Dear Altruist,

I am seeking assistance in creating a Transformer code using PyTorch, following the provided instructions:

Input shape: [Batch_size, 10, 512]

Here, ‘10’ represents the video length in frames, and ‘512’ signifies the feature dimension of each frame.

Output shape: [Batch_size, 10, 4]

To provide some context about my challenge, each video comprises 10 frames, with each frame’s features represented as a 512-dimensional vector. This configuration results in a video dimension of [10, 512]. My objective is to employ a Transformer model to process batches of videos, such as [Batch_size, 10, 512], to capture the sequential relationships among the 10 frames. This captured knowledge will then be used to predict a 4-dimensional vector for each frame over the subsequent 10 frames. Consequently, the desired output format is [Batch_size, 10, 4].

I am using the following code, but the test loss is not decreasing:

```
class Transformer(nn.Module):
def __init__(self, input_dim,hidden_dim, output_dim, num_heads, num_layers, seq_len):
super(Transformer, self).__init__()
self.embedding = nn.Linear(input_dim, hidden_dim)
self.positional_encoding = PositionalEncoding(hidden_dim)
self.dim_model = hidden_dim #256
encoder_layer = nn.TransformerEncoderLayer(
d_model=hidden_dim,
nhead=num_heads #8
)
self.encoder = nn.TransformerEncoder(
encoder_layer,
num_layers=num_layers #4
)
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, x):
x = self.embedding(x)
x = self.positional_encoding(x)
x = x.permute(1,0,2)
x = self.encoder(x)
x = x.permute(1,0,2)
x = self.fc(x)
return x
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=0.1)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
```

I would greatly appreciate your guidance and support in implementing this task.

Thank you.