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})
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()
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()
}