Python tensorflow.keras.regularizers.get() Examples
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Example #1
Source File: custom_activation.py From Echo with MIT License | 6 votes |
def call(self, inputs): def brelu(x): # get shape of X, we are interested in the last axis, which is constant shape = K.int_shape(x) # last axis dim = shape[-1] # half of the last axis (+1 if necessary) dim2 = dim // 2 if dim % 2 != 0: dim2 += 1 # multiplier will be a tensor of alternated +1 and -1 multiplier = K.ones((dim2,)) multiplier = K.stack([multiplier, -multiplier], axis=-1) if dim % 2 != 0: multiplier = multiplier[:-1] # adjust multiplier shape to the shape of x multiplier = K.reshape(multiplier, tuple(1 for _ in shape[:-1]) + (-1,)) return multiplier * tf.nn.relu(multiplier * x) return Lambda(brelu)(inputs)
Example #2
Source File: topk_pool.py From spektral with MIT License | 6 votes |
def __init__(self, ratio, return_mask=False, sigmoid_gating=False, kernel_initializer='glorot_uniform', kernel_regularizer=None, kernel_constraint=None, **kwargs): super().__init__(**kwargs) self.ratio = ratio self.return_mask = return_mask self.sigmoid_gating = sigmoid_gating self.gating_op = K.sigmoid if self.sigmoid_gating else K.tanh self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint)
Example #3
Source File: global_pool.py From spektral with MIT License | 6 votes |
def __init__(self, channels, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super().__init__(**kwargs) self.channels = channels 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.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint)
Example #4
Source File: diff_pool.py From spektral with MIT License | 6 votes |
def __init__(self, k, channels=None, return_mask=False, activation=None, kernel_initializer='glorot_uniform', kernel_regularizer=None, kernel_constraint=None, **kwargs): super().__init__(**kwargs) self.k = k self.channels = channels self.return_mask = return_mask self.activation = activations.get(activation) self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint)
Example #5
Source File: base.py From megnet with BSD 3-Clause "New" or "Revised" License | 6 votes |
def __init__(self, activation: OptStrOrCallable = None, use_bias: bool = True, kernel_initializer: OptStrOrCallable = 'glorot_uniform', bias_initializer: OptStrOrCallable = 'zeros', kernel_regularizer: OptStrOrCallable = None, bias_regularizer: OptStrOrCallable = None, activity_regularizer: OptStrOrCallable = None, kernel_constraint: OptStrOrCallable = None, bias_constraint: OptStrOrCallable = None, **kwargs): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) self.activation = activations.get(activation) # noqa 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) super().__init__(**kwargs)
Example #6
Source File: graph_conv.py From spektral with MIT License | 6 votes |
def __init__(self, channels, 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().__init__(activity_regularizer=activity_regularizer, **kwargs) self.channels = channels 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.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.supports_masking = False
Example #7
Source File: UniRepModel.py From tape-neurips2019 with MIT License | 6 votes |
def convert_sequence_vocab(self, sequence, sequence_lengths): PFAM_TO_UNIREP_ENCODED = {encoding: UNIREP_VOCAB.get(aa, 23) for aa, encoding in PFAM_VOCAB.items()} def to_uniprot_unirep(seq, seqlens): new_seq = np.zeros_like(seq) for pfam_encoding, unirep_encoding in PFAM_TO_UNIREP_ENCODED.items(): new_seq[seq == pfam_encoding] = unirep_encoding # add start/stop new_seq = np.pad(new_seq, [[0, 0], [1, 1]], mode='constant') new_seq[:, 0] = UNIREP_VOCAB['<START>'] new_seq[np.arange(new_seq.shape[0]), seqlens + 1] = UNIREP_VOCAB['<STOP>'] return new_seq new_sequence = tf.py_func(to_uniprot_unirep, [sequence, sequence_lengths], sequence.dtype) new_sequence.set_shape([sequence.shape[0], sequence.shape[1] + 2]) return new_sequence
Example #8
Source File: groupnorm.py From bcnn with MIT License | 5 votes |
def __init__(self, groups=4, axis=-1, epsilon=1e-5, center=True, scale=True, beta_initializer="zeros", gamma_initializer="ones", beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, **kwargs): super(GroupNormalization, self).__init__(**kwargs) self.supports_masking = True self.groups = groups 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 #9
Source File: custom_activation.py From Echo with MIT License | 5 votes |
def __init__( self, alpha_initializer="zeros", b_initializer="zeros", S=1, alpha_regularizer=None, b_regularizer=None, alpha_constraint=None, b_constraint=None, shared_axes=None, **kwargs ): super(APL, 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.b_initializer = initializers.get(b_initializer) self.b_regularizer = regularizers.get(b_regularizer) self.b_constraint = constraints.get(b_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) self.S = S self.alpha_arr = [] self.b_arr = []
Example #10
Source File: FRN.py From TF.Keras-Commonly-used-models with Apache License 2.0 | 5 votes |
def __init__(self, epsilon=1e-6, beta_initializer='zeros', gamma_initializer='ones', tau_initializers='zeros', beta_regularizer=None, gamma_regularizer=None, tau_regularizer=None, beta_constraint=None, gamma_constraint=None, tau_constraint=None, **kwargs): super(FRN, self).__init__(**kwargs) self.supports_masking = True self.epsilon = epsilon self.beta_initializer = initializers.get(beta_initializer) self.tau_initializer = initializers.get(tau_initializers) self.gamma_initializer = initializers.get(gamma_initializer) self.beta_regularizer = regularizers.get(beta_regularizer) self.gamma_regularizer = regularizers.get(gamma_regularizer) self.tau_regularizer = regularizers.get(tau_regularizer) self.beta_constraint = constraints.get(beta_constraint) self.gamma_constraint = constraints.get(gamma_constraint) self.tau_constraint = constraints.get(tau_constraint) self.tau = None self.gamma = None self.beta = None self.axis = -1
Example #11
Source File: set2set.py From megnet with BSD 3-Clause "New" or "Revised" License | 5 votes |
def __init__(self, T=3, n_hidden=512, activation=None, activation_lstm='tanh', recurrent_activation='hard_sigmoid', kernel_initializer='glorot_uniform', recurrent_initializer='orthogonal', bias_initializer='zeros', use_bias=True, unit_forget_bias=True, kernel_regularizer=None, recurrent_regularizer=None, bias_regularizer=None, kernel_constraint=None, recurrent_constraint=None, bias_constraint=None, **kwargs): super().__init__(**kwargs) 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.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint) self.activation_lstm = activations.get(activation_lstm) self.recurrent_activation = activations.get(recurrent_activation) self.recurrent_initializer = initializers.get(recurrent_initializer) self.unit_forget_bias = unit_forget_bias self.recurrent_regularizer = regularizers.get(recurrent_regularizer) self.recurrent_constraint = constraints.get(recurrent_constraint) self.T = T self.n_hidden = n_hidden
Example #12
Source File: group_norm.py From 3d-brain-tumor-segmentation with Apache License 2.0 | 5 votes |
def __init__(self, groups=8, axis=-1, epsilon=1e-5, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None, **kwargs): """ Initializes one group normalization layer. References: - [Group Normalization](https://arxiv.org/abs/1803.08494) """ super(GroupNormalization, self).__init__(**kwargs) self.supports_masking = True self.groups = groups 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
Source File: se_mobilenets.py From keras-squeeze-excite-network with MIT License | 5 votes |
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.depthwise_kernel = None self.bias = None
Example #14
Source File: conv_mod.py From StyleGAN2-Tensorflow-2.0 with MIT License | 5 votes |
def __init__(self, filters, kernel_size, strides=1, padding='valid', dilation_rate=1, kernel_initializer='glorot_uniform', kernel_regularizer=None, activity_regularizer=None, kernel_constraint=None, demod=True, **kwargs): super(Conv2DMod, self).__init__(**kwargs) self.filters = filters self.rank = 2 self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size') self.strides = conv_utils.normalize_tuple(strides, 2, 'strides') self.padding = conv_utils.normalize_padding(padding) self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, 'dilation_rate') self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.kernel_constraint = constraints.get(kernel_constraint) self.demod = demod self.input_spec = [InputSpec(ndim = 4), InputSpec(ndim = 2)]
Example #15
Source File: mincut_pool.py From spektral with MIT License | 5 votes |
def __init__(self, k, mlp_hidden=None, mlp_activation='relu', return_mask=False, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs): super().__init__(**kwargs) self.k = k self.mlp_hidden = mlp_hidden if mlp_hidden else [] self.mlp_activation = mlp_activation self.return_mask = return_mask 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.kernel_constraint = constraints.get(kernel_constraint) self.bias_constraint = constraints.get(bias_constraint)
Example #16
Source File: base.py From spektral with MIT License | 5 votes |
def __init__(self, input_dim_1=None, activation=None, **kwargs): super(MinkowskiProduct, self).__init__(**kwargs) self.input_dim_1 = input_dim_1 self.activation = activations.get(activation)
Example #17
Source File: base.py From spektral with MIT License | 5 votes |
def __init__(self, trainable_kernel=False, activation=None, kernel_initializer='glorot_uniform', kernel_regularizer=None, kernel_constraint=None, **kwargs): super().__init__(**kwargs) self.trainable_kernel = trainable_kernel self.activation = activations.get(activation) self.kernel_initializer = initializers.get(kernel_initializer) self.kernel_regularizer = regularizers.get(kernel_regularizer) self.kernel_constraint = constraints.get(kernel_constraint)
Example #18
Source File: keras.py From spektral with MIT License | 5 votes |
def deserialize_kwarg(key, attr): if key.endswith('_initializer'): return initializers.get(attr) if key.endswith('_regularizer'): return regularizers.get(attr) if key.endswith('_constraint'): return constraints.get(attr) if key == 'activation': return activations.get(attr)
Example #19
Source File: conv2d_mpo.py From TensorNetwork with Apache License 2.0 | 4 votes |
def __init__(self, filters: int, kernel_size: Union[int, Tuple[int, int]], num_nodes: int, bond_dim: int, strides: Union[int, Tuple[int, int]] = 1, padding: Text = "same", data_format: Optional[Text] = "channels_last", dilation_rate: Union[int, Tuple[int, int]] = (1, 1), activation: Optional[Text] = None, use_bias: bool = True, kernel_initializer: Text = "glorot_uniform", bias_initializer: Text = "zeros", kernel_regularizer: Optional[Text] = None, bias_regularizer: Optional[Text] = None, **kwargs) -> None: if num_nodes < 2: raise ValueError('Need at least 2 nodes to create MPO') if padding not in ('same', 'valid'): raise ValueError('Padding must be "same" or "valid"') if data_format not in ['channels_first', 'channels_last']: raise ValueError('Invalid data_format string provided') super(Conv2DMPO, self).__init__(**kwargs) self.nodes = [] self.filters = filters self.kernel_size = conv_utils.normalize_tuple(kernel_size, 2, 'kernel_size') self.num_nodes = num_nodes self.bond_dim = bond_dim self.strides = conv_utils.normalize_tuple(strides, 2, 'kernel_size') self.padding = padding self.data_format = data_format self.dilation_rate = conv_utils.normalize_tuple(dilation_rate, 2, '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)
Example #20
Source File: graph_attention.py From spektral with MIT License | 4 votes |
def __init__(self, channels, attn_heads=1, concat_heads=True, dropout_rate=0.5, return_attn_coef=False, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', attn_kernel_initializer='glorot_uniform', kernel_regularizer=None, bias_regularizer=None, attn_kernel_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, attn_kernel_constraint=None, **kwargs): super().__init__(channels, activation=activation, use_bias=use_bias, kernel_initializer=kernel_initializer, bias_initializer=bias_initializer, kernel_regularizer=kernel_regularizer, bias_regularizer=bias_regularizer, activity_regularizer=activity_regularizer, kernel_constraint=kernel_constraint, bias_constraint=bias_constraint, **kwargs) self.attn_heads = attn_heads self.concat_heads = concat_heads self.dropout_rate = dropout_rate self.return_attn_coef = return_attn_coef self.attn_kernel_initializer = initializers.get(attn_kernel_initializer) self.attn_kernel_regularizer = regularizers.get(attn_kernel_regularizer) self.attn_kernel_constraint = constraints.get(attn_kernel_constraint) if concat_heads: # Output will have shape (..., attention_heads * channels) self.output_dim = self.channels * self.attn_heads else: # Output will have shape (..., channels) self.output_dim = self.channels