具有输入的Keras模型乘以稠密层

Question

尝试创建一个简单的keras模型，其中模型的输出是输入乘以密集层元素。

inputs = tf.keras.Input(shape=256)
weightLayer = tf.keras.layers.Dense(256)
multipled = tf.keras.layers.Dot(axes=1)([inputs,weightLayer])
model = tf.keras.Model(inputs, multipled)

然而，这给了我"；非类型对象不可下标"；错误我想这是因为点层的输入形状面临问题吗？我该如何解决此问题？

Answer 1

Dense层必须接收某种输入：

import tensorflow as tf
inputs = tf.keras.layers.Input(shape=256)
weightLayer = tf.keras.layers.Dense(256)
multipled = tf.keras.layers.Dot(axes=1)([inputs, weightLayer(inputs)])
model = tf.keras.Model(inputs, multipled)

否则，只需定义一个权重矩阵，并将其与输入元素相乘即可。例如，通过使用自定义层：

import tensorflow as tf
class WeightedLayer(tf.keras.layers.Layer):
def __init__(self, num_outputs):
super(WeightedLayer, self).__init__()
self.num_outputs = num_outputs
self.dot_layer = tf.keras.layers.Dot(axes=1)
def build(self, input_shape):
self.kernel = self.add_weight("kernel",
shape=[int(input_shape[-1]),
self.num_outputs])
def call(self, inputs):
return self.dot_layer([inputs, self.kernel])

inputs = tf.keras.layers.Input(shape=256)
weighted_layer = WeightedLayer(256)
multipled = weighted_layer(inputs)
model = tf.keras.Model(inputs, multipled)