You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: We trained both plain and highway networks of varying varying depths on the MNIST digit classification dataset.All networks are thin: each layer has 50 blocks for highway networks and 71 units for plain networks, yielding roughly identical numbers of parameters (\approx5000) per layer.In all networks, the first layer is a fully connected plain layer followed by 9, 19, 49, or 99 fully connected plain or highway layers. Finally, the network output is produced by a softmax layer.We performed a random search of 100 runs for both plain and highway networks to find good settings for the following hyperparameters: initial learning rate, momentum, learning rate exponential decay factor & activation function (either rectified linear or tanh). For highway networks, an additional hyperparameter was the initial value for the transform gate bias (between -1 and -10). Other weights were initialized using the same normalized initialization as plain networks.

Question: What are the reasons behind the guidelines of choosing values of {-1,-2 and -3} for the initial bias for convolutional highway network of depth {10, 20 and 30} ?
Although not explicitly stated, given that the authors selected the best hyperparameters out of 100 experiments, it is highly likely that the initial bias was also selected by these experiments.