tf.keras模块中的Model类

class Model

Model groups layers into an object with training and inference features.
tensorflow API

从输入输出建立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)

继承Model类建立Model

1. 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.
2. 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)

  def call(self, inputs):
    x = self.dense1(inputs)
    if training:
      x = self.dropout(x, training=training)
    return self.dense2(x)

model = MyModel()

3. 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().

Model类中的method

1. compile

compile(
    optimizer='rmsprop', loss=None, metrics=None, loss_weights=None,
    sample_weight_mode=None, weighted_metrics=None, **kwargs
)

最后

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