问如何使用tf.browser.fromPixels(图像)对TensorFlow.js训练的基本示例进行真/假输出？

EN

let data = {
    t: [canvas1, canvas2, canvas3, ... canvas3000 ....],
    f: [canvas1, canvas2, ... and so on ...]
}

data.t.forEach(canvas => {
    const xs = tf.browser.fromPixels(canvas);
    const ys = tf.tensor([1]); // output 1, since this canvas is from the `t` (true) dataset
    model.fit(xs, ys, {
      batchSize: 1,
      epochs: 1000
    });
});

data.f.forEach(canvas => {
    const xs = tf.browser.fromPixels(canvas);
    const ys = tf.tensor([0]); // output 0, since this canvas is from the `f` (false) dataset
    model.fit(xs, ys, {
      batchSize: 1,
      epochs: 1000
    });
});

model.predict(tf.browser.fromPixels(data.t[0])).print(); // -> [1]
model.predict(tf.browser.fromPixels(data.t[1])).print(); // -> [1]
model.predict(tf.browser.fromPixels(data.t[2])).print(); // -> [1]

model.predict(tf.browser.fromPixels(data.f[0])).print(); // -> [0]
model.predict(tf.browser.fromPixels(data.f[1])).print(); // -> [0]
model.predict(tf.browser.fromPixels(data.f[2])).print(); // -> [0]

// Just imagine DataSet builds a large data set like described in my 
// question and calls a callpack function with the data variable as 
// its only argument, full of pre-categorized images. Since my database 
// of images is locally stored, I cant really produce an example here 
// that works fully, but this gets the idea across at least.
new DataSet(
  data => {
    
    const model = tf.sequential();
    
    model.add(
    
      // And yes, I realize I would want a convolutional layer, 
      // some max pooling, filtering, etc, but I'm trying to start simple
      
      tf.layers.dense({
        units: [1],
        inputShape: [25, 25, 3],
        dataFormat: "channelsLast",
        activation: "tanh"
      })
    );
    
    model.compile({optimizer: "sgd", loss: "binaryCrossentropy", lr: 0.1});
    
    data.t.forEach(canvas => {
        const xs = tf.browser.fromPixels(canvas);
        const ys = tf.tensor([1]); // output 1, since this canvas is 
        // from the `t` (true) dataset
        model.fit(xs, ys, {
          batchSize: 1,
          epochs: 1000
        });
    });
    
    data.f.forEach(canvas => {
        const xs = tf.browser.fromPixels(canvas);
        const ys = tf.tensor([0]); // output 0, since this canvas is 
        // from the `f` (false) dataset
        model.fit(xs, ys, {
          batchSize: 1,
          epochs: 1000
        });
    });
    
    model.predict(tf.browser.fromPixels(data.t[0])).print(); // -> [1]
    model.predict(tf.browser.fromPixels(data.t[1])).print(); // -> [1]
    model.predict(tf.browser.fromPixels(data.t[2])).print(); // -> [1]
    
    model.predict(tf.browser.fromPixels(data.f[0])).print(); // -> [0]
    model.predict(tf.browser.fromPixels(data.f[1])).print(); // -> [0]
    model.predict(tf.browser.fromPixels(data.f[2])).print(); // -> [0]
    
  },
  {canvas: true}
);

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