Python keras.regularizers.get() Examples

The following are 30 code examples for showing how to use keras.regularizers.get(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: deep-models   Author: LaurentMazare   File: rhn.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self, output_dim, L,
             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, self.dropout_U = dropout_W, dropout_U
    self.L = L

    if self.dropout_W or self.dropout_U:
        self.uses_learning_phase = True
    super(RHN, self).__init__(**kwargs) 
Example 2
Project: 3DGCN   Author: blackmints   File: layer.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 filters,
                 pooling='sum',
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 bias_initializer='zeros',
                 activation=None,
                 **kwargs):
        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.filters = filters
        self.pooling = pooling

        super(GraphConvS, self).__init__(**kwargs) 
Example 3
Project: Document-Classifier-LSTM   Author: AlexGidiotis   File: attention.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, u_regularizer=None, b_regularizer=None,
                 W_constraint=None, u_constraint=None, b_constraint=None,
                 bias=True, **kwargs):


        self.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.bias = bias
        super(AttentionWithContext, self).__init__(**kwargs) 
Example 4
Project: DeepResearch   Author: Hsankesara   File: attention_with_context.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, u_regularizer=None, b_regularizer=None,
                 W_constraint=None, u_constraint=None, b_constraint=None,
                 bias=True, **kwargs):

        self.supports_masking = True
        self.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.bias = bias
        super(AttentionWithContext, self).__init__(**kwargs) 
Example 5
Project: keras_bn_library   Author: bnsnapper   File: recurrent.py    License: MIT License 6 votes vote down vote up
def __init__(self, output_dim,
                 init='glorot_uniform', inner_init='orthogonal',
                 forget_bias_init='one', 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.forget_bias_init = initializations.get(forget_bias_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, self.dropout_U = dropout_W, dropout_U

		if self.dropout_W or self.dropout_U:
			self.uses_learning_phase = True
		super(DecoderVaeLSTM, self).__init__(**kwargs) 
Example 6
Project: keras_bn_library   Author: bnsnapper   File: recurrent.py    License: MIT License 6 votes vote down vote up
def __init__(self, output_dim,
                 init='glorot_uniform', inner_init='orthogonal',
                 forget_bias_init='one', 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.forget_bias_init = initializations.get(forget_bias_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
		self.stateful = False

		if self.dropout_W or self.dropout_U:
			self.uses_learning_phase = True
		super(QRNN, self).__init__(**kwargs) 
Example 7
Project: elmo-bilstm-cnn-crf   Author: UKPLab   File: ChainCRF.py    License: Apache License 2.0 6 votes vote down vote up
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)] 
Example 8
Project: deep_complex_networks   Author: ChihebTrabelsi   File: norm.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 epsilon=1e-4,
                 axis=-1,
                 beta_init='zeros',
                 gamma_init='ones',
                 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)

        super(LayerNormalization, self).__init__(**kwargs) 
Example 9
Project: Coloring-greyscale-images   Author: emilwallner   File: instance_normalization.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 axis=None,
                 epsilon=1e-3,
                 center=True,
                 scale=True,
                 beta_initializer='zeros',
                 gamma_initializer='ones',
                 beta_regularizer=None,
                 gamma_regularizer=None,
                 beta_constraint=None,
                 gamma_constraint=None,
                 **kwargs):
        super(InstanceNormalization, self).__init__(**kwargs)
        self.supports_masking = True
        self.axis = axis
        self.epsilon = epsilon
        self.center = center
        self.scale = scale
        self.beta_initializer = initializers.get(beta_initializer)
        self.gamma_initializer = initializers.get(gamma_initializer)
        self.beta_regularizer = regularizers.get(beta_regularizer)
        self.gamma_regularizer = regularizers.get(gamma_regularizer)
        self.beta_constraint = constraints.get(beta_constraint)
        self.gamma_constraint = constraints.get(gamma_constraint) 
Example 10
Project: deephlapan   Author: jiujiezz   File: attention.py    License: GNU General Public License v2.0 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, b_regularizer=None,
                 W_constraint=None, b_constraint=None,
                 bias=True,
                 return_attention=False,
                 **kwargs):

        self.supports_masking = True
        self.return_attention = return_attention
        self.init = initializers.get('glorot_uniform')

        self.W_regularizer = regularizers.get(W_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)

        self.W_constraint = constraints.get(W_constraint)
        self.b_constraint = constraints.get(b_constraint)

        self.bias = bias
        super(Attention, self).__init__(**kwargs) 
Example 11
Project: NTM-Keras   Author: SigmaQuan   File: lstm2ntm.py    License: MIT License 6 votes vote down vote up
def __init__(self, output_dim, memory_dim=128, memory_size=20,
                 controller_output_dim=100, location_shift_range=1,
                 num_read_head=1, num_write_head=1,
                 init='glorot_uniform', inner_init='orthogonal',
                 forget_bias_init='one', activation='tanh',
                 inner_activation='hard_sigmoid',
                 W_regularizer=None, U_regularizer=None, R_regularizer=None,
                 b_regularizer=None, W_y_regularizer=None,
                 W_xi_regularizer=None, W_r_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.forget_bias_init = initializations.get(forget_bias_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, self.dropout_U = dropout_W, dropout_U

        if self.dropout_W or self.dropout_U:
            self.uses_learning_phase = True
        super(NTM, self).__init__(**kwargs) 
Example 12
Project: se_relativisticgan   Author: deepakbaby   File: normalizations.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 axis=None,
                 epsilon=1e-3,
                 center=True,
                 scale=True,
                 beta_initializer='zeros',
                 gamma_initializer='ones',
                 beta_regularizer=None,
                 gamma_regularizer=None,
                 beta_constraint=None,
                 gamma_constraint=None,
                 **kwargs):
        super(InstanceNormalization, self).__init__(**kwargs)
        self.supports_masking = True
        self.axis = axis
        self.epsilon = epsilon
        self.center = center
        self.scale = scale
        self.beta_initializer = initializers.get(beta_initializer)
        self.gamma_initializer = initializers.get(gamma_initializer)
        self.beta_regularizer = regularizers.get(beta_regularizer)
        self.gamma_regularizer = regularizers.get(gamma_regularizer)
        self.beta_constraint = constraints.get(beta_constraint)
        self.gamma_constraint = constraints.get(gamma_constraint) 
Example 13
Project: se_relativisticgan   Author: deepakbaby   File: normalizations.py    License: MIT License 6 votes vote down vote up
def __init__(self, axis=-1, momentum=0.99, center=True, scale=True, epsilon=1e-3,
                 r_max_value=3., d_max_value=5., t_delta=1e-3, weights=None, beta_initializer='zero',
                 gamma_initializer='one', moving_mean_initializer='zeros',
                 moving_variance_initializer='ones', gamma_regularizer=None, beta_regularizer=None,
                 beta_constraint=None, gamma_constraint=None, **kwargs):
        self.supports_masking = True
        self.axis = axis
        self.epsilon = epsilon
        self.center = center
        self.scale = scale
        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
        self.beta_initializer = initializers.get(beta_initializer)
        self.gamma_initializer = initializers.get(gamma_initializer)
        self.moving_mean_initializer = initializers.get(moving_mean_initializer)
        self.moving_variance_initializer = initializers.get(moving_variance_initializer)
        self.beta_constraint = constraints.get(beta_constraint)
        self.gamma_constraint = constraints.get(gamma_constraint)

        super(BatchRenormalization, self).__init__(**kwargs) 
Example 14
Project: keras-utilities   Author: cbaziotis   File: layers.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, u_regularizer=None, b_regularizer=None,
                 W_constraint=None, u_constraint=None, b_constraint=None,
                 bias=True,
                 return_attention=False, **kwargs):

        self.supports_masking = True
        self.return_attention = return_attention
        self.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.bias = bias
        super(AttentionWithContext, self).__init__(**kwargs) 
Example 15
Project: DigiX_HuaWei_Population_Age_Attribution_Predict   Author: WeavingWong   File: models.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, u_regularizer=None, b_regularizer=None,
                 W_constraint=None, u_constraint=None, b_constraint=None,
                 bias=True, **kwargs):

        self.supports_masking = True
        self.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.bias = bias
        super(AttentionWithContext, self).__init__(**kwargs) 
Example 16
Project: DigiX_HuaWei_Population_Age_Attribution_Predict   Author: WeavingWong   File: models.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, u_regularizer=None, b_regularizer=None,
                 W_constraint=None, u_constraint=None, b_constraint=None,
                 bias=True, **kwargs):

        self.supports_masking = True
        self.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.bias = bias
        super(AttentionWithContext, self).__init__(**kwargs) 
Example 17
Project: DigiX_HuaWei_Population_Age_Attribution_Predict   Author: WeavingWong   File: models.py    License: MIT License 6 votes vote down vote up
def on_epoch_end(self, epoch, logs=None):
        logs = logs or {}
        self.epochs_since_last_save += 1
        if self.epochs_since_last_save >= self.period:
            self.epochs_since_last_save = 0
            #filepath = self.filepath.format(epoch=epoch + 1, **logs)
            current = logs.get(self.monitor)
            if current is None:
                warnings.warn('Can pick best model only with %s available, '
                              'skipping.' % (self.monitor), RuntimeWarning)
            else:
                if self.monitor_op(current, self.best):
                    if self.verbose > 0:
                        print('\nEpoch %05d: %s improved from %0.5f to %0.5f,'
                              ' storing weights.'
                              % (epoch + 1, self.monitor, self.best,
                                 current))
                    self.best = current
                    self.best_epochs = epoch + 1
                    self.best_weights = self.model.get_weights()
                else:
                    if self.verbose > 0:
                        print('\nEpoch %05d: %s did not improve' %
                              (epoch + 1, self.monitor)) 
Example 18
Project: DigiX_HuaWei_Population_Age_Attribution_Predict   Author: WeavingWong   File: rnn_feature.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None, u_regularizer=None, b_regularizer=None,
                 W_constraint=None, u_constraint=None, b_constraint=None,
                 bias=True, **kwargs):

        self.supports_masking = True
        self.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.bias = bias
        super(AttentionWithContext, self).__init__(**kwargs) 
Example 19
Project: dts   Author: albertogaspar   File: FFNN.py    License: MIT License 6 votes vote down vote up
def evaluate(self, inputs, fn_inverse=None, fn_plot=None):
        try:
            X, y = inputs
            inputs = X
        except:
            X, conditions, y = inputs
            inputs = [X, conditions]

        y_hat = self.predict(inputs)

        if fn_inverse is not None:
            y_hat = fn_inverse(y_hat)
            y = fn_inverse(y)

        if fn_plot is not None:
            fn_plot([y, y_hat])

        results = []
        for m in self.model.metrics:
            if isinstance(m, str):
                results.append(K.eval(K.mean(get(m)(y, y_hat))))
            else:
                results.append(K.eval(K.mean(m(y, y_hat))))
        return results 
Example 20
Project: naacl18-multitask_argument_mining   Author: UKPLab   File: ChainCRF.py    License: Apache License 2.0 6 votes vote down vote up
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) 
Example 21
Project: Attention-Based-Aspect-Extraction   Author: madrugado   File: my_layers.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self,
                 W_regularizer=None,
                 b_regularizer=None,
                 W_constraint=None,
                 b_constraint=None,
                 bias=True, **kwargs):
        """
            Keras Layer that implements an Content Attention mechanism.
            Supports Masking.
        """
        self.supports_masking = True
        self.init = initializers.get('glorot_uniform')

        self.W_regularizer = regularizers.get(W_regularizer)
        self.b_regularizer = regularizers.get(b_regularizer)
        self.W_constraint = constraints.get(W_constraint)
        self.b_constraint = constraints.get(b_constraint)

        self.bias = bias
        super(Attention, self).__init__(**kwargs) 
Example 22
Project: Attention-Based-Aspect-Extraction   Author: madrugado   File: my_layers.py    License: Apache License 2.0 6 votes vote down vote up
def __init__(self, input_dim, output_dim,
                 init='uniform', input_length=None,
                 W_regularizer=None, activity_regularizer=None,
                 W_constraint=None,
                 weights=None, dropout=0., **kwargs):
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.init = initializers.get(init)
        self.input_length = input_length
        self.dropout = dropout

        self.W_constraint = constraints.get(W_constraint)
        self.W_regularizer = regularizers.get(W_regularizer)
        self.activity_regularizer = regularizers.get(activity_regularizer)

        if 0. < self.dropout < 1.:
            self.uses_learning_phase = True
        self.initial_weights = weights
        kwargs['input_shape'] = (self.input_length,)
        kwargs['input_dtype'] = K.floatx()
        super(WeightedAspectEmb, self).__init__(**kwargs) 
Example 23
Project: keras-contrib   Author: keras-team   File: core.py    License: MIT License 6 votes vote down vote up
def __init__(self, units, kernel_initializer='glorot_uniform',
                 activation=None, weights=None,
                 kernel_regularizer=None, bias_regularizer=None,
                 activity_regularizer=None,
                 kernel_constraint=None, bias_constraint=None,
                 use_bias=True, **kwargs):
        if 'input_shape' not in kwargs and 'input_dim' in kwargs:
            kwargs['input_shape'] = (kwargs.pop('input_dim'),)

        self.kernel_initializer = initializers.get(kernel_initializer)
        self.activation = activations.get(activation)
        self.units = units

        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.initial_weights = weights
        super(CosineDense, self).__init__(**kwargs) 
Example 24
Project: keras-contrib   Author: keras-team   File: pelu.py    License: MIT License 6 votes vote down vote up
def __init__(self, alpha_initializer='ones',
                 alpha_regularizer=None,
                 alpha_constraint=None,
                 beta_initializer='ones',
                 beta_regularizer=None,
                 beta_constraint=None,
                 shared_axes=None,
                 **kwargs):
        super(PELU, 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) 
Example 25
Project: keras-contrib   Author: keras-team   File: capsule.py    License: MIT License 6 votes vote down vote up
def __init__(self,
                 num_capsule,
                 dim_capsule,
                 routings=3,
                 share_weights=True,
                 initializer='glorot_uniform',
                 activation=None,
                 regularizer=None,
                 constraint=None,
                 **kwargs):
        super(Capsule, self).__init__(**kwargs)
        self.num_capsule = num_capsule
        self.dim_capsule = dim_capsule
        self.routings = routings
        self.share_weights = share_weights

        self.activation = activations.get(activation)
        self.regularizer = regularizers.get(regularizer)
        self.initializer = initializers.get(initializer)
        self.constraint = constraints.get(constraint) 
Example 26
Project: keras-mobilenet   Author: rcmalli   File: depthwise_conv2d.py    License: MIT License 5 votes vote down vote up
def __init__(self, filters,
                 kernel_size,
                 strides=(1, 1),
                 padding='valid',
                 data_format=None,
                 depth_multiplier=1,
                 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=filters,
            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) 
Example 27
Project: kaggle-carvana-2017   Author: killthekitten   File: mobile_net_fixed.py    License: MIT License 5 votes vote down vote up
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) 
Example 28
Project: FasterRCNN_KERAS   Author: akshaylamba   File: FixedBatchNormalization.py    License: Apache License 2.0 5 votes vote down vote up
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) 
Example 29
Project: 3DGCN   Author: blackmints   File: layer.py    License: MIT License 5 votes vote down vote up
def __init__(self,
                 filters,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 bias_initializer='zeros',
                 activation=None,
                 **kwargs):
        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.filters = filters

        super(GraphSToS, self).__init__(**kwargs) 
Example 30
Project: 3DGCN   Author: blackmints   File: layer.py    License: MIT License 5 votes vote down vote up
def __init__(self,
                 filters,
                 kernel_initializer='glorot_uniform',
                 kernel_regularizer=None,
                 bias_initializer='zeros',
                 activation=None,
                 **kwargs):
        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.filters = filters

        super(GraphSToV, self).__init__(**kwargs)