Function inputLayer

An input layer is an entry point into a tf.LayersModel.

InputLayer is generated automatically for tf.Sequential models by specifying the inputshape or batchInputShape for the first layer. It should not be specified explicitly. However, it can be useful sometimes, e.g., when constructing a sequential model from a subset of another sequential model's layers. Like the code snippet below shows.

// Define a model which simply adds two inputs.
const model1 = tf.sequential();
model1.add(tf.layers.dense({inputShape: [4], units: 3, activation: 'relu'}));
model1.add(tf.layers.dense({units: 1, activation: 'sigmoid'}));
model1.summary();
model1.predict(tf.zeros([1, 4])).print();

// Construct another model, reusing the second layer of `model1` while
// not using the first layer of `model1`. Note that you cannot add the second
// layer of `model` directly as the first layer of the new sequential model,
// because doing so will lead to an error related to the fact that the layer
// is not an input layer. Instead, you need to create an `inputLayer` and add
// it to the new sequential model before adding the reused layer.
const model2 = tf.sequential();
// Use an inputShape that matches the input shape of `model1`'s second
// layer.
model2.add(tf.layers.inputLayer({inputShape: [3]}));
model2.add(model1.layers[1]);
model2.summary();
model2.predict(tf.zeros([1, 3])).print();
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