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Tensorflow layers.fully_connected 参数（自用）

原创淇迹 最后发布于2018-07-06 15:32:18 阅读数 7094 收藏 展开 def fully_connected(inputs, num_outputs, activation_fn=nn.relu, normalizer_fn=None, normalizer_params=None, weights_initializer=initializers.xavier_initializer(), weights_regularizer=None, biases_initializer=init_ops.zeros_initializer(), biases_regularizer=None, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, scope=None):

inputs: A tensor of at least rank 2 and static value for the last dimension; i.e. [batch_size, depth], [None, None, None, channels].

至少有两层张量且最后一维是静态值。例如[batch_size,depth]，[None, None, None, channels]. num_outputs: Integer or long, the number of output units in the layer. int型整数或者是long型，是层中输出单元的个数 activation_fn: Activation function. The default value is a ReLU function.Explicitly set it to None to skip it and maintain a linear activation.

激活函数，默认值是ReLU函数，如果将它设为None则会跳过且保持线性激活 normalizer_fn: Normalization function to use instead of biases. If normalizer_fn is provided then biases_initializer and biases_regularizer are ignored and biases are not created nor added.default set to None for no normalizer function 正则化函数用来代替偏置，如果设置了正则化函数，则biases_initializer和biases_regularizer将被忽略且biases不会被创建。 默认设置None，不设置正则化函数 normalizer_params: Normalization function parameters. 正则化函数参数 weights_initializer: An initializer for the weights. ———————————————— 版权声明：本文为CSDN博主「淇迹」的原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接及本声明。 原文链接：https://blog.csdn.net/qq_36235192/article/details/80940357