matchnn.py 文件源码

def call(self,inputs):
        """
        inputs in as array which contains the support set the embeddings, 
        the target embedding as the second last value in the array, and true class of target embedding as the last value in the array
        """ 
        similarities = []

        targetembedding = inputs[-2] # embedding of the query image
        numsupportset = len(inputs)-2
        for ii in range(numsupportset):
            supportembedding = inputs[ii] # embedding for i^{th} member in the support set

            sum_support = tf.reduce_sum(tf.square(supportembedding), 1, keep_dims=True)
            supportmagnitude = tf.rsqrt(tf.clip_by_value(sum_support, self.eps, float("inf"))) #reciprocal of the magnitude of the member of the support 

            sum_query = tf.reduce_sum(tf.square(targetembedding), 1, keep_dims=True)
            querymagnitude = tf.rsqrt(tf.clip_by_value(sum_query, self.eps, float("inf"))) #reciprocal of the magnitude of the query image

            dot_product = tf.matmul(tf.expand_dims(targetembedding,1),tf.expand_dims(supportembedding,2))
            dot_product = tf.squeeze(dot_product,[1])

            cosine_similarity = dot_product*supportmagnitude*querymagnitude
            similarities.append(cosine_similarity)

        similarities = tf.concat(axis=1,values=similarities)
        softmax_similarities = tf.nn.softmax(similarities)
        preds = tf.squeeze(tf.matmul(tf.expand_dims(softmax_similarities,1),inputs[-1]))

        preds.set_shape((inputs[0].shape[0],self.nway))

        return preds