Python tensorflow.python.keras.layers.Lambda() Examples

The following are 6 code examples of tensorflow.python.keras.layers.Lambda(). 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. You may also want to check out all available functions/classes of the module tensorflow.python.keras.layers , or try the search function .
Example #1
Source Project: cxplain   Author: d909b   File: rnn.py    License: MIT License 6 votes vote down vote up
def build(self, input_layer):
        last_layer = input_layer
        input_shape = K.int_shape(input_layer)

        if self.with_embedding:
            if input_shape[-1] != 1:
                raise ValueError("Only one feature (the index) can be used with embeddings, "
                                 "i.e. the input shape should be (num_samples, length, 1). "
                                 "The actual shape was: " + str(input_shape))

            last_layer = Lambda(lambda x: K.squeeze(x, axis=-1),
                                output_shape=K.int_shape(last_layer)[:-1])(last_layer)  # Remove feature dimension.
            last_layer = Embedding(self.embedding_size, self.embedding_dimension,
                                   input_length=input_shape[-2])(last_layer)

        for _ in range(self.num_layers):
            last_layer = Dense(self.num_units, activation=self.activation)(last_layer)
            if self.with_bn:
                last_layer = BatchNormalization()(last_layer)
            if not np.isclose(self.p_dropout, 0):
                last_layer = Dropout(self.p_dropout)(last_layer)
        return last_layer 
Example #2
Source Project: icme2019   Author: ShenDezhou   File: sequence.py    License: MIT License 5 votes vote down vote up
def call(self, inputs, mask=None, **kwargs):

        input_fw = inputs
        input_bw = inputs
        for i in range(self.layers):
            output_fw = self.fw_lstm[i](input_fw)
            output_bw = self.bw_lstm[i](input_bw)
            output_bw = Lambda(lambda x: K.reverse(
                x, 1), mask=lambda inputs, mask: mask)(output_bw)

            if i >= self.layers - self.res_layers:
                output_fw += input_fw
                output_bw += input_bw
            input_fw = output_fw
            input_bw = output_bw

        output_fw = input_fw
        output_bw = input_bw

        if self.merge_mode == "fw":
            output = output_fw
        elif self.merge_mode == "bw":
            output = output_bw
        elif self.merge_mode == 'concat':
            output = K.concatenate([output_fw, output_bw])
        elif self.merge_mode == 'sum':
            output = output_fw + output_bw
        elif self.merge_mode == 'ave':
            output = (output_fw + output_bw) / 2
        elif self.merge_mode == 'mul':
            output = output_fw * output_bw
        elif self.merge_mode is None:
            output = [output_fw, output_bw]

        return output 
Example #3
Source Project: GraphEmbedding   Author: shenweichen   File: line.py    License: MIT License 5 votes vote down vote up
def create_model(numNodes, embedding_size, order='second'):

    v_i = Input(shape=(1,))
    v_j = Input(shape=(1,))

    first_emb = Embedding(numNodes, embedding_size, name='first_emb')
    second_emb = Embedding(numNodes, embedding_size, name='second_emb')
    context_emb = Embedding(numNodes, embedding_size, name='context_emb')

    v_i_emb = first_emb(v_i)
    v_j_emb = first_emb(v_j)

    v_i_emb_second = second_emb(v_i)
    v_j_context_emb = context_emb(v_j)

    first = Lambda(lambda x: tf.reduce_sum(
        x[0]*x[1], axis=-1, keep_dims=False), name='first_order')([v_i_emb, v_j_emb])
    second = Lambda(lambda x: tf.reduce_sum(
        x[0]*x[1], axis=-1, keep_dims=False), name='second_order')([v_i_emb_second, v_j_context_emb])

    if order == 'first':
        output_list = [first]
    elif order == 'second':
        output_list = [second]
    else:
        output_list = [first, second]

    model = Model(inputs=[v_i, v_j], outputs=output_list)

    return model, {'first': first_emb, 'second': second_emb} 
Example #4
def trivial_model(num_classes):
  """Trivial model for ImageNet dataset."""

  input_shape = (224, 224, 3)
  img_input = layers.Input(shape=input_shape)

  x = layers.Lambda(lambda x: backend.reshape(x, [-1, 224 * 224 * 3]),
                    name='reshape')(img_input)
  x = layers.Dense(1, name='fc1')(x)
  x = layers.Dense(num_classes, name='fc1000')(x)
  x = layers.Activation('softmax', dtype='float32')(x)

  return models.Model(img_input, x, name='trivial') 
Example #5
Source Project: DeepCTR   Author: shenweichen   File: sequence.py    License: Apache License 2.0 5 votes vote down vote up
def call(self, inputs, mask=None, **kwargs):

        input_fw = inputs
        input_bw = inputs
        for i in range(self.layers):
            output_fw = self.fw_lstm[i](input_fw)
            output_bw = self.bw_lstm[i](input_bw)
            output_bw = Lambda(lambda x: K.reverse(
                x, 1), mask=lambda inputs, mask: mask)(output_bw)

            if i >= self.layers - self.res_layers:
                output_fw += input_fw
                output_bw += input_bw
            input_fw = output_fw
            input_bw = output_bw

        output_fw = input_fw
        output_bw = input_bw

        if self.merge_mode == "fw":
            output = output_fw
        elif self.merge_mode == "bw":
            output = output_bw
        elif self.merge_mode == 'concat':
            output = K.concatenate([output_fw, output_bw])
        elif self.merge_mode == 'sum':
            output = output_fw + output_bw
        elif self.merge_mode == 'ave':
            output = (output_fw + output_bw) / 2
        elif self.merge_mode == 'mul':
            output = output_fw * output_bw
        elif self.merge_mode is None:
            output = [output_fw, output_bw]

        return output 
Example #6
Source Project: models   Author: tensorflow   File: test_utils.py    License: Apache License 2.0 5 votes vote down vote up
def trivial_model(num_classes):
  """Trivial model for ImageNet dataset."""

  input_shape = (224, 224, 3)
  img_input = layers.Input(shape=input_shape)

  x = layers.Lambda(lambda x: backend.reshape(x, [-1, 224 * 224 * 3]),
                    name='reshape')(img_input)
  x = layers.Dense(1, name='fc1')(x)
  x = layers.Dense(num_classes, name='fc1000')(x)
  x = layers.Activation('softmax', dtype='float32')(x)

  return models.Model(img_input, x, name='trivial')