def __init__(self,
output_dim,
init='glorot_uniform',
inner_init='orthogonal',
activation='tanh',
inner_activation='hard_sigmoid',
W_regularizer=None,
U_regularizer=None,
b_regularizer=None,
dropout_W=0.,
dropout_U=0.,
**kwargs):
self.output_dim = output_dim
self.init = initializations.get(init)
self.inner_init = initializations.get(inner_init)
self.activation = activations.get(activation)
self.inner_activation = activations.get(inner_activation)
self.W_regularizer = regularizers.get(W_regularizer)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.dropout_W = dropout_W
self.dropout_U = dropout_U
if self.dropout_W or self.dropout_U:
self.uses_learning_phase = True
super(MGU, self).__init__(**kwargs)
def __init__(self, output_dim,
init='glorot_uniform', inner_init='orthogonal',
activation='tanh', inner_activation='hard_sigmoid', **kwargs):
self.output_dim = output_dim
self.init = initializations.get(init)
self.inner_init = initializations.get(inner_init)
self.activation = activations.get(activation)
self.inner_activation = activations.get(inner_activation)
super(DecoderGRU, self).__init__(**kwargs)
def __init__(self,
units,
activation='tanh',
recurrent_activation='hard_sigmoid',
attention_activation='tanh',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
attention_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
attention_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
attention_constraint=None,
dropout=0.,
recurrent_dropout=0.,
return_attention=False,
implementation=1,
**kwargs):
super(AttentionLSTM, self).__init__(**kwargs)
self.units = units
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.attention_activation = activations.get(attention_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.attention_initializer = initializers.get(attention_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.attention_regularizer = regularizers.get(attention_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.attention_constraint = constraints.get(attention_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.return_attention = return_attention
self.state_spec = [
InputSpec(shape=(None, self.units)),
InputSpec(shape=(None, self.units))
]
self.implementation = implementation
def __init__(self, output_dim, attention_vec, attn_activation='tanh',
attn_inner_activation='tanh', single_attn=False,
n_attention_dim=None, **kwargs):
self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.attn_inner_activation = activations.get(attn_inner_activation)
self.single_attention_param = single_attn
self.n_attention_dim = output_dim if n_attention_dim is None else n_attention_dim
super(AttentionLSTM, self).__init__(output_dim, **kwargs)
def __init__(
self,
units, #units in decoder
steps, # steps for output
output_dim, # dimension of output
atten_units, #attencion units in dense layer
gmax, #sub hidden units maxout
activation='tanh',
recurrent_activation='hard_sigmoid',
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
return_probabilities=False,
**kwargs):
self.units = units
self.steps = steps
self.output_dim = output_dim
self.atten_units = atten_units
self.activation = activations.get(activation)
self.gmax = gmax
self.recurrent_activation = activations.get(recurrent_activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self._dropout_mask = None
self._recurrent_dropout_mask = None
self.return_probabilities = return_probabilities
"""if self.dropout or self.recurrent_dropout:
self.uses_learning_phase = True"""
super(attention_LSTM, self).__init__(**kwargs)
def __init__(self,output_dim,att_dim,attn_activation='tanh',
attn_inner_activation='tanh',
single_attn=False,**kwargs):
'''
attention_vec: 输入到这一层的attention向量，根据这个向量来计算这一层的attention输出
single_attention_param: 每个时间t,的向量中的元素是否使用同一个attention值
'''
self.attn_activation=activations.get(attn_activation)
self.attn_inner_activation=activations.get(attn_inner_activation)
self.single_attention_param=single_attn
self.input_spec=None
self.att_dim=att_dim
super(AttentionLSTM,self).__init__(output_dim,**kwargs)
def __init__(self,
output_dim,
attn_activation='tanh',
attn_inner_activation='tanh',
single_attn=True,
n_attention_dim=None,
**kwargs):
#self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.attn_inner_activation = activations.get(attn_inner_activation)
self.single_attention_param = single_attn
self.n_attention_dim = output_dim if n_attention_dim is None else n_attention_dim
super(AttentionLSTM, self).__init__(output_dim, **kwargs)
def __init__(self, units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(MultiHead, self).__init__(**kwargs)
self.units = units
self.heads = 8
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
self.input_spec = [InputSpec(min_ndim=3), InputSpec(min_ndim=3), InputSpec(min_ndim=3)]
def __init__(self, filters, kernel_size, rank=1, strides=1, padding='valid', data_format='channels_last',
dilation_rate=1, activation=None, use_bias=True, kernel_initializer='glorot_uniform',
bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, bias_constraint=None, type = 1, **kwargs):
super(Conv1D_linearphaseType, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size_=kernel_size
if kernel_size % 2:
self.kernel_size = conv_utils.normalize_tuple(kernel_size // 2 + 1, rank, 'kernel_size')
else:
self.kernel_size = conv_utils.normalize_tuple(kernel_size // 2, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(ndim=self.rank + 2)
if type > 4:
raise ValueError('FIR type should be between 1-4')
else:
self.type = type
def __init__(self,
units,
bond_classes,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
super(EdgeNetwork, self).__init__(**kwargs)
self.units = units
self.bond_classes = bond_classes
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
def __init__(self, layer, attention_vec, attn_activation='tanh', single_attention_param=False, **kwargs):
assert isinstance(layer, LSTM) or isinstance(layer, GRU)
super(AttentionWrapper, self).__init__(layer, **kwargs)
self.supports_masking = True
self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.single_attention_param = single_attention_param
def __init__(self, nb_filter, nb_row, nb_col,
init='glorot_uniform', activation=None, weights=None,
border_mode='valid', subsample=(1, 1),
dim_ordering='default',
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, **kwargs):
if dim_ordering == 'default':
dim_ordering = K.image_dim_ordering()
if border_mode != 'valid':
raise ValueError('Invalid border mode for LocallyConnected2D '
'(only "valid" is supported):', border_mode)
self.nb_filter = nb_filter
self.nb_row = nb_row
self.nb_col = nb_col
self.init = initializations.get(init, dim_ordering=dim_ordering)
self.activation = activations.get(activation)
self.border_mode = border_mode
self.subsample = tuple(subsample)
if dim_ordering not in {'tf', 'th'}:
raise ValueError('`dim_ordering` must be in {tf, th}.')
self.dim_ordering = dim_ordering
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
super(LocallyConnected2D, self).__init__(**kwargs)
def __init__(self,
output_dim,
mem_vec_dim,
init='glorot_uniform',
activation='linear',
weights=None,
activity_regularizer=None,
input_dim=None,
**kwargs):
'''
Params:
output_dim: 输出的维度
mem_vec_dim: query向量的维度
'''
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
self.input_dim = input_dim
self.mem_vector_dim = mem_vec_dim
self.activity_regularizer = regularizers.get(activity_regularizer)
self.initial_weights = weights
if self.input_dim:
kwargs['input_shape'] = (self.input_dim, )
super(MemoryNet, self).__init__(**kwargs)
def __init__(self,
units,
map=None,
nonzero_ind=None,
kernel_initializer='glorot_uniform',
W_regularizer=None,
activation='tanh',
use_bias=True,
bias_initializer='zeros',
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
self.units = units
self.activation = activation
self.map = map
self.nonzero_ind = nonzero_ind
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(W_regularizer)
self.bias_initializer = initializers.get(bias_initializer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activation_fn = activations.get(activation)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
super(SparseTF, self).__init__(**kwargs)
def __init__(self, filters=1, kernel_size=80, rank=1, strides=1, padding='valid',
data_format='channels_last', dilation_rate=1, activation=None, use_bias=True,
fsHz=1000.,
fc_initializer=initializers.RandomUniform(minval=10, maxval=400),
n_order_initializer=initializers.constant(4.),
amp_initializer=initializers.constant(10 ** 5),
beta_initializer=initializers.RandomNormal(mean=30, stddev=6),
bias_initializer='zeros',
**kwargs):
super(Conv1D_gammatone, self).__init__(**kwargs)
self.rank = rank
self.filters = filters
self.kernel_size_ = kernel_size
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank, 'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.bias_initializer = initializers.get(bias_initializer)
self.fc_initializer = initializers.get(fc_initializer)
self.n_order_initializer = initializers.get(n_order_initializer)
self.amp_initializer = initializers.get(amp_initializer)
self.beta_initializer = initializers.get(beta_initializer)
self.input_spec = InputSpec(ndim=self.rank + 2)
self.fsHz = fsHz
self.t = tf.range(start=0, limit=kernel_size / float(fsHz),
delta=1 / float(fsHz), dtype=K.floatx())
self.t = tf.expand_dims(input=self.t, axis=-1)
def __init__(self, nb_filter, filter_length,
init='glorot_uniform', activation=None, weights=None,
border_mode='valid', subsample_length=1,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, input_length=None, **kwargs):
if border_mode != 'valid':
raise ValueError('Invalid border mode for LocallyConnected1D '
'(only "valid" is supported):', border_mode)
self.nb_filter = nb_filter
self.filter_length = filter_length
self.init = initializations.get(init, dim_ordering='th')
self.activation = activations.get(activation)
self.border_mode = border_mode
self.subsample_length = subsample_length
self.W_regularizer = regularizers.get(W_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.b_constraint = constraints.get(b_constraint)
self.bias = bias
self.input_spec = [InputSpec(ndim=3)]
self.initial_weights = weights
self.input_dim = input_dim
self.input_length = input_length
if self.input_dim:
kwargs['input_shape'] = (self.input_length, self.input_dim)
super(LocallyConnected1D, self).__init__(**kwargs)
def __init__(self, layer, attention_vec, attn_activation='tanh', single_attention_param=False, **kwargs):
assert isinstance(layer, LSTM)
self.supports_masking = True
self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.single_attention_param = single_attention_param
super(AttentionLSTMWrapper, self).__init__(layer, **kwargs)
def __init__(self, filters, kernel_size=(2, 2), strides=None, padding='valid', data_format=None,
kernel_initializer='uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
activation='linear',
with_softmax=True,
**kwargs):
super(KernelBasedPooling, self).__init__(kernel_size, strides, padding,
data_format, **kwargs)
self.rank = 2
self.with_softmax = with_softmax
self.filters = filters
self.kernel_size = kernel_size
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
self.trainable = True
def __init__(self,
units,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
W_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
# self.output_dim = output_dim
# self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.units = units
self.activation = activation
self.activation_fn = activations.get(activation)
self.use_bias = use_bias
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_initializer = initializers.get(kernel_initializer)
self.W_regularizer = W_regularizer
self.bias_regularizer = bias_regularizer
self.kernel_regularizer = regularizers.get(W_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = bias_constraint
super(Diagonal, self).__init__(**kwargs)
def __init__(self, mode="bilinear", attention_embedding_size=None, inner_activation="tanh",
return_attention_matrix=False, initializer="glorot_uniform", **kwargs):
self.mode = mode
self.initializer = initializers.get(initializer)
self.attention_embedding_size = attention_embedding_size
self.inner_activation = activations.get(inner_activation)
self.return_attention_matrix = return_attention_matrix
super(CrossAttention, self).__init__(**kwargs)
def __init__(self,
output_dim,
att_dim,
attn_activation='tanh',
attn_inner_activation='tanh',
single_attn=False,
**kwargs):
'''
attention_vec: 输入到这一层的attention向量，根据这个向量来计算这一层的attention输出
single_attention_param: 每个时间t,的向量中的元素是否使用同一个attention值
'''
self.attn_activation = activations.get(attn_activation)
self.attn_inner_activation = activations.get(attn_inner_activation)
self.single_attention_param = single_attn
self.input_spec = None
self.att_dim = att_dim
super(AttentionLSTM, self).__init__(output_dim, **kwargs)
def __init__(self,
units=None,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
unit_forget_bias=True,
dropout=0.,
recurrent_dropout=0.,
implementation=1,
**kwargs):
if units is None:
assert 'output_dim' in kwargs, 'Missing argument: units'
else:
kwargs['output_dim'] = units
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.unit_forget_bias = unit_forget_bias
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.dropout = min(1., max(0., dropout))
self.implementation = implementation
self._trainable_weights = []
self._dropout_mask = None
self._recurrent_dropout_mask = None
super(ExtendedRNNCell, self).__init__(**kwargs)
def __init__(self, units, rank,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
implementation=1,
**kwargs):
super(CLSTMCell, self).__init__(**kwargs)
self.units = units
self.rank = rank
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.unit_forget_bias = unit_forget_bias
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(recurrent_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(recurrent_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.dropout = min(1., max(0., dropout))
self.recurrent_dropout = min(1., max(0., recurrent_dropout))
self.implementation = implementation
self.state_size = (self.rank, self.units)
self.output_size = self.rank
self._dropout_mask = None
self._recurrent_dropout_mask = None
def __init__(self,
output_dim,
attention_vec,
attn_activation='tanh',
attn_inner_activation='tanh',
single_attn=False,
n_attention_dim=None,
**kwargs):
'''
attention_vec: 输入到这一层的attention向量，根据这个向量来计算这一层的attention输出
如果attention_vec=None,就不使用attention
'''
self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.attn_inner_activation = activations.get(attn_inner_activation)
self.single_attention_param = single_attn
self.n_attention_dim = output_dim if n_attention_dim is None else n_attention_dim
super(AttentionLSTM, self).__init__(output_dim, **kwargs)
def __init__(self,
output_dim,
init='glorot_uniform',
attn_activation='tanh',
**kwargs):
self.output_dim = output_dim
self.init = initializations.get(init)
self.attn_activation = activations.get(attn_activation)
super(AttentionLayer, self).__init__(**kwargs)
def __init__(self,
filters,
kernel_size,
strides=1,
padding='valid',
data_format=None,
dilation_rate=1,
activation=None,
use_bias=True,
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
conv_select_activation=None,
select_weight_init_value=0,
**kwargs):
super(ConvBank, self).__init__(**kwargs)
rank = 2
self.rank = 2
self.filters = filters
self.kernel_size = conv_utils.normalize_tuple(kernel_size, rank,
'kernel_size')
self.strides = conv_utils.normalize_tuple(strides, rank, 'strides')
self.padding = conv_utils.normalize_padding(padding)
self.data_format = conv_utils.normalize_data_format(data_format)
self.dilation_rate = conv_utils.normalize_tuple(
dilation_rate, rank, 'dilation_rate')
self.activation = activations.get(activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
self.select_activation = None if conv_select_activation == None else activations.get(
conv_select_activation)
self.select_weight_init = initializers.Constant(
select_weight_init_value)
def apply_gates(self, inputs, input_shape, axis):
gates = K.dot(K.mean(inputs, axis=axis), self.gates_kernel)
gates = K.bias_add(gates, self.gates_bias, data_format='channels_last')
gates = activations.get('softmax')(gates)
inputs_mul_gates = K.reshape(gates, [-1, self.k] + [1] *
(len(input_shape) - 1)) * K.expand_dims(
inputs, axis=1)
# inputs_mul_gates = K.stack([K.reshape(gates[:, k], [-1] + [1] * (len(input_shape) - 1)) * inputs
# for k in range(self.k)], axis=0)
return inputs_mul_gates
def __init__(self, units, attn_activation='tanh', single_attention_param=False,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
unit_forget_bias=True,
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.,
recurrent_dropout=0.,
implementation=1,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs):
self.supports_masking = True
self.attn_activation = activations.get(attn_activation)
self.single_attention_param = single_attention_param
cell = AttentionLSTMCell(units,
attn_activation=attn_activation, single_attention_param=single_attention_param,
activation=activation,
recurrent_activation=recurrent_activation,
use_bias=use_bias,
kernel_initializer=kernel_initializer,
recurrent_initializer=recurrent_initializer,
unit_forget_bias=unit_forget_bias,
bias_initializer=bias_initializer,
kernel_regularizer=kernel_regularizer,
recurrent_regularizer=recurrent_regularizer,
bias_regularizer=bias_regularizer,
kernel_constraint=kernel_constraint,
recurrent_constraint=recurrent_constraint,
bias_constraint=bias_constraint,
dropout=dropout,
recurrent_dropout=recurrent_dropout,
implementation=implementation)
super(AttentionLSTM, self).__init__(cell,
return_sequences=return_sequences,
return_state=return_state,
go_backwards=go_backwards,
stateful=stateful,
unroll=unroll,
**kwargs)
self.activity_regularizer = regularizers.get(activity_regularizer)
def __init__(
self,
units,
output_dim,
activation='tanh',
return_probabilities=False,
name='AttentionDecoder',
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
# return_sequences=True,
**kwargs):
"""
self.return_sequences 默认是True
:param units:
:param output_dim:
:param activation:
:param return_probabilities:
:param name:
:param kernel_initializer: 初始权重
:param recurrent_initializer:
:param bias_initializer:
:param kernel_regularizer: 正则化
:param bias_regularizer:
:param activity_regularizer:
:param kernel_constraint:
:param bias_constraint:
:param kwargs:
"""
self.units = units
self.output_dim = output_dim
self.return_probabilities = return_probabilities
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.recurrent_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.recurrent_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
# self.return_sequences = return_sequences # must return sequences
super(AttentionDecoder, self).__init__(**kwargs)
self.name = name
def __init__(self,
output_dim,
attention_vec,
attn_activation='tanh',
single_attention_param=False,
**kwargs):
self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.single_attention_param = single_attention_param
super(AttentionLSTM, self).__init__(output_dim, **kwargs)
def __init__(self, units,
activation='linear',
kernel_initializer='glorot_uniform',
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
self.units = units # k
self.activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
super(BilinearLayer, self).__init__(**kwargs)
def __init__(self,
activation=None,
use_bias=True,
bias_initializer='zeros',
bias_regularizer=None,
**kwargs):
self.activation = activations.get(activation)
self.use_bias = use_bias
self.bias_initializer = initializers.get(bias_initializer)
self.bias_regularizer = regularizers.get(bias_regularizer)
super(OutputLayer, self).__init__(**kwargs)
def __init__(self,
output_dim,
attention_vec,
attn_activation='tanh',
single_attention_param=False,
**kwargs):
self.attention_vec = attention_vec
self.attn_activation = activations.get(attn_activation)
self.single_attention_param = single_attention_param
super(AttentionLSTM, self).__init__(output_dim, **kwargs)
def call(self, inputs):
output1 = K.dot(inputs, self.kernel1)
output2 = K.dot(inputs, self.kernel2)
output3 = K.dot(inputs, self.kernel3)
output4 = K.dot(inputs, self.kernel4)
output5 = K.dot(inputs, self.kernel5)
output6 = K.dot(inputs, self.kernel6)
if self.use_bias:
output1 = K.bias_add(output1,
self.bias1,
data_format='channels_last')
output2 = K.bias_add(output2,
self.bias2,
data_format='channels_last')
output3 = K.bias_add(output3,
self.bias3,
data_format='channels_last')
output4 = K.bias_add(output4,
self.bias4,
data_format='channels_last')
output5 = K.bias_add(output5,
self.bias5,
data_format='channels_last')
output6 = K.bias_add(output6,
self.bias6,
data_format='channels_last')
self.activation = activations.get('X_1')
output1 = self.activation(output1)
self.activation = activations.get('X_2')
output2 = self.activation(output2)
self.activation = activations.get('X_3')
output3 = self.activation(output3)
self.activation = activations.get('X_4')
output4 = self.activation(output4)
self.activation = activations.get('X_5')
output5 = self.activation(output5)
self.activation = activations.get('X_6')
output6 = self.activation(output6)
output_all = concatenate(
[output1, output2, output3, output4, output5, output6])
output_all = K.dot(output_all, self.kernel_all)
output_all = K.bias_add(output_all,
self.bias_all,
data_format='channels_last')
self.activation = activations.get('linear')
output_all = self.activation(output_all)
return output_all
def __init__(self, dims, **kwargs):
super(AttentionLayer, self).__init__(**kwargs)
self.dims = dims
self.act_tanh = activations.get('tanh')
self.w = self.add_weight(name='w',
shape=(self.dims, self.dims),
initializer='glorot_uniform',
trainable=True)
self.b = self.add_weight(name='b',
shape=(self.dims, ),
initializer='zeros',
trainable=True)
def __init__(self,
units,
out_units,
hidden_layers=1,
dropout_rate=0.0,
random_input_order=False,
activation='elu',
out_activation='sigmoid',
kernel_initializer='glorot_uniform',
bias_initializer='zeros',
out_kernel_initializer='glorot_uniform',
out_bias_initializer='zeros',
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'), )
super(MaskingDense, self).__init__(**kwargs)
self.input_sel = None
self.random_input_order = random_input_order
self.rate = min(1., max(0., dropout_rate))
self.kernel_sels = []
self.units = units
self.out_units = out_units
self.hidden_layers = hidden_layers
self.activation = activations.get(activation)
self.out_activation = activations.get(activation)
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.out_kernel_initializer = initializers.get(out_kernel_initializer)
self.out_bias_initializer = initializers.get(out_bias_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.bias_regularizer = regularizers.get(bias_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.bias_constraint = constraints.get(bias_constraint)
def __init__(self,
units=50,
normalize=False,
init_diag=False,
activation='tanh',
recurrent_activation='hard_sigmoid',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
**kwargs):
super(SpatialGRU, self).__init__(**kwargs)
self.units = units
self.normalize = normalize
self.init_diag = init_diag
self.supports_masking = True
self.activation = activations.get(activation)
self.recurrent_activation = activations.get(recurrent_activation)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.recurrent_initializer = initializers.get(recurrent_initializer)
self.bias_initializer = initializers.get(bias_initializer)
def __init__(self,output_dim,mem_vec_dim,init='glorot_uniform', activation='linear', weights=None,
activity_regularizer=None,input_dim=None, **kwargs):
'''
Params:
output_dim: 输出的维度
mem_vec_dim: query向量的维度
'''
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
self.input_dim = input_dim
self.mem_vector_dim=mem_vec_dim
self.activity_regularizer = regularizers.get(activity_regularizer)
self.initial_weights = weights
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(MemoryNet,self).__init__(**kwargs)