[英]Keras model with input multiply dense layer
Trying to create a simply keras model where the output of the model is the input multiplied by a dense layer element-wise.尝试创建一个简单的 keras model,其中 model 的 output 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)
However this gives me the "N.netype object is not subscriptable" error.但是,这给了我“N.netype object 不可订阅”错误。 I'm assuming this is because the input shape for the Dot layer is facing issues?我假设这是因为点层的输入形状面临问题? How do I solve this?我该如何解决这个问题?
The Dense
layer has to receive some kind of input: 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)
Otherwise just define a weight matrix and multiply it with your input element-wise.否则只需定义一个权重矩阵并将其与您的输入元素相乘。 For example, by using a custom layer:例如,通过使用自定义图层:
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)
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