tfbasemodel.py 文件源码

def nn_layer_(self,input_tensor, input_dim, output_dim, layer_name, act=tf.nn.relu):
        """Reusable code for making a simple neural net layer.
        It does a matrix multiply, bias add, and then uses relu to nonlinearize.
        It also sets up name scoping so that the resultant graph is easy to read,
        and adds a number of summary ops.
        """
        # Adding a name scope ensures logical grouping of the layers in the graph.
        with tf.name_scope(layer_name):
            with tf.name_scope('weights'):
                weights = self.weight_variable([input_dim, output_dim])
                self.variable_summaries(weights, layer_name + '/weights')
            with tf.name_scope('biases'):
                biases = self.bias_variable([output_dim])
                self.variable_summaries(biases, layer_name + '/biases')
            with tf.name_scope('Wx_plus_b'):
                preactivate = tf.matmul(input_tensor, weights) + biases
                tf.histogram_summary(layer_name + '/pre_activations', preactivate)               
            activations = act(preactivate, 'activation')
            tf.histogram_summary(layer_name + '/activations', activations)

        return activations