简单粗暴的tensorflow自定义层、损失函数、评估指标

# 自定义层  y_pred=w*x+b
class LinearLayer(tf.keras.layers.Layer):
    def __init__(self, units):
        super().__init__()
        self.units = units

    def build(self, input_shape):     # 这里 input_shape 是第一次运行call()时参数inputs的形状
        self.w = self.add_weight(name='w',
            shape=[input_shape[-1], self.units], initializer=tf.zeros_initializer())
        self.b = self.add_weight(name='b',
            shape=[self.units], initializer=tf.zeros_initializer())

    def call(self, inputs):
        y_pred = tf.matmul(inputs, self.w) + self.b
        return y_pred
# 模型定义
class LinearModel(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.layer = LinearLayer(units=1)

    def call(self, inputs):
        output = self.layer(inputs)
        return output
# 自定义损失函数
class MeanSquaredError(tf.keras.losses.Loss):
    def call(self, y_true, y_pred):
        return tf.reduce_mean(tf.square(y_pred - y_true))
# 自定义评估指标
class SparseCategoricalAccuracy(tf.keras.metrics.Metric):
    def __init__(self):
        super().__init__()
        self.total = self.add_weight(name='total', dtype=tf.int32, initializer=tf.zeros_initializer())
        self.count = self.add_weight(name='count', dtype=tf.int32, initializer=tf.zeros_initializer())

    def update_state(self, y_true, y_pred, sample_weight=None):
        values = tf.cast(tf.equal(y_true, tf.argmax(y_pred, axis=-1, output_type=tf.int32)), tf.int32)
        self.total.assign_add(tf.shape(y_true)[0])
        self.count.assign_add(tf.reduce_sum(values))

    def result(self):
        return self.count / self.total