def dropout(cntk_layer, inputs):
'''
Setup dropout op with given parameters
Args:
cntk_layer (:class:`~cntk.contrib.crosstalkcaffe.unimodel.cntkmodel.CntkLayersDefinition`):
the layer definition of dropout op
inputs (list): a list contains all :class:`~cntk.ops.functions.Function` or
:class:`~cntk.input`
Return:
:func:`~cntk.ops.functions.Function`: instaced cntk dropout op
'''
sanitize_output = ops.sanitize_input(inputs[0])
return ops.dropout(sanitize_output, name=cntk_layer.op_name)
def dropout(cntk_layer, inputs):
'''
Setup dropout op with given parameters
Args:
cntk_layer (:class:`~cntk.contrib.crosstalkcaffe.unimodel.cntkmodel.CntkLayersDefinition`):
the layer definition of dropout op
inputs (list): a list contains all :class:`~cntk.ops.functions.Function` or
:class:`~cntk.input`
Return:
:func:`~cntk.ops.functions.Function`: instaced cntk dropout op
'''
sanitize_output = ops.sanitize_input(inputs[0])
return ops.dropout(sanitize_output, name=cntk_layer.op_name)
def Dropout(prob):
# expression
x = Placeholder(name='dropout_arg')
apply_x = dropout(x, dropout_rate=prob)
return Block(apply_x, 'Dropout')
def Dropout(prob):
# expression
x = Placeholder(name='dropout_arg')
apply_x = dropout(x, dropout_rate=prob)
return Block(apply_x, 'Dropout')
def create_model(params: model_params):
"""
Create ReasoNet model
Args:
params (class:`model_params`): The parameters used to create the model
"""
logger.log(
"Create model: dropout_rate: {0}, init:{1}, embedding_init: {2}".
format(params.dropout_rate, params.init, params.embedding_init))
# Query and Doc/Context/Paragraph inputs to the model
query_seq_axis = Axis('sourceAxis')
context_seq_axis = Axis('contextAxis')
query_sequence = sequence.input(shape=(params.vocab_dim),
is_sparse=True,
sequence_axis=query_seq_axis,
name='query')
context_sequence = sequence.input(shape=(params.vocab_dim),
is_sparse=True,
sequence_axis=context_seq_axis,
name='context')
entity_ids_mask = sequence.input(shape=(1, ),
is_sparse=False,
sequence_axis=context_seq_axis,
name='entity_ids_mask')
# embedding
if params.embedding_init is None:
embedding_init = create_random_matrix(params.vocab_dim,
params.embedding_dim)
else:
embedding_init = params.embedding_init
embedding = parameter(shape=(params.vocab_dim, params.embedding_dim),
init=None)
embedding.value = embedding_init
embedding_matrix = constant(embedding_init,
shape=(params.vocab_dim, params.embedding_dim))
if params.dropout_rate is not None:
query_embedding = ops.dropout(times(query_sequence, embedding),
params.dropout_rate,
name='query_embedding')
context_embedding = ops.dropout(times(context_sequence, embedding),
params.dropout_rate,
name='context_embedding')
else:
query_embedding = times(query_sequence,
embedding,
name='query_embedding')
context_embedding = times(context_sequence,
embedding,
name='context_embedding')
contextGruW = Parameter(_INFERRED + _as_tuple(params.hidden_dim),
init=glorot_uniform(),
name='gru_params')
queryGruW = Parameter(_INFERRED + _as_tuple(params.hidden_dim),
init=glorot_uniform(),
name='gru_params')
entity_embedding = ops.times(context_sequence,
embedding_matrix,
name='constant_entity_embedding')
# Unlike other words in the context, we keep the entity vectors fixed as a random vector so that each vector just means an identifier of different entities in the context and it has no semantic meaning
full_context_embedding = ops.element_select(entity_ids_mask,
entity_embedding,
context_embedding)
context_memory = ops.optimized_rnnstack(full_context_embedding,
contextGruW,
params.hidden_dim,
1,
True,
recurrent_op='gru',
name='context_mem')
query_memory = ops.optimized_rnnstack(query_embedding,
queryGruW,
params.hidden_dim,
1,
True,
recurrent_op='gru',
name='query_mem')
qfwd = ops.slice(sequence.last(query_memory),
-1,
0,
params.hidden_dim,
name='fwd')
qbwd = ops.slice(sequence.first(query_memory),
-1,
params.hidden_dim,
params.hidden_dim * 2,
name='bwd')
init_status = ops.splice(
qfwd, qbwd,
name='Init_Status') # get last fwd status and first bwd status
return attention_model(context_memory,
query_memory,
init_status,
params.hidden_dim,
params.attention_dim,
max_steps=params.max_rl_steps)