PreMaskMLM(*args, **kwargs) :: Model
Model groups layers into an object with training and inference features.
Arguments:
inputs: The input(s) of the model: a keras.Input object or list of
keras.Input objects.
outputs: The output(s) of the model. See Functional API example below.
name: String, the name of the model.
There are two ways to instantiate a Model:
1 - With the "Functional API", where you start from Input,
you chain layer calls to specify the model's forward pass,
and finally you create your model from inputs and outputs:
import tensorflow as tf
inputs = tf.keras.Input(shape=(3,))
x = tf.keras.layers.Dense(4, activation=tf.nn.relu)(inputs)
outputs = tf.keras.layers.Dense(5, activation=tf.nn.softmax)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
2 - By subclassing the Model class: in that case, you should define your
layers in __init__ and you should implement the model's forward pass
in call.
import tensorflow as tf
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
def call(self, inputs):
x = self.dense1(inputs)
return self.dense2(x)
model = MyModel()
If you subclass Model, you can optionally have
a training argument (boolean) in call, which you can use to specify
a different behavior in training and inference:
import tensorflow as tf
class MyModel(tf.keras.Model):
def __init__(self):
super(MyModel, self).__init__()
self.dense1 = tf.keras.layers.Dense(4, activation=tf.nn.relu)
self.dense2 = tf.keras.layers.Dense(5, activation=tf.nn.softmax)
self.dropout = tf.keras.layers.Dropout(0.5)
def call(self, inputs, training=False):
x = self.dense1(inputs)
if training:
x = self.dropout(x, training=training)
return self.dense2(x)
model = MyModel()
Once the model is created, you can config the model with losses and metrics
with model.compile(), train the model with model.fit(), or use the model
to do prediction with model.predict().