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@ -84,6 +84,7 @@ __all__ = [
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'GeneratedInput',
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'SubsequenceInput',
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'gru_step_layer',
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'gru_step_naive_layer',
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'recurrent_layer',
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'BaseGeneratedInput',
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'conv_operator',
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@ -2284,7 +2285,7 @@ def img_pool_layer(input,
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type_name = pool_type.name + '-projection' \
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if (
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isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
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isinstance(pool_type, AvgPooling) or isinstance(pool_type, MaxPooling)) \
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else pool_type.name
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pool_size_y = pool_size if pool_size_y is None else pool_size_y
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@ -3084,6 +3085,78 @@ def gru_step_layer(input,
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activation=act)
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@wrap_bias_attr_default()
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@wrap_param_attr_default()
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@wrap_act_default(param_names=['gate_act'], act=SigmoidActivation())
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@wrap_act_default(act=TanhActivation())
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@wrap_name_default('gru_step_naive')
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@layer_support(ERROR_CLIPPING, DROPOUT)
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def gru_step_naive_layer(input,
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output_mem,
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size=None,
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name=None,
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act=None,
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gate_act=None,
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bias_attr=None,
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param_attr=None,
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layer_attr=None):
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"""
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GRU Step Layer, but using MixedLayer to generate. It support ERROR_CLIPPING
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and DROPOUT.
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:param input:
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:param output_mem:
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:param size:
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:param name:
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:param act:
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:param gate_act:
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:param bias_attr:
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:param param_attr:
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:param layer_attr:
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:return:
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"""
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if input.size % 3 != 0:
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raise ValueError("GruStep input size must be divided by 3")
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if size is None:
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size = input.size / 3
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def __gate__(gate_name, offset):
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with mixed_layer(
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name=name + "_" + gate_name,
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size=size,
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layer_attr=layer_attr,
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bias_attr=bias_attr,
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act=gate_act) as gate:
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gate += identity_projection(input=input, offset=offset)
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gate += full_matrix_projection(
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input=output_mem, param_attr=param_attr)
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return gate
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update_gate = __gate__("update", 0)
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reset_gate = __gate__("reset", size)
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with mixed_layer(
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name=name + "_reset_output", bias_attr=False) as reset_output:
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reset_output += dotmul_operator(a=output_mem, b=reset_gate)
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with mixed_layer(
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name=name + "_output_candidate",
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size=size,
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layer_attr=layer_attr,
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bias_attr=bias_attr,
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act=act) as output_candidate:
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output_candidate += identity_projection(input=input, offset=2 * size)
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output_candidate += full_matrix_projection(
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input=reset_output, param_attr=param_attr)
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with mixed_layer(name=name) as output:
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output += identity_projection(output_mem)
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output += dotmul_operator(a=output_mem, b=update_gate, scale=-1.0)
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output += dotmul_operator(a=output_candidate, b=update_gate)
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return output
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@wrap_name_default()
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@layer_support()
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def get_output_layer(input, arg_name, name=None, layer_attr=None):
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Yu Yang
8 years ago
committed by
GitHub