自定义图层多输入问题(未捕获的类型错误：无法读取未定义的属性'dtype')

Question

我正在尝试编写一个自定义层(lambda层替换(，在没有模型的情况下推断的层做得很好，将模型运行到一定程度，而不是崩溃。在崩溃之前，在层本身中打印接收到的输入是有效的。github的问题是问题

<!-- Load TensorFlow.js -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
<script>
/******************************************************************************
* tensorflow.js lambda layer
* written by twitter.com/benjaminwegener
* license: MIT
*/
class lambdaLayer extends tf.layers.Layer {
constructor(config) {
super(config);
this.name = config.name;
this.lambdaFunction = config.lambdaFunction;
}
call(input) {
return tf.tidy(() => {
return eval(this.lambdaFunction);
});
}
getConfig() {
const config = super.getConfig();
Object.assign(config, {lambdaFunction: this.lambdaFunction});
return config;
}

static get className() {
return 'lambdaLayer';
}
}
tf.serialization.registerClass(lambdaLayer);
console.log('------------------- first inference');
const a = tf.tensor([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2], 'float32');
const b = tf.tensor([8, 7, 6, 5, 4, 3, 2, 1], [1, 2, 2, 2], 'float32');
const output = new lambdaLayer({lambdaFunction: 'tf.matMul(input[0], input[1], false, true);'}).apply([a, b]).print(true);
console.log('------------------- second inference');
const input1 = tf.input({shape: [null, null, null]});
const input2 = tf.input({shape: [null, null, null]});
const customLayer = new lambdaLayer({lambdaFunction: 'input[0].print(true); input[1].print(true); tf.matMul(input[0], input[1], false, true);'});
const model = tf.model({inputs: [input1, input2], outputs: customLayer.apply([input1, input2])});
console.log(model.predict([a, b]));
</script>

导致以下控制台.log：

debug2.html:35 ------------------- first inference
array_ops.ts:1180 Tensor
dtype: float32
rank: 4
shape: [1,2,2,2]
values:
[[[[22, 16],
[52, 38]],
[[38, 16],
[52, 22]]]]
debug2.html:42 ------------------- second inference
array_ops.ts:1180 Tensor
dtype: float32
rank: 4
shape: [1,2,2,2]
values:
[[[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]]]
array_ops.ts:1180 Tensor
dtype: float32
rank: 4
shape: [1,2,2,2]
values:
[[[[8, 7],
[6, 5]],
[[4, 3],
[2, 1]]]]
executor.ts:29 Uncaught TypeError: Cannot read property 'dtype' of undefined
at executor.ts:29
at t.add (executor.ts:96)
at Od (executor.ts:341)
at training.ts:1063
at engine.ts:424
at t.scopedRun (engine.ts:435)
at t.tidy (engine.ts:422)
at We (globals.ts:182)
at s (training.ts:1045)
at training.ts:1045
(anonymous) @ executor.ts:29
t.add @ executor.ts:96
Od @ executor.ts:341
(anonymous) @ training.ts:1063
(anonymous) @ engine.ts:424
t.scopedRun @ engine.ts:435
t.tidy @ engine.ts:422
We @ globals.ts:182
s @ training.ts:1045
(anonymous) @ training.ts:1045
(anonymous) @ engine.ts:424
t.scopedRun @ engine.ts:435
t.tidy @ engine.ts:422
We @ globals.ts:182
e.predictLoop @ training.ts:1029
e.predict @ training.ts:1111
(anonymous) @ debug2.html:50

Answer 1

自定义层在与模型一起使用时出现错误，因为您需要提供输出形状。

当inputShape与输出形状不同时，需要它。

<!-- Load TensorFlow.js -->
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs/dist/tf.min.js"> </script>
<script>
/******************************************************************************
* tensorflow.js lambda layer
* written by twitter.com/benjaminwegener
* license: MIT
*/
class lambdaLayer extends tf.layers.Layer {
constructor(config) {
super(config);
this.name = config.name;
this.lambdaFunction = config.lambdaFunction;
}
call(input) {
return tf.tidy(() => {
return eval(this.lambdaFunction);
});
}
getConfig() {
const config = super.getConfig();
Object.assign(config, {lambdaFunction: this.lambdaFunction});
return config;
}

static get className() {
return 'lambdaLayer';
}

computeOutputShape(inputShape) {
return [1,2,2,2] // expected shape
}
}
tf.serialization.registerClass(lambdaLayer);
console.log('------------------- first inference');
const a = tf.tensor([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2], 'float32');
const b = tf.tensor([8, 7, 6, 5, 4, 3, 2, 1], [1, 2, 2, 2], 'float32');
const output = new lambdaLayer({lambdaFunction: 'tf.matMul(input[0], input[1], false, true);'}).apply([a, b]).print(true);
console.log('------------------- second inference');
const input1 = tf.input({shape: [null, null, null]});
const input2 = tf.input({shape: [null, null, null]});
const customLayer = new lambdaLayer({lambdaFunction: 'input[0].print(true); input[1].print(true); tf.matMul(input[0], input[1], false, true);'});
const model = tf.model({inputs: [input1, input2], outputs: customLayer.apply([input1, input2])});
model.predict([a, b]).print();
</script>