_context_embedder.inputs
self.inputs = self.sequences + self.context
self.children = [ self.pre_context_embedder, self.post_context_embedder, self.input_to_rec, self.rec, self.rec_to_output ]
self.initial_state_ = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_state")
self.initial_cells = shared_floatx_zeros((config.hidden_state_dim,),
name="initial_cells")
def _push_initialization_config(self):
for mlp in [self.input_to_rec, self.rec_to_output]:
mlp.weights_init = self.config.weights_init
mlp.biases_init = self.config.biases_init
self.rec.weights_init = self.config.weights_init
def get_dim(self, name):
return self.rec.get_dim(name)
def process_rto(self, rto):
return rto
def rec_input(self, latitude, longitude, **kwargs):
return (tensor.shape_padright(latitude), tensor.shape_padright(longitude))
@recurrent(states=['states', 'cells'], outputs=['destination', 'states', 'cells'])
def predict_all(self, **kwargs):
pre_emb = tuple(self.pre_context_embedder.apply(**kwargs))
itr_in = tensor.concatenate(pre_emb + self.rec_input(**kwargs), axis=1)
itr = self.input_to_rec.apply(itr_in)
itr = itr.repeat(4, axis=1)
(next_states, next_cells) = self.rec.apply(itr, kwargs['states'], kwargs['cells'], mask=kwargs['latitude_mask'], iterate=False)
post_emb = tuple(self.post_context_embedder.apply(**kwargs))
rto = self.rec_to_output.apply(tensor.concatenate(post_emb + (next_states,), axis=1))
rto = self.process_rto(rto)
return (rto, next_states, next_cells)
@predict_all.property('sequences')
def predict_all_sequences(self):
return self.sequences
@application(outputs=predict_all.states)
def initial_states(self, *args, **kwargs):
return self.rec.initial_states(*args, **kwargs)
@predict_all.property('contexts')
def predict_all_context(self):
return self.context
def before_predict_all(self, kwargs):
kwargs['latitude'] = (kwargs['latitude'].T - data.train_gps_mean[0]) / data.train_gps_std[0]
kwargs['longitude'] = (kwargs['longitude'].T - data.train_gps_mean[1]) / data.train_gps_std[1]
kwargs['latitude_mask'] = kwargs['latitude_mask'].T
@application(outputs=['destination'])
def predict(self, **kwargs):
self.before_predict_all(kwargs)
res = self.predict_all(**kwargs)[0]
last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=0) - 1, dtype='int64')
return res[last_id, tensor.arange(kwargs['latitude_mask'].shape[1])]
@predict.property('inputs')
def predict_inputs(self):
return self.inputs
@application(outputs=['cost_matrix'])
def cost_matrix(self, **kwargs):
self.before_predict_all(kwargs)
res = self.predict_all(**kwargs)[0]
target = tensor.concatenate(
(kwargs['destination_latitude'].dimshuffle('x', 0, 'x'),
kwargs['destination_longitude'].dimshuffle('x', 0, 'x')),
axis=2)
target = target.repeat(kwargs['latitude'].shape[0], axis=0)
ce = error.erdist(target.reshape((-1, 2)), res.reshape((-1, 2)))
ce = ce.reshape(kwargs['latitude'].shape)
return ce * kwargs['latitude_mask']
@cost_matrix.property('inputs')
def cost_matrix_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def cost(self, latitude_mask, **kwargs):
return self.cost_matrix(latitude_mask=latitude_mask, **kwargs).sum() / latitude_mask.sum()
@cost.property('inputs')
def cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
@application(outputs=['cost'])
def valid_cost(self, **kwargs):
last_id = tensor.cast(kwargs['latitude_mask'].sum(axis=1) - 1, dtype='int64')
return self.cost_matrix(**kwargs)[last_id, tensor.arange(kwargs['latitude_mask'].shape[0])].mean()
@valid_cost.property('inputs')
def valid_cost_inputs(self):
return self.inputs + ['destination_latitude', 'destination_longitude']
