def __init__(self, input_length, input_dim, output_dim, context_dim, attention_hidden_dim,
name='AttentionLSTM', truncate_gradient=-1, go_backwards=False,
weight_init = 'glorot_uniform', inner_init = 'orthogonal', bias_init = 'zero', forget_bias_init = 'one',
activation='tanh', attention_activation='tanh', inner_activation='hard_sigmoid'):
super(AttentionLSTM_X, self).__init__()
self.input_length = input_length
self.input_dim = input_dim
self.output_dim = output_dim
self.context_dim = context_dim
self.attention_hidden_dim = attention_hidden_dim
self.name=name
self.truncate_gradient = truncate_gradient
self.go_backwards = go_backwards
self.required_input_sets = [['input_single', 'context'], ['input_sequence', 'context'], ['input_sequence', 'input_mask', 'context']]
self.output_names = ['output_last', 'output_sequence', 'output_sequence_with_alpha', 'output_last_with_alpha']
self.required_function_sets = [['weight_init', 'inner_init', 'bias_init', 'forget_bias_init', 'activation', 'attention_activation']]
self.set_output('output_last', self.output_last)
self.set_output('output_sequence', self.output_sequence)
self.set_output('output_last_with_alpha', self.output_last_with_alpha)
self.set_output('output_sequence_with_alpha', self.output_sequence_with_alpha)
self.set_function('activation', activations.get(activation))
self.set_function('attention_activation', activations.get(attention_activation))
self.set_function('inner_activation', activations.get(inner_activation))
self.set_function('weight_init', initializations.get(weight_init))
self.set_function('inner_init', initializations.get(weight_init))
self.set_function('bias_init', initializations.get(bias_init))
self.set_function('forget_bias_init', initializations.get(forget_bias_init))
def __init__(self,
input_length,
input_dim,
output_dim,
context_dim,
attention_hidden_dim,
name='AttentionLSTM',
truncate_gradient=-1,
go_backwards=False,
weight_init='glorot_uniform',
inner_init='orthogonal',
bias_init='zero',
forget_bias_init='one',
activation='tanh',
attention_activation='tanh',
inner_activation='hard_sigmoid'):
super(AttentionLSTM_X, self).__init__()
self.input_length = input_length
self.input_dim = input_dim
self.output_dim = output_dim
self.context_dim = context_dim
self.attention_hidden_dim = attention_hidden_dim
self.name = name
self.truncate_gradient = truncate_gradient
self.go_backwards = go_backwards
self.required_input_sets = [['input_single', 'context'],
['input_sequence', 'context'],
[
'input_sequence', 'input_mask',
'context'
]]
self.output_names = [
'output_last', 'output_sequence', 'output_sequence_with_alpha',
'output_last_with_alpha'
]
self.required_function_sets = [[
'weight_init', 'inner_init', 'bias_init', 'forget_bias_init',
'activation', 'attention_activation'
]]
self.set_output('output_last', self.output_last)
self.set_output('output_sequence', self.output_sequence)
self.set_output('output_last_with_alpha', self.output_last_with_alpha)
self.set_output('output_sequence_with_alpha',
self.output_sequence_with_alpha)
self.set_function('activation', activations.get(activation))
self.set_function('attention_activation',
activations.get(attention_activation))
self.set_function('inner_activation',
activations.get(inner_activation))
self.set_function('weight_init', initializations.get(weight_init))
self.set_function('inner_init', initializations.get(weight_init))
self.set_function('bias_init', initializations.get(bias_init))
self.set_function('forget_bias_init',
initializations.get(forget_bias_init))
def __init__(self,
input_length,
input_dim,
output_dim,
name='RNN',
truncate_gradient=-1,
go_backwards=False,
weight_init='glorot_uniform',
inner_init='orthogonal',
bias_init='zero',
activation='sigmoid'):
super(RNN, self).__init__()
self.input_length = input_length
self.input_dim = input_dim
self.output_dim = output_dim
self.name = name
self.truncate_gradient = truncate_gradient
self.go_backwards = go_backwards
self.required_input_sets = [['input_single'], ['input_sequence'],
['input_sequence', 'input_mask']]
self.output_names = ['output_last', 'output_sequence']
self.required_function_sets = [[
'weight_init', 'inner_init', 'bias_init', 'activation'
]]
self.set_output('output_last', self.output_last)
self.set_output('output_sequence', self.output_sequence)
self.set_function('activation', activations.get(activation))
self.set_function('weight_init', initializations.get(weight_init))
self.set_function('inner_init', initializations.get(weight_init))
self.set_function('bias_init', initializations.get(bias_init))
def __init__(self, activation):
super(Activation, self).__init__()
self.required_input_sets = [['input']]
self.output_names = ['output']
self.required_function_sets = [['activation']]
self.set_output('output', self.output)
self.set_function('activation', activations.get(activation))
def __init__(self, activation):
super(Activation, self).__init__()
self.required_input_sets = [['input']]
self.output_names = ['output']
self.required_function_sets = [['activation']]
self.set_output('output', self.output)
self.set_function('activation', activations.get(activation))
def __init__(self, input_dim, output_dim, name='Dense', weight_init='glorot_uniform', bias_init='zero', activation='linear'):
super(Dense, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.name=name
self.required_input_sets = [['input']]
self.output_names = ['output']
self.required_function_sets = [['weight_init', 'bias_init', 'activation']]
self.set_output('output', self.output)
self.set_function('activation', activations.get(activation))
self.set_function('weight_init', initializations.get(weight_init))
self.set_function('bias_init', initializations.get(bias_init))
def __init__(self, input_length, input_dim, output_dim, name='RNN', truncate_gradient=-1, go_backwards=False,
weight_init = 'glorot_uniform', inner_init = 'orthogonal', bias_init = 'zero', activation='sigmoid'):
super(RNN, self).__init__()
self.input_length = input_length
self.input_dim = input_dim
self.output_dim = output_dim
self.name=name
self.truncate_gradient = truncate_gradient
self.go_backwards = go_backwards
self.required_input_sets = [['input_single'], ['input_sequence'], ['input_sequence', 'input_mask']]
self.output_names = ['output_last', 'output_sequence']
self.required_function_sets = [['weight_init', 'inner_init', 'bias_init', 'activation']]
self.set_output('output_last', self.output_last)
self.set_output('output_sequence', self.output_sequence)
self.set_function('activation', activations.get(activation))
self.set_function('weight_init', initializations.get(weight_init))
self.set_function('inner_init', initializations.get(weight_init))
self.set_function('bias_init', initializations.get(bias_init))
def __init__(self,
input_dim,
output_dim,
name='Dense',
weight_init='glorot_uniform',
bias_init='zero',
activation='linear'):
super(Dense, self).__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.name = name
self.required_input_sets = [['input']]
self.output_names = ['output']
self.required_function_sets = [[
'weight_init', 'bias_init', 'activation'
]]
self.set_output('output', self.output)
self.set_function('activation', activations.get(activation))
self.set_function('weight_init', initializations.get(weight_init))
self.set_function('bias_init', initializations.get(bias_init))
def add_one(x):
return x + 1.0
init_list = [('softmax', data2),
('softplus', data2),
('relu', data2),
('tanh', data2),
('sigmoid',data2),
('hard_sigmoid',data2),
('linear',data2),
(add_one, data2),
('softmax', data3),
('softplus', data3),
('relu', data3),
('tanh', data3),
('sigmoid',data3),
('hard_sigmoid',data3),
('linear',data3),
(add_one, data3)
]
for (act, data) in init_list:
if data.ndim == 2:
x = T.matrix('x')
else:
x = T.tensor3('x')
fun = theano.function([x], activations.get(act)(x))
val = fun(data)
print act, 'data' + str(data.ndim), ':'
print val
print
