本文介绍了从一个模型获取图层并将其分配给另一个模型的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!
问题描述
给定使用 tf.sequential()
创建的模型,是否可以获取图层并使用它们通过 tf.model()
创建另一个模型?
Given a model created using tf.sequential()
, is it possible to get the layers and to use them to create another model using tf.model()
?
const model = tf.sequential();
model.add(tf.layers.dense({units: 32, inputShape: [50]}));
model.add(tf.layers.dense({units: 4}));
// get the layers
layers
// use the layers to create another model
tf.model({layers})
推荐答案
要获取使用 tf.sequential
创建的模型的图层,需要使用属性 layers
模型的
To get the layers of the model created using tf.sequential
, one needs to use the property layers
of the model
const model = tf.sequential();
// first layer
model.add(tf.layers.dense({units: 32, inputShape: [50]}));
// second layer
model.add(tf.layers.dense({units: 4}));
// get all the layers of the model
const layers = model.layers
// second model
const model2 = tf.model({
inputs: layers[0].input,
outputs: layers[1].output
})
model2.predict(tf.randomNormal([1, 50])).print()
<html>
<head>
<!-- Load TensorFlow.js -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.12.0"> </script>
</head>
<body>
</body>
</html>
也可以使用应用方法
const model = tf.sequential();
// first layer
model.add(tf.layers.dense({units: 32, inputShape: [50]}));
// second layer
model.add(tf.layers.dense({units: 4}));
var input = tf.randomNormal([1, 50])
var layers = model.layers
for (var i=0; i < layers.length; i++){
var layer = layers[i]
var output = layer.apply(input)
input = output
output.print()
}
<html>
<head>
<!-- Load TensorFlow.js -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@0.12.0"> </script>
</head>
<body>
</body>
</html>
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