Python layers.Lambda方法代码示例

# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Lambda [as 别名]
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 

# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Lambda [as 别名]
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 

# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Lambda [as 别名]
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} 

# 需要导入模块: from tensorflow.python.keras import layers [as 别名]
# 或者: from tensorflow.python.keras.layers import Lambda [as 别名]
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')