# Imitating Human Go Players via Vision Transformer
## 4 Experiments and Results
### Experimental Settings
#### 4.1.2 Training Settings for CNNs  
We also evaluated two additional models to compare the performance of the ViT with that of CNN-based architectures, using identical training and holdout sets. The comparison models included the policy network architecture from AlphaGo (Figure 6) and a 12-layer CNN adapted from [12] (Figure 7).  
The **policy network** begins by padding input features with zeros to form a tensor of size \(\mathbb{R}^{10\times 23\times 23}\) This tensor is processed through a sequence of convolutional layers, each followed by rectified linear unit (ReLU) activations. A final \(1\times 1\) convolutional layer, combined with flattening operations, converts the tensor into a vector of size \(\mathbb{R}^{361}\). A softmax operation then derives the probability distribution of potential moves.  
The **12-layer CNN** processes preprocessed input data structured as a tensor of size \(\mathbb{R}^{10\times 19\times 19}\). A sequence of convolutional layers with ReLU activations is applied, and zero padding ensures that the tensor size remains \(\mathbb{R}^{10\times 19\times 19}\). The final \(1\times 1\) convolutional layer outputs two \(\mathbb{R}^{19\times 19}\) tensors. Each tensor is flattened, and softmax operations are applied to produce the probability distributions for black and white moves, respectively.  
In this study, these models are referred to as the \"policy network\" and the \"12-layer CNN\", and their performance is compared to that of the ViT.  
Figure 6: The policy network’s architecture processes the raw board position through feature extraction, using the features specified in Table 1.  
The two models share a similar CNN backbone structure, but their output layers are tailored to different objectives. The policy network employs a single output block that consolidates all information into a unified prediction. In contrast, the 12-layer CNN utilizes two separate output blocks, enabling it to handle game dynamics uniquely for each player. This distinction is significant, as it allows the 12-layer CNN to make more nuanced predictions by better capturing the complexities of player-specific strategies.  
A significant architectural difference between the models lies in their input layers. The policy network uses the same comprehensive feature set as the ViT model, encom-passing various aspects of the game state, such as the current board configuration, previ-ous moves, and additional relevant features. In contrast, the 12-layer CNN excludes the \"turn\" feature from its input set, as detailed in Table 1 This exclusion required adjustments to the input layer to ensure the model could still effectively process the board state.  
Both CNN-based models underwent the same rigorous training process. They were trained for 80 epochs, providing ample time for the models to learn from the training data. Stochastic Gradient Descent (SGD) was used as the optimization algorithm, with an initial learning rate of 0.003 that decayed by 50% every 16 epochs. Cross-entropy loss was employed as the loss function. Notably, the 12-layer CNN only updated parameters related to its output layer during training.  
Figure 7: The 12-layer CNN for move prediction preprocesses the inputs by extracting the features listed in Table 1.  
By comparing the performance of these two CNN-based models with the ViT approach, we aimed to gain deeper insights into how different deep learning architectures perform in the task of imitating human Go players. This comparative analysis is essential for identifying the model best suited for use as a training or analysis tool for Go players, ultimately contributing to their skill enhancement and understanding of the game.