In this guide you have familiarized yourself with the different ways to create a model using the Layers and the Core API. You can create a Variable using tf.variable() and passing in an existing Tensor. Every weight is backed by a Variablewhich signals to TensorFlow.js that these tensors are learnable. Note that in the Core API we are responsible for creating and initializing the weights of the model. The same model as above written using the Core API looks like this: // The weights and biases for the two dense layers.Ĭonst w1 = tf.variable(tf.randomNormal()) Ĭonst b1 = tf.variable(tf.randomNormal()) Ĭonst w2 = tf.variable(tf.randomNormal()) Ĭonst b2 = tf.variable(tf.randomNormal()) Models in the Core API are just functions that take one or more Tensors and return a Tensor. You don't need serialization, or can implement your own serialization logic.You need maximum flexibility or control.You may want to use the Core API whenever: The Layers API also offers various off-the-shelf solutions such as weight initialization, model serialization, monitoring training, portability, and safety checking. The general rule of thumb is to always try to use the Layers API first, since it is modeled after the well-adopted Keras API which follows best practices and reduces cognitive load. In the beginning of this guide, we mentioned that there are two ways to create a machine learning model in TensorFlow.js. IMPORTANT: If you add a custom layer, you lose the ability to serialize a model. To test it, we can call the apply() method with a concrete tensor: const t = tf.tensor() Ĭonst o = new SquaredSumLayer().apply(t) You can create a Sequential model by passing a list of layers to the sequential() function: const model = tf.sequential( The most common type of model is the Sequential model, which is a linear stack of layers. The next two sections look at each type more closely. There are two ways to create a model using the Layers API: A sequential model, and a functional model. Then, we will show how to build the same model using the Core API. using the Core API with lower-level ops such as tf.matMul(), tf.add(), etc.įirst, we will look at the Layers API, which is a higher-level API for building models.using the Layers API where you build a model using layers. In TensorFlow.js there are two ways to create a machine learning model: A well-trained model will provide an accurate mapping from the input to the desired output. The optimal parameters are obtained by training the model on data. In machine learning, a model is a function with learnable parameters that maps an input to an output.
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