给定使用tf.sequential()
创建的模型,是否可以使用tf.model()
获取图层并使用它们创建另一个模型?
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})
答案 0 :(得分:1)
要获取使用tf.sequential
创建的模型的各层,需要使用模型的属性layers
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>
一个人也可以使用apply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>