dnn.py 文件源码

def buildForwardGraph(self, batch_size, discrimivative=False):
        """

        :param batch_size: Minibatch Size. Currently unused. Using None.
        :param discrimivative: True for discriminative pretraining (Creates a graph with zero hidden layers). Default \
        value: False (Creates a graph with specified hidden layers)
        """
        with tf.variable_scope('forward_variables', reuse=False):
            self.input = tf.placeholder(tf.float32, (None, self.input_dim), 'input_nodes')
            self.output = tf.placeholder(tf.float32, (None, self.output_dim), 'output_nodes')
            inpt = self.input;
            if not discrimivative:
                inpt = self.__buildFullGraph__()
                self.layers.append(LinearLayer(self.layer_dims[-2], self.layer_dims[-1], inpt,
                                               str(len(self.layer_dims) - 2) + 'layerNet_output'))
            else:
                self.layers.append(
                    LinearLayer(self.layer_dims[0], self.layer_dims[-1], inpt, '0layerNet_output'))
            self.global_step = tf.Variable(0, name='global_step', trainable=False)
            self.step_incr = tf.assign_add(self.global_step, 1)