vae

ML/Pytorch/more_advanced/VAE/model.py

@@ -0,0 +1,45 @@
+import torch
+from torch import nn
+
+
+class VariationalAutoEncoder(nn.Module):
+    def __init__(self, input_dim, h_dim=200, z_dim=20):
+        super().__init__()
+        # encoder
+        self.img_2hid = nn.Linear(input_dim, h_dim)
+        self.hid_2mu = nn.Linear(h_dim, z_dim)
+        self.hid_2sigma = nn.Linear(h_dim, z_dim)
+
+        # decoder
+        self.z_2hid = nn.Linear(z_dim, h_dim)
+        self.hid_2img = nn.Linear(h_dim, input_dim)
+
+        self.relu = nn.ReLU()
+
+    def encode(self, x):
+        h = self.relu(self.img_2hid(x))
+        mu, sigma = self.hid_2mu(h), self.hid_2sigma(h)
+        return mu, sigma
+
+    def decode(self, z):
+        h = self.relu(self.z_2hid(z))
+        return torch.sigmoid(self.hid_2img(h))
+
+    def forward(self, x):
+        mu, sigma = self.encode(x)
+        epsilon = torch.randn_like(sigma)
+        z_new = mu + sigma*epsilon
+        x_reconstructed = self.decode(z_new)
+        return x_reconstructed, mu, sigma
+
+
+if __name__ == "__main__":
+    x = torch.randn(4, 28*28)
+    vae = VariationalAutoEncoder(input_dim=784)
+    x_reconstructed, mu, sigma = vae(x)
+    print(x_reconstructed.shape)
+    print(mu.shape)
+    print(sigma.shape)
+
+
+

ML/Pytorch/more_advanced/VAE/train.py

@@ -0,0 +1,86 @@
+import torch
+import torchvision.datasets as datasets  # Standard datasets
+from tqdm import tqdm
+from torch import nn, optim
+from model import VariationalAutoEncoder
+from torchvision import transforms
+from torchvision.utils import save_image
+from torch.utils.data import DataLoader
+
+# Configuration
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+INPUT_DIM = 784
+H_DIM = 200
+Z_DIM = 20
+NUM_EPOCHS = 10
+BATCH_SIZE = 32
+LR_RATE = 3e-4  # Karpathy constant
+
+# Dataset Loading
+dataset = datasets.MNIST(root="dataset/", train=True, transform=transforms.ToTensor(), download=True)
+train_loader = DataLoader(dataset=dataset, batch_size=BATCH_SIZE, shuffle=True)
+model = VariationalAutoEncoder(INPUT_DIM, H_DIM, Z_DIM).to(DEVICE)
+optimizer = optim.Adam(model.parameters(), lr=LR_RATE)
+loss_fn = nn.BCELoss(reduction="sum")
+
+# Start Training
+for epoch in range(NUM_EPOCHS):
+    loop = tqdm(enumerate(train_loader))
+    for i, (x, _) in loop:
+        # Forward pass
+        x = x.to(DEVICE).view(x.shape[0], INPUT_DIM)
+        x_reconstructed, mu, sigma = model(x)
+
+        # Compute loss
+        reconstruction_loss = loss_fn(x_reconstructed, x)
+        kl_div = -torch.sum(1 + torch.log(sigma.pow(2)) - mu.pow(2) - sigma.pow(2))
+
+        # Backprop
+        loss = reconstruction_loss + kl_div
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        loop.set_postfix(loss=loss.item())
+
+
+model = model.to("cpu")
+def inference(digit, num_examples=1):
+    """
+    Generates (num_examples) of a particular digit.
+    Specifically we extract an example of each digit,
+    then after we have the mu, sigma representation for
+    each digit we can sample from that.
+
+    After we sample we can run the decoder part of the VAE
+    and generate examples.
+    """
+    images = []
+    idx = 0
+    for x, y in dataset:
+        if y == idx:
+            images.append(x)
+            idx += 1
+        if idx == 10:
+            break
+
+    encodings_digit = []
+    for d in range(10):
+        with torch.no_grad():
+            mu, sigma = model.encode(images[d].view(1, 784))
+        encodings_digit.append((mu, sigma))
+
+    mu, sigma = encodings_digit[digit]
+    for example in range(num_examples):
+        epsilon = torch.randn_like(sigma)
+        z = mu + sigma * epsilon
+        out = model.decode(z)
+        out = out.view(-1, 1, 28, 28)
+        save_image(out, f"generated_{digit}_ex{example}.png")
+
+for idx in range(10):
+    inference(idx, num_examples=5)
+
+
+
+
+

ML/Pytorch/more_advanced/transformer_from_scratch/transformer_from_scratch.py

@@ -23,10 +23,10 @@ class SelfAttention(nn.Module):
             self.head_dim * heads == embed_size
         ), "Embedding size needs to be divisible by heads"
 
-        self.values = nn.Linear(self.head_dim, self.head_dim, bias=False)
-        self.keys = nn.Linear(self.head_dim, self.head_dim, bias=False)
-        self.queries = nn.Linear(self.head_dim, self.head_dim, bias=False)
-        self.fc_out = nn.Linear(heads * self.head_dim, embed_size)
+        self.values = nn.Linear(embed_size, embed_size)
+        self.keys = nn.Linear(embed_size, embed_size)
+        self.queries = nn.Linear(embed_size, embed_size)
+        self.fc_out = nn.Linear(embed_size, embed_size)
 
     def forward(self, values, keys, query, mask):
         # Get number of training examples
@@ -34,14 +34,14 @@ class SelfAttention(nn.Module):
 
         value_len, key_len, query_len = values.shape[1], keys.shape[1], query.shape[1]
 
+        values = self.values(values)  # (N, value_len, embed_size)
+        keys = self.keys(keys)  # (N, key_len, embed_size)
+        queries = self.queries(query)  # (N, query_len, embed_size)
+
         # Split the embedding into self.heads different pieces
         values = values.reshape(N, value_len, self.heads, self.head_dim)
         keys = keys.reshape(N, key_len, self.heads, self.head_dim)
-        query = query.reshape(N, query_len, self.heads, self.head_dim)
-
-        values = self.values(values)  # (N, value_len, heads, head_dim)
-        keys = self.keys(keys)  # (N, key_len, heads, head_dim)
-        queries = self.queries(query)  # (N, query_len, heads, heads_dim)
+        queries = queries.reshape(N, query_len, self.heads, self.head_dim)
 
         # Einsum does matrix mult. for query*keys for each training example
         # with every other training example, don't be confused by einsum