labels_rbm.py 文件源码

def create_model(self, model_input, vocab_size, l2_penalty=1e-8, **unused_params):
        """Creates a logistic model.

        Args:
          model_input: 'batch' x 'num_features' matrix of input features.
          vocab_size: The number of classes in the dataset.

        Returns:
          A dictionary with a tensor containing the probability predictions of the
          model in the 'predictions' key. The dimensions of the tensor are
          batch_size x num_classes."""

        input_size = vocab_size
        output_size = FLAGS.hidden_size
        with tf.name_scope("rbm"):
            self.weights = tf.Variable(tf.truncated_normal([input_size, output_size],
                                    stddev=1.0 / math.sqrt(float(input_size))), name="weights")
            self.v_bias = tf.Variable(tf.zeros([input_size]), name="v_bias")
            self.h_bias = tf.Variable(tf.zeros([output_size]), name="h_bias")
            tf.add_to_collection(name=tf.GraphKeys.REGULARIZATION_LOSSES, value=l2_penalty*tf.nn.l2_loss(self.weights))
            tf.add_to_collection(name=tf.GraphKeys.REGULARIZATION_LOSSES, value=l2_penalty*tf.nn.l2_loss(self.v_bias))
            tf.add_to_collection(name=tf.GraphKeys.REGULARIZATION_LOSSES, value=l2_penalty*tf.nn.l2_loss(self.h_bias))