def __init__(self, output_dim, init='glorot_uniform', activation='relu',weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None, input_dim=None, **kwargs):
self.W_initializer = initializers.get(init)
self.b_initializer = initializers.get('zeros')
self.activation = activations.get(activation)
self.output_dim = output_dim
self.input_dim = input_dim
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.initial_weights = weights
self.input_spec = InputSpec(ndim=2)
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(SparseFullyConnectedLayer, self).__init__(**kwargs)
python类get()的实例源码
def __init__(self, epsilon=1e-3, mode=0, axis=-1, momentum=0.99,
r_max_value=3., d_max_value=5., t_delta=1., weights=None, beta_init='zero',
gamma_init='one', gamma_regularizer=None, beta_regularizer=None,
**kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.mode = mode
self.axis = axis
self.momentum = momentum
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
self.r_max_value = r_max_value
self.d_max_value = d_max_value
self.t_delta = t_delta
if self.mode == 0:
self.uses_learning_phase = True
super(BatchRenormalization, self).__init__(**kwargs)
def __init__(self, alpha_initializer=0.2,
beta_initializer=5.0,
alpha_regularizer=None,
alpha_constraint=None,
beta_regularizer=None,
beta_constraint=None,
shared_axes=None,
**kwargs):
super(ParametricSoftplus, self).__init__(**kwargs)
self.supports_masking = True
self.alpha_initializer = initializers.get(alpha_initializer)
self.alpha_regularizer = regularizers.get(alpha_regularizer)
self.alpha_constraint = constraints.get(alpha_constraint)
self.beta_initializer = initializers.get(beta_initializer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.beta_constraint = constraints.get(beta_constraint)
if shared_axes is None:
self.shared_axes = None
elif not isinstance(shared_axes, (list, tuple)):
self.shared_axes = [shared_axes]
else:
self.shared_axes = list(shared_axes)
def __init__(self, filters,
centers_initializer='zeros',
centers_regularizer=None,
centers_constraint=None,
stds_initializer='ones',
stds_regularizer=None,
stds_constraint=None,
gauss_scale=100,
**kwargs):
self.filters = filters
self.gauss_scale = gauss_scale
super(GaussianReceptiveFields, self).__init__(**kwargs)
self.centers_initializer = initializers.get(centers_initializer)
self.stds_initializer = initializers.get(stds_initializer)
self.centers_regularizer = regularizers.get(centers_regularizer)
self.stds_regularizer = regularizers.get(stds_regularizer)
self.centers_constraint = constraints.get(centers_constraint)
self.stds_constraint = constraints.get(stds_constraint)
def __init__(self, quadratic_filters=2, init='glorot_uniform', weights=None,
W_quad_regularizer=None, W_lin_regularizer=None, activity_regularizer=None,
W_quad_constraint=None, W_lin_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.quadratic_filters = quadratic_filters
self.input_dim = input_dim
self.W_quad_regularizer = regularizers.get(W_quad_regularizer)
self.W_lin_regularizer = regularizers.get(W_lin_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_quad_constraint = constraints.get(W_quad_constraint)
self.W_lin_constraint = constraints.get(W_lin_constraint)
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=2)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(GQM, self).__init__(**kwargs)
def __init__(self, quadratic_filters=2, init='glorot_uniform', weights=None,
W_quad_regularizer=None, W_lin_regularizer=None, activity_regularizer=None,
W_quad_constraint=None, W_lin_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.quadratic_filters = quadratic_filters
self.input_dim = input_dim
self.W_quad_regularizer = regularizers.get(W_quad_regularizer)
self.W_lin_regularizer = regularizers.get(W_lin_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_quad_constraint = constraints.get(W_quad_constraint)
self.W_lin_constraint = constraints.get(W_lin_constraint)
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=5)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(GQM_conv, self).__init__(**kwargs)
def __init__(self, quadratic_filters=2, init='glorot_uniform', weights=None,
W_quad_regularizer=None, W_lin_regularizer=None, activity_regularizer=None,
W_quad_constraint=None, W_lin_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.quadratic_filters = quadratic_filters
self.input_dim = input_dim
self.W_quad_regularizer = regularizers.get(W_quad_regularizer)
self.W_lin_regularizer = regularizers.get(W_lin_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_quad_constraint = constraints.get(W_quad_constraint)
self.W_lin_constraint = constraints.get(W_lin_constraint)
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=5)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(GQM_4D, self).__init__(**kwargs)
def __init__(self, units,
kernel_initializer='glorot_uniform',
kernel_regularizer=None,
kernel_constraint=constraints.NonNeg(),
k_initializer='zeros',
k_regularizer=None,
k_constraint=None,
tied_k=False,
activity_regularizer=None,
**kwargs):
if 'input_shape' not in kwargs and 'input_dim' in kwargs:
kwargs['input_shape'] = (kwargs.pop('input_dim'),)
super(SoftMinMax, self).__init__(**kwargs)
self.units = units
self.kernel_initializer = initializers.get(kernel_initializer)
self.kernel_regularizer = regularizers.get(kernel_regularizer)
self.kernel_constraint = constraints.get(kernel_constraint)
self.k_initializer = initializers.get(k_initializer)
self.k_regularizer = regularizers.get(k_regularizer)
self.k_constraint = constraints.get(k_constraint)
self.tied_k = tied_k
self.activity_regularizer = regularizers.get(activity_regularizer)
self.input_spec = InputSpec(min_ndim=2)
self.supports_masking = True
def __init__(self, output_dim, init='glorot_uniform',
activation=None, weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.output_dim = output_dim
self.input_dim = input_dim
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.initial_weights = weights
self.input_spec = [InputSpec(ndim='2+')]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(DenseNonNeg, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
activation=None, weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
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.initial_weights = weights
self.input_spec = [InputSpec(ndim='2+')]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(Feedback, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
activation=None, weights=None,
W_regularizer=None, b_regularizer=None, activity_regularizer=None,
W_constraint=None, b_constraint=None,
bias=True, input_dim=None, **kwargs):
self.init = initializations.get(init)
self.activation = activations.get(activation)
self.input_dim = input_dim
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.initial_weights = weights
self.input_spec = [InputSpec(ndim='2+')]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(DivisiveNormalization, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
U_regularizer=None, b_start_regularizer=None, b_end_regularizer=None,
U_constraint=None, b_start_constraint=None, b_end_constraint=None,
weights=None,
**kwargs):
self.supports_masking = True
self.uses_learning_phase = True
self.input_spec = [InputSpec(ndim=3)]
self.init = initializations.get(init)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_start_regularizer = regularizers.get(b_start_regularizer)
self.b_end_regularizer = regularizers.get(b_end_regularizer)
self.U_constraint = constraints.get(U_constraint)
self.b_start_constraint = constraints.get(b_start_constraint)
self.b_end_constraint = constraints.get(b_end_constraint)
self.initial_weights = weights
super(ChainCRF, self).__init__(**kwargs)
def __init__(self, epsilon=1e-3, mode=0, axis=-1, momentum=0.99,
r_max_value=3., d_max_value=5., t_delta=1., weights=None, beta_init='zero',
gamma_init='one', gamma_regularizer=None, beta_regularizer=None,
**kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.mode = mode
self.axis = axis
self.momentum = momentum
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
self.r_max_value = r_max_value
self.d_max_value = d_max_value
self.t_delta = t_delta
if self.mode == 0:
self.uses_learning_phase = True
super(BatchRenormalization, self).__init__(**kwargs)
discrimination.py 文件源码
项目：Keras-GAN-Animeface-Character
作者: forcecore
项目源码
文件源码
阅读 20
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def __init__(self, nb_kernels, kernel_dim, init='glorot_uniform', weights=None,
W_regularizer=None, activity_regularizer=None,
W_constraint=None, input_dim=None, **kwargs):
self.init = initializers.get(init)
self.nb_kernels = nb_kernels
self.kernel_dim = kernel_dim
self.input_dim = input_dim
self.W_regularizer = regularizers.get(W_regularizer)
self.activity_regularizer = regularizers.get(activity_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.initial_weights = weights
self.input_spec = [InputSpec(ndim=2)]
if self.input_dim:
kwargs['input_shape'] = (self.input_dim,)
super(MinibatchDiscrimination, self).__init__(**kwargs)
def __init__(self, init='glorot_uniform',
U_regularizer=None,
b_start_regularizer=None,
b_end_regularizer=None,
U_constraint=None,
b_start_constraint=None,
b_end_constraint=None,
weights=None,
**kwargs):
super(ChainCRF, self).__init__(**kwargs)
self.init = initializers.get(init)
self.U_regularizer = regularizers.get(U_regularizer)
self.b_start_regularizer = regularizers.get(b_start_regularizer)
self.b_end_regularizer = regularizers.get(b_end_regularizer)
self.U_constraint = constraints.get(U_constraint)
self.b_start_constraint = constraints.get(b_start_constraint)
self.b_end_constraint = constraints.get(b_end_constraint)
self.initial_weights = weights
self.supports_masking = True
self.uses_learning_phase = True
self.input_spec = [InputSpec(ndim=3)]
def __init__(self,
W_regularizer=None, u_regularizer=None, b_regularizer=None,
W_constraint=None, u_constraint=None, b_constraint=None,
W_dropout=0., u_dropout=0., bias=True, **kwargs):
self.supports_masking = True
self.W_init = initializers.get('orthogonal')
self.u_init = initializers.get('glorot_uniform')
self.W_regularizer = regularizers.get(W_regularizer)
self.u_regularizer = regularizers.get(u_regularizer)
self.b_regularizer = regularizers.get(b_regularizer)
self.W_constraint = constraints.get(W_constraint)
self.u_constraint = constraints.get(u_constraint)
self.b_constraint = constraints.get(b_constraint)
self.W_dropout = min(1., max(0., W_dropout))
self.u_dropout = min(1., max(0., u_dropout))
self.bias = bias
super(AttentionWithContext, self).__init__(**kwargs)
def __init__(self, state_sync=False, decode=False, output_length=None, return_states=False, readout=False, readout_activation='linear', teacher_force=False, state_initializer=None, **kwargs):
self.state_sync = state_sync
self.cells = []
if decode and output_length is None:
raise Exception('output_length should be specified for decoder')
self.decode = decode
self.output_length = output_length
if decode:
if output_length is None:
raise Exception('output_length should be specified for decoder')
kwargs['return_sequences'] = True
self.return_states = return_states
super(RecurrentModel, self).__init__(**kwargs)
self.readout = readout
self.readout_activation = activations.get(readout_activation)
self.teacher_force = teacher_force
self._optional_input_placeholders = {}
if state_initializer:
if type(state_initializer) in [list, tuple]:
state_initializer = [initializers.get(init) if init else initializers.get('zeros') for init in state_initializer]
else:
state_initializer = initializers.get(state_initializer)
self._state_initializer = state_initializer
def __init__(self, output_dim, window_size=3, stride=1,
kernel_initializer='uniform', bias_initializer='zero',
activation='linear', activity_regularizer=None,
kernel_regularizer=None, bias_regularizer=None,
kernel_constraint=None, bias_constraint=None,
use_bias=True, input_dim=None, input_length=None, **kwargs):
self.output_dim = output_dim
self.window_size = window_size
self.strides = (stride, 1)
self.use_bias = use_bias
self.kernel_initializer = initializers.get(kernel_initializer)
self.bias_initializer = initializers.get(bias_initializer)
self.activation = activations.get(activation)
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=3)]
self.input_dim = input_dim
self.input_length = input_length
if self.input_dim:
kwargs['input_shape'] = (self.input_length, self.input_dim)
super(GCNN, self).__init__(**kwargs)
def __init__(self, units, window_size=2, stride=1,
return_sequences=False, go_backwards=False,
stateful=False, unroll=False, activation='tanh',
kernel_initializer='uniform', bias_initializer='zero',
kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None, bias_constraint=None,
dropout=0, use_bias=True, input_dim=None, input_length=None,
**kwargs):
self.return_sequences = return_sequences
self.go_backwards = go_backwards
self.stateful = stateful
self.unroll = unroll
self.units = units
self.window_size = window_size
self.strides = (stride, 1)
self.use_bias = use_bias
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.dropout = dropout
self.supports_masking = True
self.input_spec = [InputSpec(ndim=3)]
self.input_dim = input_dim
self.input_length = input_length
if self.input_dim:
kwargs['input_shape'] = (self.input_length, self.input_dim)
super(QRNN, self).__init__(**kwargs)
def __init__(self, axis=-1,
gamma_init='one', beta_init='zero',
gamma_regularizer=None, beta_regularizer=None,
epsilon=1e-6, **kwargs):
super(LayerNormalization, self).__init__(**kwargs)
self.axis = to_list(axis)
self.gamma_init = initializers.get(gamma_init)
self.beta_init = initializers.get(beta_init)
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.epsilon = epsilon
self.supports_masking = True
def __init__(self,
ratio,
data_format=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(SE, self).__init__(**kwargs)
self.ratio = ratio
self.data_format= conv_utils.normalize_data_format(data_format)
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.supports_masking = True
FixedBatchNormalization.py 文件源码
项目：AerialCrackDetection_Keras
作者: TTMRonald
项目源码
文件源码
阅读 17
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def __init__(self, epsilon=1e-3, axis=-1,
weights=None, beta_init='zero', gamma_init='one',
gamma_regularizer=None, beta_regularizer=None, **kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.axis = axis
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
super(FixedBatchNormalization, self).__init__(**kwargs)
def __init__(self, vocab_words, initializer):
self._vocab_words = set(vocab_words)
self._word_vector_of = dict()
self._initializer = initializers.get(initializer)
def vectorize_words(self, words):
vectors = []
for word in words:
vector = self._word_vector_of.get(word)
vectors.append(vector)
num_unknowns = len(filter(lambda x: x is None, vectors))
inits = self._initializer(shape=(num_unknowns, self._embedding_size))
inits = K.get_session().run(inits)
inits = iter(inits)
for i in range(len(vectors)):
if vectors[i] is None:
vectors[i] = next(inits)
return np.array(vectors)
def __init__(self, epsilon=1e-3, axis=-1,
weights=None, beta_init='zero', gamma_init='one',
gamma_regularizer=None, beta_regularizer=None, **kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.axis = axis
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
super(FixedBatchNormalization, self).__init__(**kwargs)
def __init__(self,
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv2D, self).__init__(
filters=None,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.bias_initializer = initializers.get(bias_initializer)
def __init__(self, filters, kernel_size,
kernel_initializer='glorot_uniform', activation=None, weights=None,
padding='valid', strides=(1, 1), data_format=None,
kernel_regularizer=None, bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None, bias_constraint=None,
use_bias=True, **kwargs):
if data_format is None:
data_format = K.image_data_format()
if padding not in {'valid', 'same', 'full'}:
raise ValueError('Invalid border mode for CosineConvolution2D:', padding)
self.filters = filters
self.kernel_size = kernel_size
self.nb_row, self.nb_col = self.kernel_size
self.kernel_initializer = initializers.get(kernel_initializer)
self.activation = activations.get(activation)
self.padding = padding
self.strides = tuple(strides)
self.data_format = normalize_data_format(data_format)
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.use_bias = use_bias
self.input_spec = [InputSpec(ndim=4)]
self.initial_weights = weights
super(CosineConvolution2D, self).__init__(**kwargs)
def __init__(self,
kernel_size,
strides=(1, 1),
padding='valid',
depth_multiplier=1,
data_format=None,
activation=None,
use_bias=True,
depthwise_initializer='glorot_uniform',
bias_initializer='zeros',
depthwise_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
depthwise_constraint=None,
bias_constraint=None,
**kwargs):
super(DepthwiseConv2D, self).__init__(
filters=None,
kernel_size=kernel_size,
strides=strides,
padding=padding,
data_format=data_format,
activation=activation,
use_bias=use_bias,
bias_regularizer=bias_regularizer,
activity_regularizer=activity_regularizer,
bias_constraint=bias_constraint,
**kwargs)
self.depth_multiplier = depth_multiplier
self.depthwise_initializer = initializers.get(depthwise_initializer)
self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
self.depthwise_constraint = constraints.get(depthwise_constraint)
self.bias_initializer = initializers.get(bias_initializer)
self._padding = _preprocess_padding(self.padding)
self._strides = (1,) + self.strides + (1,)
self._data_format = "NHWC"
def get_initializer(initializer):
if keras_2:
from keras import initializers
return initializers.get(initializer)
else:
from keras import initializations
return initializations.get(initializer)
def __init__(self, weights=None, axis=-1, momentum = 0.9, beta_init='zero', gamma_init='one', **kwargs):
self.momentum = momentum
self.axis = axis
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.initial_weights = weights
super(Scale, self).__init__(**kwargs)
