这篇教程Python layers.InputSpec方法代码示例写得很实用,希望能帮到您。
本文整理汇总了Python中keras.layers.InputSpec方法的典型用法代码示例。如果您正苦于以下问题:Python layers.InputSpec方法的具体用法?Python layers.InputSpec怎么用?Python layers.InputSpec使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块keras.layers 的用法示例。 在下文中一共展示了layers.InputSpec方法的28个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于我们的系统推荐出更棒的Python代码示例。 示例1: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): self._validate_input_shape(input_shape) self.input_spec = InputSpec(shape=input_shape) if not self.layer.built: self.layer.build(input_shape) self.layer.built = True input_dim = input_shape[-1] if self.layer.return_sequences: output_dim = self.layer.compute_output_shape(input_shape)[0][-1] else: output_dim = self.layer.compute_output_shape(input_shape)[-1] self._W1 = self.add_weight(shape=(input_dim, input_dim), name="{}_W1".format(self.name), initializer=self.weight_initializer) self._W2 = self.add_weight(shape=(output_dim, input_dim), name="{}_W2".format(self.name), initializer=self.weight_initializer) self._W3 = self.add_weight(shape=(2*input_dim, input_dim), name="{}_W3".format(self.name), initializer=self.weight_initializer) self._b2 = self.add_weight(shape=(input_dim,), name="{}_b2".format(self.name), initializer=self.weight_initializer) self._b3 = self.add_weight(shape=(input_dim,), name="{}_b3".format(self.name), initializer=self.weight_initializer) self._V = self.add_weight(shape=(input_dim,1), name="{}_V".format(self.name), initializer=self.weight_initializer) super(AttentionRNNWrapper, self).build()
开发者ID:zimmerrol,项目名称:keras-utility-layer-collection,代码行数:26,代码来源:attention.py
示例2: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, nb_filters_in, nb_filters_out, nb_filters_att, nb_rows, nb_cols, init='normal', inner_init='orthogonal', attentive_init='zero', activation='tanh', inner_activation='sigmoid', W_regularizer=None, U_regularizer=None, weights=None, go_backwards=False, **kwargs): self.nb_filters_in = nb_filters_in self.nb_filters_out = nb_filters_out self.nb_filters_att = nb_filters_att self.nb_rows = nb_rows self.nb_cols = nb_cols self.init = initializations.get(init) self.inner_init = initializations.get(inner_init) self.attentive_init = initializations.get(attentive_init) self.activation = activations.get(activation) self.inner_activation = activations.get(inner_activation) self.initial_weights = weights self.go_backwards = go_backwards self.W_regularizer = W_regularizer self.U_regularizer = U_regularizer self.input_spec = [InputSpec(ndim=5)] super(AttentiveConvLSTM, self).__init__(**kwargs)
示例3: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]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)
示例4: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): assert len(input_shape) == 2 input_dim = input_shape[1] #self.input_spec = InputSpec(dtype=K.floatx(), shape=(None, input_dim)) self.input_spec = InputSpec(ndim=2, axes={1: input_dim}) self.W = self.add_weight( shape=(input_dim, self.output_dim), initializer=self.W_initializer, name='SparseFullyConnected_W', regularizer=self.W_regularizer, constraint=self.W_constraint) self.b = self.add_weight( shape=(self.output_dim,), initializer=self.b_initializer, name='SparseFullyConnected_b', regularizer=self.b_regularizer, constraint=self.b_constraint) if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights #self.built = True #super(SparseFullyConnectedLayer, self).build(input_shape)
示例5: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): input_dim = input_shape[2] self.input_dim = input_dim self.input_spec = [InputSpec(shape=input_shape)] self.kernel_shape = (self.window_size, 1, input_dim, self.output_dim * 2) self.kernel = self.add_weight(self.kernel_shape, initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) if self.use_bias: self.bias = self.add_weight((self.output_dim * 2,), initializer=self.bias_initializer, name='b', regularizer=self.bias_regularizer, constraint=self.bias_constraint) self.built = True
开发者ID:DingKe,项目名称:nn_playground,代码行数:21,代码来源:gcnn.py
示例6: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, ch_j, n_j, r_num=1, b_alphas=[8, 8, 8], kernel_initializer='glorot_uniform', kernel_regularizer=None, activity_regularizer=None, kernel_constraint=None, **kwargs): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) super(DenseCaps, self).__init__(**kwargs) self.ch_j = ch_j # number of capsules in layer J self.n_j = n_j # number of neurons in a capsule in J self.r_num = r_num self.b_alphas = b_alphas 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.input_spec = InputSpec(min_ndim=3) self.supports_masking = True
示例7: call# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def call(self, x, mask=None, **kwargs): input_shape = K.int_shape(x) res = super(ShareableGRU, self).call(x, mask, **kwargs) self.input_spec = [InputSpec(shape=(self.input_spec[0].shape[0], None, self.input_spec[0].shape[2]))] if K.ndim(x) == K.ndim(res): # A recent change in Keras # (https://github.com/fchollet/keras/commit/a9b6bef0624c67d6df1618ca63d8e8141b0df4d0) # made it so that K.rnn with a tensorflow backend does not retain shape information for # the sequence length, even if it's present in the input. We need to fix that here so # that our models have the right shape information. A simple K.reshape is good enough # to fix this. result_shape = K.int_shape(res) if input_shape[1] is not None and result_shape[1] is None: shape = (input_shape[0] if input_shape[0] is not None else -1, input_shape[1], result_shape[2]) res = K.reshape(res, shape=shape) return res
示例8: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): if isinstance(input_shape, tuple): input_shape = [input_shape] assert all(len(shape) >= 3 for shape in input_shape), "Need 3 dims to TimeDistribute" all_timesteps = [i[1] for i in input_shape] assert len(set(all_timesteps)) == 1, "Tensors must have same number of timesteps" self.input_spec = [InputSpec(shape=shape) for shape in input_shape] if not self.layer.built: child_input_shape = [(shape[0],) + shape[2:] for shape in input_shape] if len(input_shape) == 1: child_input_shape = child_input_shape[0] self.layer.build(child_input_shape) self.layer.built = True self.built = True # It's important that we call Wrapper.build() here, because it sets some important member # variables. But we can't call KerasTimeDistributed.build(), because it assumes only one # input, which we're trying to fix. So we use super(KerasTimeDistributed, self).build() # here on purpose - this is not a copy-paste bug. super(KerasTimeDistributed, self).build(input_shape) # pylint: disable=bad-super-call
示例9: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, layer, weight_initializer="glorot_uniform", return_attention=False, **kwargs): assert isinstance(layer, RNN) self.layer = layer self.supports_masking = True self.weight_initializer = weight_initializer self.return_attention = return_attention self._num_constants = None super(ExternalAttentionRNNWrapper, self).__init__(layer, **kwargs) self.input_spec = [InputSpec(ndim=3), InputSpec(ndim=3)]
开发者ID:zimmerrol,项目名称:keras-utility-layer-collection,代码行数:13,代码来源:attention.py
示例10: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, units, activation=None, use_bias=True, init_criterion='he', kernel_initializer='complex', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, seed=None, **kwargs): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) super(ComplexDense, self).__init__(**kwargs) self.units = units self.activation = activations.get(activation) self.use_bias = use_bias self.init_criterion = init_criterion if kernel_initializer in {'complex'}: self.kernel_initializer = kernel_initializer else: 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) if seed is None: self.seed = np.random.randint(1, 10e6) else: self.seed = seed self.input_spec = InputSpec(ndim=2) self.supports_masking = True
开发者ID:ChihebTrabelsi,项目名称:deep_complex_networks,代码行数:38,代码来源:dense.py
示例11: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): self.input_spec = InputSpec(ndim=len(input_shape), axes={self.axis: input_shape[self.axis]}) shape = (input_shape[self.axis],) self.gamma = self.add_weight(shape, initializer=self.gamma_init, regularizer=self.gamma_regularizer, name='{}_gamma'.format(self.name)) self.beta = self.add_weight(shape, initializer=self.beta_init, regularizer=self.beta_regularizer, name='{}_beta'.format(self.name)) self.built = True
开发者ID:ChihebTrabelsi,项目名称:deep_complex_networks,代码行数:17,代码来源:norm.py
示例12: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): kernel_shape_f_g = (1, 1) + (self.channels, self.filters_f_g) kernel_shape_h = (1, 1) + (self.channels, self.filters_h) # Create a trainable weight variable for this layer: self.gamma = self.add_weight(name='gamma', shape=[1], initializer='zeros', trainable=True) self.kernel_f = self.add_weight(shape=kernel_shape_f_g, initializer='glorot_uniform', name='kernel_f') self.kernel_g = self.add_weight(shape=kernel_shape_f_g, initializer='glorot_uniform', name='kernel_g') self.kernel_h = self.add_weight(shape=kernel_shape_h, initializer='glorot_uniform', name='kernel_h') self.bias_f = self.add_weight(shape=(self.filters_f_g,), initializer='zeros', name='bias_F') self.bias_g = self.add_weight(shape=(self.filters_f_g,), initializer='zeros', name='bias_g') self.bias_h = self.add_weight(shape=(self.filters_h,), initializer='zeros', name='bias_h') super(Attention, self).build(input_shape) # Set input spec. self.input_spec = InputSpec(ndim=4, axes={3: input_shape[-1]}) self.built = True
开发者ID:emilwallner,项目名称:Coloring-greyscale-images,代码行数:31,代码来源:attention.py
示例13: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): # This currently only works for 4D inputs: assuming (B, H, W, C) self.input_spec = [InputSpec(shape=input_shape)] shape = (1, 1, 1, input_shape[-1]) self.gamma = self.gamma_init(shape, name='{}_gamma'.format(self.name)) self.beta = self.beta_init(shape, name='{}_beta'.format(self.name)) self.trainable_weights = [self.gamma, self.beta] self.built = True
开发者ID:robertomest,项目名称:neural-style-keras,代码行数:12,代码来源:layers.py
示例14: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, units, activation=None, use_bias=True, init_criterion='he', kernel_initializer='quaternion', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, seed=None, **kwargs): if 'input_shape' not in kwargs and 'input_dim' in kwargs: kwargs['input_shape'] = (kwargs.pop('input_dim'),) super(QuaternionDense, self).__init__(**kwargs) self.units = units self.q_units = units // 4 self.activation = activations.get(activation) self.use_bias = use_bias self.init_criterion = init_criterion self.kernel_initializer = 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) if seed is None: self.seed = np.random.randint(1, 10e6) else: self.seed = seed self.input_spec = InputSpec(ndim=2) self.supports_masking = True
开发者ID:Orkis-Research,项目名称:Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition,代码行数:36,代码来源:dense.py
示例15: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): assert len(input_shape) == 2 assert input_shape[-1] % 2 == 0 input_dim = input_shape[-1] // 4 data_format = K.image_data_format() kernel_shape = (input_dim, self.units) init_shape = (input_dim, self.q_units) self.kernel_init = qdense_init(init_shape, self.init_criterion) self.kernel = self.add_weight( shape=kernel_shape, initializer=self.kernel_init, name='r', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint ) if self.use_bias: self.bias = self.add_weight( shape=(self.units,), initializer='zeros', name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint ) else: self.bias = None self.input_spec = InputSpec(ndim=2, axes={-1: 4 * input_dim}) self.built = True
开发者ID:Orkis-Research,项目名称:Quaternion-Convolutional-Neural-Networks-for-End-to-End-Automatic-Speech-Recognition,代码行数:34,代码来源:dense.py
示例16: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]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)
开发者ID:amansrivastava17,项目名称:embedding-as-service,代码行数:38,代码来源:qrnn.py
示例17: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): if isinstance(input_shape, list): input_shape = input_shape[0] batch_size = input_shape[0] if self.stateful else None self.input_dim = input_shape[2] self.input_spec = InputSpec(shape=(batch_size, None, self.input_dim)) self.state_spec = InputSpec(shape=(batch_size, self.units)) self.states = [None] if self.stateful: self.reset_states() kernel_shape = (self.window_size, 1, self.input_dim, self.units * 3) self.kernel = self.add_weight(name='kernel', shape=kernel_shape, initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) if self.use_bias: self.bias = self.add_weight(name='bias', shape=(self.units * 3,), initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) self.built = True
开发者ID:amansrivastava17,项目名称:embedding-as-service,代码行数:29,代码来源:qrnn.py
示例18: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, size=(1, 1), target_size=None, data_format='default', **kwargs): if data_format == 'default': data_format = KB.image_data_format() self.size = tuple(size) if target_size is not None: self.target_size = tuple(target_size) else: self.target_size = None assert data_format in { 'channels_last', 'channels_first'}, 'data_format must be in {tf, th}' self.data_format = data_format self.input_spec = [KL.InputSpec(ndim=4)] super(BilinearUpSampling2D, self).__init__(**kwargs)
开发者ID:waspinator,项目名称:deep-learning-explorer,代码行数:15,代码来源:layers.py
示例19: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): input_shape = to_tuple(input_shape) self.input_spec = [InputSpec(shape=input_shape)] self.input_dim = input_shape[-1] self.kernel = self.add_weight(shape=(self.input_dim, self.units), name='kernel', initializer=self.kernel_initializer, regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) self.chain_kernel = self.add_weight(shape=(self.units, self.units), name='chain_kernel', initializer=self.chain_initializer, regularizer=self.chain_regularizer, constraint=self.chain_constraint) if self.use_bias: self.bias = self.add_weight(shape=(self.units,), name='bias', initializer=self.bias_initializer, regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = 0 if self.use_boundary: self.left_boundary = self.add_weight(shape=(self.units,), name='left_boundary', initializer=self.boundary_initializer, regularizer=self.boundary_regularizer, constraint=self.boundary_constraint) self.right_boundary = self.add_weight(shape=(self.units,), name='right_boundary', initializer=self.boundary_initializer, regularizer=self.boundary_regularizer, constraint=self.boundary_constraint) self.built = True
开发者ID:keras-team,项目名称:keras-contrib,代码行数:38,代码来源:crf.py
示例20: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): input_shape = to_tuple(input_shape) ndim = len(input_shape) assert ndim >= 2 input_dim = input_shape[-1] self.input_dim = input_dim self.input_spec = [InputSpec(dtype=K.floatx(), ndim=ndim)] self.kernel = self.add_weight(shape=(input_dim, self.units), initializer=self.kernel_initializer, name='{}_W'.format(self.name), regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) if self.use_bias: self.bias = self.add_weight(shape=(self.units,), initializer='zero', name='{}_b'.format(self.name), regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.bias = None if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights self.built = True
开发者ID:keras-team,项目名称:keras-contrib,代码行数:29,代码来源:core.py
示例21: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): input_shape = to_tuple(input_shape) param_shape = list(input_shape[1:]) self.param_broadcast = [False] * len(param_shape) if self.shared_axes is not None: for i in self.shared_axes: param_shape[i - 1] = 1 self.param_broadcast[i - 1] = True param_shape = tuple(param_shape) # Initialised as ones to emulate the default ELU self.alpha = self.add_weight(shape=param_shape, name='alpha', initializer=self.alpha_initializer, regularizer=self.alpha_regularizer, constraint=self.alpha_constraint) self.beta = self.add_weight(shape=param_shape, name='beta', initializer=self.beta_initializer, regularizer=self.beta_regularizer, constraint=self.beta_constraint) # Set input spec axes = {} if self.shared_axes: for i in range(1, len(input_shape)): if i not in self.shared_axes: axes[i] = input_shape[i] self.input_spec = InputSpec(ndim=len(input_shape), axes=axes) self.built = True
开发者ID:keras-team,项目名称:keras-contrib,代码行数:32,代码来源:pelu.py
示例22: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): input_shape = to_tuple(input_shape) param_shape = list(input_shape[1:]) self.param_broadcast = [False] * len(param_shape) if self.shared_axes is not None: for i in self.shared_axes: param_shape[i - 1] = 1 self.param_broadcast[i - 1] = True param_shape = tuple(param_shape) self.t_left = self.add_weight(shape=param_shape, name='t_left', initializer=self.t_left_initializer) self.a_left = self.add_weight(shape=param_shape, name='a_left', initializer=self.a_left_initializer) self.t_right = self.add_weight(shape=param_shape, name='t_right', initializer=self.t_right_initializer) self.a_right = self.add_weight(shape=param_shape, name='a_right', initializer=self.a_right_initializer) # Set input spec axes = {} if self.shared_axes: for i in range(1, len(input_shape)): if i not in self.shared_axes: axes[i] = input_shape[i] self.input_spec = InputSpec(ndim=len(input_shape), axes=axes) self.built = True
开发者ID:keras-team,项目名称:keras-contrib,代码行数:37,代码来源:srelu.py
示例23: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): dim = input_shape[self.axis] if dim is None: raise ValueError('Axis ' + str(self.axis) + ' of ' 'input tensor should have a defined dimension ' 'but the layer received an input with shape ' + str(input_shape) + '.') if dim < self.groups: raise ValueError('Number of groups (' + str(self.groups) + ') cannot be ' 'more than the number of channels (' + str(dim) + ').') if dim % self.groups != 0: raise ValueError('Number of groups (' + str(self.groups) + ') must be a ' 'multiple of the number of channels (' + str(dim) + ').') self.input_spec = InputSpec(ndim=len(input_shape), axes={self.axis: dim}) shape = (dim,) if self.scale: self.gamma = self.add_weight(shape=shape, name='gamma', initializer=self.gamma_initializer, regularizer=self.gamma_regularizer, constraint=self.gamma_constraint) else: self.gamma = None if self.center: self.beta = self.add_weight(shape=shape, name='beta', initializer=self.beta_initializer, regularizer=self.beta_regularizer, constraint=self.beta_constraint) else: self.beta = None self.built = True
示例24: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]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)
示例25: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def __init__(self, nb_gaussian, init='normal', weights=None, W_regularizer=None, activity_regularizer=None, W_constraint=None, **kwargs): self.nb_gaussian = nb_gaussian self.init = initializations.get(init, dim_ordering='th') self.W_regularizer = regularizers.get(W_regularizer) self.activity_regularizer = regularizers.get(activity_regularizer) self.W_constraint = constraints.get(W_constraint) self.input_spec = [InputSpec(ndim=4)] self.initial_weights = weights super(LearningPrior, self).__init__(**kwargs)
示例26: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): self.input_spec = [InputSpec(ndim=3)] assert len(input_shape) == 3 self.w = self.add_weight(shape=(input_shape[2], 1), name='{}_w'.format(self.name), initializer=self.init) self.trainable_weights = [self.w] super(AttentionWeightedAverage, self).build(input_shape)
开发者ID:tsterbak,项目名称:keras_attention,代码行数:11,代码来源:models.py
示例27: build# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]def build(self, input_shape): assert len(input_shape) >= 2 input_dim = input_shape[1] if self.H == 'Glorot': self.H = np.float32(np.sqrt(1.5 / (input_dim + self.units))) #print('Glorot H: {}'.format(self.H)) if self.kernel_lr_multiplier == 'Glorot': self.kernel_lr_multiplier = np.float32(1. / np.sqrt(1.5 / (input_dim + self.units))) #print('Glorot learning rate multiplier: {}'.format(self.kernel_lr_multiplier)) self.kernel_constraint = Clip(-self.H, self.H) self.kernel_initializer = initializers.RandomUniform(-self.H, self.H) self.kernel = self.add_weight(shape=(input_dim, self.units), initializer=self.kernel_initializer, name='kernel', regularizer=self.kernel_regularizer, constraint=self.kernel_constraint) if self.use_bias: self.lr_multipliers = [self.kernel_lr_multiplier, self.bias_lr_multiplier] self.bias = self.add_weight(shape=(self.output_dim,), initializer=self.bias_initializer, name='bias', regularizer=self.bias_regularizer, constraint=self.bias_constraint) else: self.lr_multipliers = [self.kernel_lr_multiplier] self.bias = None self.input_spec = InputSpec(min_ndim=2, axes={-1: input_dim}) self.built = True
示例28: __init__# 需要导入模块: from keras import layers [as 别名]# 或者: from keras.layers import InputSpec [as 别名]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.recurrent_dropout = 0 #not used, added to maintain compatibility with keras.Bidirectional 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)
开发者ID:DingKe,项目名称:nn_playground,代码行数:39,代码来源:qrnn.py
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