From 7fd1a6046ca96d8f52bb427c4c554649f2e6a45c Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Mon, 18 Nov 2024 17:18:10 +0100 Subject: [PATCH 01/18] refactor: Update DeepDreamer class initialization and image preprocessing This commit refactors the DeepDreamer class in deepdream.py to improve the model initialization and image preprocessing steps. The model initialization now uses the weights parameter, and the image preprocessing includes resizing while maintaining the aspect ratio and converting the image to a tensor and normalizing it. --- images/cat.jpg | Bin 0 -> 755993 bytes images/dreamed_cat.jpg | Bin 0 -> 109100 bytes src/poc/deepdream.py | 157 +++++++++++++++++++++++++++++++++++++++++ 3 files changed, 157 insertions(+) create mode 100644 images/cat.jpg create mode 100644 images/dreamed_cat.jpg create mode 100644 src/poc/deepdream.py diff --git a/images/cat.jpg b/images/cat.jpg new file mode 100644 index 0000000000000000000000000000000000000000..6557c35747ae45466e77326ae0f66b5b8eae9deb GIT binary patch literal 755993 zcmd431wd6x*DyRM;Lu1M1f)AeIz-~oEgh0lQqqk`iK0hB6i`8Fqy$t_B&EaxNd=Ma zMi3!neD;*gHbS}6yS6Y04*)R0|3AS zxG-{nfFKz355Q;v&NmnUBiOM&U`rU+k1}uo;1s{gM>zrHkMa=BZ`X|<_uq3oC__OQ zq=1l+U_9(Q41*)U%`4wva_HIX^q*w}1q9Mzzo@6fe^SSnPe=TOQRdmWKgvKaA6|0$ z3TkRlu5asQZ|~;q=CCPeq=| z?Z)a`3`CQ!v9}EWOnx6wootY 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as plt + + +class DeepDreamer: + def __init__(self, model_name="inception_v3", layer_name="Mixed_5b"): + # Update model initialization to use weights parameter + weights = models.Inception_V3_Weights.IMAGENET1K_V1 + self.model = models.inception_v3(weights=weights) + self.model.eval() + + # Dictionary to store activations + self.activations = {} + self.layer_name = layer_name + + # Register forward hook + for name, layer in self.model.named_modules(): + if name == layer_name: + layer.register_forward_hook(self._get_activation(name)) + + def _get_activation(self, name): + def hook(model, input, output): + self.activations[name] = output + + return hook + + def preprocess_image(self, image_path, size=512): + image = Image.open(image_path) + # Resize while maintaining aspect ratio + ratio = size / min(image.size) + new_size = tuple(int(x * ratio) for x in image.size) + image = image.resize(new_size, Image.LANCZOS) + + # Convert to tensor and normalize + loader = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize( + mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225] + ), + ] + ) + image = loader(image).unsqueeze(0) + return image + + def deprocess_image(self, tensor): + # Convert back to image + tensor = tensor.squeeze(0) + # Denormalize + tensor = tensor * torch.tensor([0.229, 0.224, 0.225]).view( + 3, 1, 1 + ) + torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1) + tensor = torch.clamp(tensor, 0, 1) + + # Convert to PIL image + transform = transforms.ToPILImage() + return transform(tensor) + + def dream( + self, image_path, num_iterations=20, lr=0.01, octave_scale=1.4, num_octaves=4 + ): + # Load base image + base_img = self.preprocess_image(image_path) + + # Create octaves pyramid + octaves = [] + for i in range(num_octaves - 1): + octaves.append(base_img) + base_img = torch.nn.functional.interpolate( + base_img, + scale_factor=1 / octave_scale, + mode="bicubic", + align_corners=False, + ) + + detail = None + for octave_idx, octave_base in enumerate(reversed(octaves)): + if detail is not None: + detail = torch.nn.functional.interpolate( + detail, + size=octave_base.shape[2:], + mode="bilinear", + align_corners=False, + ) + + # Add detail from previous octave to current + input_img = octave_base + detail if detail is not None else octave_base + input_img = input_img.detach() # Detach from previous graph + input_img.requires_grad_(True) # Enable gradients for new iteration + + for i in range(num_iterations): + # Clear gradients at start of iteration + if input_img.grad is not None: + input_img.grad.zero_() + + # Forward pass + out = self.model(input_img) + activation = self.activations[self.layer_name] + + # Calculate loss + loss = activation.norm() # Remove negative sign for maximization + + # Backward pass + loss.backward() + + # Ensure we have gradients + if input_img.grad is not None: + # Gradient normalization and update + grad = input_img.grad.data + grad_mean = grad.abs().mean() + grad_norm = grad / (grad_mean + 1e-8) + input_img.data += lr * grad_norm + + # Apply image regularization + input_img.data = torch.clamp(input_img.data, -1, 1) + + if (i + 1) % 5 == 0: + print( + f"Octave {octave_idx+1}/{num_octaves}, " + f"Iteration {i+1}/{num_iterations}, " + f"Loss: {loss.item():.3f}" + ) + + # Extract detail produced in this octave + detail = input_img.data - octave_base + + return self.deprocess_image(input_img.detach()) + + +# Example usage +def generate_dream(image_path, output_path, iterations=7, lr=0.09): + dreamer = DeepDreamer() + dreamed_image = dreamer.dream( + image_path, num_iterations=iterations, lr=lr, octave_scale=1.9, num_octaves=4 + ) + dreamed_image.save(output_path) + + # Display original and dreamed images + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 7)) + ax1.imshow(Image.open(image_path)) + ax1.set_title("Original Image") + ax1.axis("off") + + ax2.imshow(dreamed_image) + ax2.set_title("DeepDream Image") + ax2.axis("off") + + plt.show() + + +if __name__ == "__main__": + generate_dream("images/cat.jpg", "images/dreamed_cat.jpg") From e856ebfafa6a2451e05794216b8b71939f9f6cfe Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 08:04:20 +0100 Subject: [PATCH 02/18] feat(visualization): add XOR problem demonstration Add visual demonstration of the XOR problem showing: - Why XOR is not linearly separable - Neural network's learned decision boundaries - Interactive visualization of training process This builds on 01_xor_network.py to provide visual insights into: - Data point distribution - Failed linear separation attempts - Complex decision boundaries learned by the network Technical additions: - Contour plot of network decisions - Grid-based boundary visualization - Improved training monitoring --- src/journey_to_transformer/01_xor_network.py | 170 +++++++++++++++++++ 1 file changed, 170 insertions(+) create mode 100644 src/journey_to_transformer/01_xor_network.py diff --git a/src/journey_to_transformer/01_xor_network.py b/src/journey_to_transformer/01_xor_network.py new file mode 100644 index 0000000..9397b1c --- /dev/null +++ b/src/journey_to_transformer/01_xor_network.py @@ -0,0 +1,170 @@ +""" +The XOR Problem and Its Historical Significance + +The XOR (exclusive OR) problem was a pivotal challenge in AI history that helped lead to +the first AI winter in the 1970s. The controversy began when Marvin Minsky and Seymour +Papert published their 1969 book "Perceptrons", which demonstrated that single-layer +perceptrons could not solve the XOR problem. + +The XOR function returns: +- 1 when inputs are different (1,0) or (0,1) +- 0 when inputs are same (0,0) or (1,1) + +This created a crisis because: +1. XOR is a simple logical operation that humans can easily understand +2. Single-layer perceptrons could not learn this pattern +3. It wasn't clear if adding layers would help or if they could be trained effectively + +The solution emerged in the 1980s with: +1. Multi-layer networks (adding hidden layers) +2. Backpropagation algorithm for training +3. Non-linear activation functions + +This combination allowed neural networks to learn the complex decision boundaries needed +for XOR, helping to end the first AI winter. The XOR problem demonstrates that: +- Sometimes simple-looking problems require complex solutions +- The limitations of one approach can drive innovation in new directions +- Understanding failure cases is crucial for advancing the field + +The network below uses: +- 2 input neurons (for the two binary inputs) +- 4 hidden neurons (to create complex decision boundaries) +- 1 output neuron (for the binary output) +- ReLU activation (to introduce non-linearity) + + +It doesn't always learn correctly. This is a classic case of the network getting stuck in a +local minimum - in this case, it's actually stuck at its initial state where it's just +predicting 0.5 for everything. The constant loss of 0.6931 (which is approximately -ln(0.5)) +is a telltale sign that the network isn't learning at all. + +This happens because: +- Neural networks are initialized with random weights +- Sometimes these initial weights lead to a configuration where the gradients aren't + strong enough to push the network out of this "lazy" state +- The network finds it's "comfortable" just predicting 0.5 for everything, as this + minimizes its maximum error for any input + +Solutions typically include: +- Just restart training with new random weights (reinitialize the model) +- Try different learning rates +- Use different weight initialization strategies +- Add momentum to the optimizer + +This is actually a great learning example because it shows how neural networks can sometimes +get stuck, just like humans can get stuck in suboptimal thinking patterns! The good news is +that if you just run the code again, the new random initialization will likely give you +better results. +""" + +import torch +import torch.nn as nn +import matplotlib.pyplot as plt + +# Configure matplotlib to work in VS Code +plt.switch_backend('TkAgg') + +class XORNetwork(nn.Module): + """ + A simple neural network for solving the XOR problem. + """ + def __init__(self): + super().__init__() + self.layers = nn.Sequential( + # First layer: 2 inputs -> 4 neurons + # We need 4 neurons because XOR is a complex pattern: + # - 2 neurons aren't enough to separate the data properly + # - 4 neurons give us more "decision boundaries" to work with + nn.Linear(2, 4), + + # ReLU activation function + # - Converts negative numbers to 0 + # - Keeps positive numbers as they are + # - Helps network learn non-linear patterns + nn.ReLU(), + + # Output layer: 4 neurons -> 1 output + # - Takes the 4 intermediate values + # - Combines them into final yes/no decision + nn.Linear(4, 1), + + # Sigmoid squishes output between 0 and 1 + # - Perfect for yes/no decisions + # - 0 = false, 1 = true + nn.Sigmoid(), + ) + + def forward(self, x): + return self.layers(x) + +# Create training data +# XOR truth table: output is 1 if inputs are different, 0 if same +X = torch.tensor([[0.0, 0.0], # Input: (0,0) -> Output should be 0 + [0.0, 1.0], # Input: (0,1) -> Output should be 1 + [1.0, 0.0], # Input: (1,0) -> Output should be 1 + [1.0, 1.0]]) # Input: (1,1) -> Output should be 0 + +y = torch.tensor([[0.0], # Expected output for (0,0) + [1.0], # Expected output for (0,1) + [1.0], # Expected output for (1,0) + [0.0]]) # Expected output for (1,1) + +# Create network and training tools +model = XORNetwork() +# Binary Cross Entropy Loss: good for yes/no problems +criterion = nn.BCELoss() +# Adam optimizer: automatically adjusts learning speed +# lr=0.05 means "take bigger steps" when learning +optimizer = torch.optim.Adam(model.parameters(), lr=0.05) + +# Keep track of how well we're learning +losses = [] + +print("Training the network to solve XOR...") +print("Epoch Loss") +print("-" * 20) + +# Train for 1000 rounds +for epoch in range(1000): + # 1. Make a prediction with current network + output = model(X) + # 2. Calculate how wrong we were + loss = criterion(output, y) + # 3. Reset gradients from last time + optimizer.zero_grad() + # 4. Calculate how to adjust the network + loss.backward() + # 5. Update the network + optimizer.step() + + # Store loss for plotting + losses.append(loss.item()) + + # Show progress every 100 epochs + if (epoch + 1) % 100 == 0: + print(f"{epoch+1:5d} {loss.item():.4f}") + +# Test how well we learned +print("\nTesting the network:") +print("Input Target Prediction Result") +print("-" * 40) +with torch.no_grad(): # Don't need gradients for testing + predictions = model(X) + for i in range(len(X)): + prediction = predictions[i].item() + target = y[i].item() + # Consider prediction wrong if it's more than 0.2 away from target + is_correct = abs(prediction - target) < 0.2 + result = "āœ…" if is_correct else "šŸ’„" + print(f"{X[i].numpy()} {target:.0f} {prediction:.3f} {result}") + +# Plot how the learning progressed +plt.figure(figsize=(10, 5)) +plt.plot(losses) +plt.title("Training Loss Over Time") +plt.xlabel("Epoch") +plt.ylabel("Loss") +plt.grid(True) +plt.show() + +print("\nLook how quickly it learns! Much faster than waiting 17 years... šŸ˜‰") From 26df2e5a4c1226dc236c05c17381ba0b73b665fe Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 08:47:04 +0100 Subject: [PATCH 03/18] refactor: Update XORNetwork class and add ModernXORNetwork class - Refactor the XORNetwork class to improve code readability and maintainability. - Update the class initialization and activation functions for better learning. - Add a new ModernXORNetwork class with batch normalization and leaky ReLU activation. - Ensure the output is between 0 and 1 using sigmoid activation. --- src/journey_to_transformer/01_xor_network.py | 61 +++++++++++++++----- 1 file changed, 46 insertions(+), 15 deletions(-) diff --git a/src/journey_to_transformer/01_xor_network.py b/src/journey_to_transformer/01_xor_network.py index 9397b1c..00d9c67 100644 --- a/src/journey_to_transformer/01_xor_network.py +++ b/src/journey_to_transformer/01_xor_network.py @@ -55,6 +55,25 @@ get stuck, just like humans can get stuck in suboptimal thinking patterns! The good news is that if you just run the code again, the new random initialization will likely give you better results. + +Modern XOR networks use: +- Batch normalization to stabilize values +- Leaky ReLU activation to improve learning +- Sigmoid at the end to ensure output is between 0 and 1 + +class ModernXORNetwork(nn.Module): + def __init__(self): + super().__init__() + self.network = nn.Sequential( + nn.Linear(2, 4), + nn.BatchNorm1d(4), # Stabilize values + nn.LeakyReLU(), # Better activation + nn.Linear(4, 1), + nn.Sigmoid(), # Only at end for 0-1 output + ) + + def forward(self, x): + return self.network(x) """ import torch @@ -62,12 +81,14 @@ import matplotlib.pyplot as plt # Configure matplotlib to work in VS Code -plt.switch_backend('TkAgg') +plt.switch_backend("TkAgg") + class XORNetwork(nn.Module): """ A simple neural network for solving the XOR problem. """ + def __init__(self): super().__init__() self.layers = nn.Sequential( @@ -76,18 +97,15 @@ def __init__(self): # - 2 neurons aren't enough to separate the data properly # - 4 neurons give us more "decision boundaries" to work with nn.Linear(2, 4), - # ReLU activation function # - Converts negative numbers to 0 # - Keeps positive numbers as they are # - Helps network learn non-linear patterns nn.ReLU(), - # Output layer: 4 neurons -> 1 output # - Takes the 4 intermediate values # - Combines them into final yes/no decision nn.Linear(4, 1), - # Sigmoid squishes output between 0 and 1 # - Perfect for yes/no decisions # - 0 = false, 1 = true @@ -97,17 +115,26 @@ def __init__(self): def forward(self, x): return self.layers(x) + # Create training data # XOR truth table: output is 1 if inputs are different, 0 if same -X = torch.tensor([[0.0, 0.0], # Input: (0,0) -> Output should be 0 - [0.0, 1.0], # Input: (0,1) -> Output should be 1 - [1.0, 0.0], # Input: (1,0) -> Output should be 1 - [1.0, 1.0]]) # Input: (1,1) -> Output should be 0 - -y = torch.tensor([[0.0], # Expected output for (0,0) - [1.0], # Expected output for (0,1) - [1.0], # Expected output for (1,0) - [0.0]]) # Expected output for (1,1) +X = torch.tensor( + [ + [0.0, 0.0], # Input: (0,0) -> Output should be 0 + [0.0, 1.0], # Input: (0,1) -> Output should be 1 + [1.0, 0.0], # Input: (1,0) -> Output should be 1 + [1.0, 1.0], + ] +) # Input: (1,1) -> Output should be 0 + +y = torch.tensor( + [ + [0.0], # Expected output for (0,0) + [1.0], # Expected output for (0,1) + [1.0], # Expected output for (1,0) + [0.0], + ] +) # Expected output for (1,1) # Create network and training tools model = XORNetwork() @@ -136,10 +163,10 @@ def forward(self, x): loss.backward() # 5. Update the network optimizer.step() - + # Store loss for plotting losses.append(loss.item()) - + # Show progress every 100 epochs if (epoch + 1) % 100 == 0: print(f"{epoch+1:5d} {loss.item():.4f}") @@ -158,6 +185,10 @@ def forward(self, x): result = "āœ…" if is_correct else "šŸ’„" print(f"{X[i].numpy()} {target:.0f} {prediction:.3f} {result}") +print("\nNetwork parameters (weights and biases):") +for name, param in model.named_parameters(): + print(f"{name}: {param.data}") +print() # Plot how the learning progressed plt.figure(figsize=(10, 5)) plt.plot(losses) From 08a516a85acc7a7e25dc63558529116789f9ed77 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 08:51:12 +0100 Subject: [PATCH 04/18] refactor: Update DeepDreamer class initialization and image preprocessing --- src/poc/deepdream.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/poc/deepdream.py b/src/poc/deepdream.py index 00f91dc..2ba77db 100644 --- a/src/poc/deepdream.py +++ b/src/poc/deepdream.py @@ -69,7 +69,7 @@ def dream( # Create octaves pyramid octaves = [] - for i in range(num_octaves - 1): + for _ in range(num_octaves - 1): octaves.append(base_img) base_img = torch.nn.functional.interpolate( base_img, From dd019a5b675196dfdd7776efbbbbeb2ad504e8e9 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 09:04:03 +0100 Subject: [PATCH 05/18] docs(neural-net): add comprehensive documentation for neural network evolution This commit enhances the documentation of our neural network evolution demo by: - Adding detailed historical context for each architecture (1980s-2010s) - Explaining key innovations and their significance: * Sigmoid to Tanh activation transition * Introduction of ReLU and Batch Normalization * Early memory mechanisms leading to LSTM - Documenting architectural decisions and their rationale - Clarifying the progression of neural network development The comments help developers understand: - Why each architecture was significant - What problems each innovation solved - How different components work together - The historical context of deep learning evolution --- .../02_neural_net_evolution.py | 216 ++++++++++++++++++ 1 file changed, 216 insertions(+) create mode 100644 src/journey_to_transformer/02_neural_net_evolution.py diff --git a/src/journey_to_transformer/02_neural_net_evolution.py b/src/journey_to_transformer/02_neural_net_evolution.py new file mode 100644 index 0000000..6642cfe --- /dev/null +++ b/src/journey_to_transformer/02_neural_net_evolution.py @@ -0,0 +1,216 @@ +""" +Neural Network Evolution: From Basic to Modern Architectures + +This file demonstrates the historical evolution of neural network architectures, +showing how various improvements helped solve fundamental problems: + +1. BasicNetwork: Uses sigmoid activation (historical approach from 1980s) +2. ImprovedNetwork: Uses tanh activation (1990s improvement) +3. ModernNetwork: Implements batch normalization and ReLU (2010s best practices) +4. SimpleMemoryNetwork: Demonstrates early memory concepts (precursor to LSTM) + +Each network shows key innovations that helped advance deep learning. +""" + +import torch +import torch.nn as nn +import torch.optim as optim +import matplotlib.pyplot as plt + + +# 1. Basic Network with Sigmoid (prone to vanishing gradients) +class BasicNetwork(nn.Module): + """ + Represents the earliest practical neural networks (1980s-style). + + Problems with this architecture: + - Sigmoid activation suffers from vanishing gradients + - Gradients become very small for extreme values + - Network learns very slowly in deep layers + """ + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.layer1 = nn.Linear(input_size, hidden_size) + self.sigmoid = nn.Sigmoid() # Historical activation function + self.layer2 = nn.Linear(hidden_size, output_size) + + def forward(self, x): + x = self.sigmoid(self.layer1(x)) # Sigmoid squashes values to (0,1) + x = self.layer2(x) + return x + + +# 2. Improved Network with Better Activation +class ImprovedNetwork(nn.Module): + """ + Represents 1990s improvements with tanh activation. + + Advantages over sigmoid: + - Outputs centered around 0 (-1 to 1 range) + - Stronger gradients + - Generally faster convergence + """ + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.layer1 = nn.Linear(input_size, hidden_size) + self.tanh = nn.Tanh() # Centered activation function + self.layer2 = nn.Linear(hidden_size, output_size) + + def forward(self, x): + x = self.tanh(self.layer1(x)) + x = self.layer2(x) + return x + + +# 3. Deep Network with Modern Solutions +class ModernNetwork(nn.Module): + """ + Represents current best practices (2010s onwards). + + Key modern features: + - ReLU activation (solves vanishing gradient) + - Batch Normalization (stabilizes training) + - Deeper architecture (more layers) + - Xavier/Glorot initialization (built into PyTorch) + """ + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + # Modern architecture with multiple improvements + self.layer1 = nn.Linear(input_size, hidden_size) + self.bn1 = nn.BatchNorm1d(hidden_size) # Normalizes layer outputs + self.relu = nn.ReLU() # Modern activation function + self.layer2 = nn.Linear(hidden_size, hidden_size) + self.bn2 = nn.BatchNorm1d(hidden_size) + self.layer3 = nn.Linear(hidden_size, output_size) + + def forward(self, x): + # Each layer follows the pattern: Linear -> BatchNorm -> ReLU + x = self.relu(self.bn1(self.layer1(x))) + x = self.relu(self.bn2(self.layer2(x))) + x = self.layer3(x) # No activation on final layer + return x + + +# 4. Early LSTM-like Memory (simplified for demonstration) +class SimpleMemoryNetwork(nn.Module): + """ + Demonstrates early attempts at networks with memory (pre-LSTM). + + Key concepts: + - Input gate: Controls what information to store + - Memory cell: Maintains state over time + - Output gate: Controls what information to output + + This is a simplified version showing the concept that led to LSTM/GRU. + """ + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.hidden_size = hidden_size + + # Gates control information flow + self.input_gate = nn.Linear(input_size + hidden_size, hidden_size) + self.memory_transform = nn.Linear(input_size + hidden_size, hidden_size) + self.output_gate = nn.Linear(input_size + hidden_size, hidden_size) + + self.output = nn.Linear(hidden_size, output_size) + + # Activation functions for different purposes + self.tanh = nn.Tanh() # For memory content + self.sigmoid = nn.Sigmoid() # For gates + + def forward(self, x, hidden_state=None): + batch_size = x.size(0) + + # Initialize hidden state if none provided + if hidden_state is None: + hidden_state = torch.zeros(batch_size, self.hidden_size).to(x.device) + + # Combine current input with previous state + combined = torch.cat((x, hidden_state), dim=1) + + # Gate mechanisms + input_gate = self.sigmoid(self.input_gate(combined)) + memory_write = self.tanh(self.memory_transform(combined)) + output_gate = self.sigmoid(self.output_gate(combined)) + + # Update memory state + memory_cell = input_gate * memory_write + + # Generate output using gated memory + hidden_state = output_gate * self.tanh(memory_cell) + output = self.output(hidden_state) + + return output, hidden_state + + +# Demonstration +def train_and_compare(): + """ + Trains all network variants on a simple task and compares their performance. + + The task is to sum input features - chosen because: + - It's simple enough to learn quickly + - Complex enough to show differences between architectures + - Easy to verify results + """ + # Generate some sample data + X = torch.randn(100, 10) # 100 samples, 10 features + y = torch.sum(X, dim=1).unsqueeze(1) # Simple sum task + + # Create networks + networks = { + "Basic (Sigmoid)": BasicNetwork(10, 20, 1), + "Improved (Tanh)": ImprovedNetwork(10, 20, 1), + "Modern (ReLU+BN)": ModernNetwork(10, 20, 1), + "Memory Net": SimpleMemoryNetwork(10, 20, 1), + } + + # Training settings + epochs = 500 + losses = {name: [] for name in networks} + + for name, net in networks.items(): + print(f"\nTraining {name}...") + optimizer = optim.Adam(net.parameters(), lr=0.01) + criterion = nn.MSELoss() + + for epoch in range(epochs): + optimizer.zero_grad() + + # Forward pass (handle memory network separately) + if isinstance(net, SimpleMemoryNetwork): + output, _ = net(X) + else: + output = net(X) + + # Compute loss + loss = criterion(output, y) + losses[name].append(loss.item()) + + # Backward pass + loss.backward() + + # Gradient clipping for stability + torch.nn.utils.clip_grad_norm_(net.parameters(), max_norm=1.0) + + optimizer.step() + + if (epoch + 1) % 20 == 0: + print(f"Epoch {epoch+1}/{epochs}, Loss: {loss.item():.4f}") + + return losses + + +# Run training and plot results +losses = train_and_compare() + +plt.figure(figsize=(10, 6)) +for name, loss_values in losses.items(): + plt.plot(loss_values, label=name) +plt.xlabel("Epoch") +plt.ylabel("Loss") +plt.title("Training Loss Comparison") +plt.legend() +plt.yscale("log") # Better visualization of loss differences +plt.grid(True) +plt.show() From 2daa29158fe3332845edfc20f68cc2afb4af55a3 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 09:15:05 +0100 Subject: [PATCH 06/18] feat(lstm): add model evaluation and testing functionality - Add test_lstm_model function for model evaluation - Calculate key metrics (MAE, MSE, RMSE) on test data - Update demo to include testing phase - Add proper model evaluation mode handling This enables better assessment of model performance and generalization capabilities through standardized metrics. --- src/journey_to_transformer/03_lstm.py | 236 ++++++++++++++++++++++++++ 1 file changed, 236 insertions(+) create mode 100644 src/journey_to_transformer/03_lstm.py diff --git a/src/journey_to_transformer/03_lstm.py b/src/journey_to_transformer/03_lstm.py new file mode 100644 index 0000000..b9823b2 --- /dev/null +++ b/src/journey_to_transformer/03_lstm.py @@ -0,0 +1,236 @@ +""" +Long Short-Term Memory (LSTM) Networks and Their Significance + +LSTMs were introduced in 1997 by Hochreiter & Schmidhuber to solve the vanishing gradient +problem in traditional RNNs. They're particularly good at learning long-term dependencies +in sequential data. + +Key Components of an LSTM: +1. Forget Gate: Decides what information to throw away from the cell state +2. Input Gate: Decides which new information to store in the cell state +3. Candidate Memory: Creates new candidate values that could be added to the state +4. Output Gate: Decides what parts of the cell state to output + +The LSTM's power comes from its cell state (C_t), which acts like a conveyor belt. +Information can flow along it unchanged, and the network can learn to add or remove +information from the cell state, regulated by the gates. + +The gates are the key innovation: +- They use sigmoid functions that output numbers between 0 and 1 +- These numbers are used as filters (0 = "let nothing through", 1 = "let everything through") +- The network learns what information is important to keep or throw away + +Mathematical Formulation: +f_t = Ļƒ(W_f Ā· [h_{t-1}, x_t] + b_f) # Forget gate +i_t = Ļƒ(W_i Ā· [h_{t-1}, x_t] + b_i) # Input gate +CĢƒ_t = tanh(W_c Ā· [h_{t-1}, x_t] + b_c) # Candidate memory +o_t = Ļƒ(W_o Ā· [h_{t-1}, x_t] + b_o) # Output gate +C_t = f_t * C_{t-1} + i_t * CĢƒ_t # Cell state update +h_t = o_t * tanh(C_t) # Hidden state update + +Where: +- Ļƒ is the sigmoid function +- * is element-wise multiplication +- [h_{t-1}, x_t] is concatenation of previous hidden state and current input +""" + +import torch +import torch.nn as nn + + +class LSTM(nn.Module): + """LSTM implementation.""" + + def __init__(self, input_size, hidden_size): + """ + Initialize LSTM components. + + Args: + input_size: Dimension of input features + hidden_size: Dimension of hidden state and cell state + """ + super().__init__() + self.hidden_size = hidden_size + + # Each gate is a linear transformation followed by a sigmoid + # They take concatenated (previous_hidden, current_input) as input + self.forget_gate = nn.Linear(input_size + hidden_size, hidden_size) + self.input_gate = nn.Linear(input_size + hidden_size, hidden_size) + self.candidate_memory = nn.Linear(input_size + hidden_size, hidden_size) + self.output_gate = nn.Linear(input_size + hidden_size, hidden_size) + + # Activation functions + self.sigmoid = nn.Sigmoid() # For gates (0-1 range) + self.tanh = nn.Tanh() # For state updates (-1 to 1 range) + + def forward(self, x, state=None): + """ + Process one timestep through the LSTM. + + Args: + x: Input tensor of shape (batch_size, input_size) + state: Tuple of (hidden_state, cell_state) or None for initial step + + Returns: + tuple: (new_hidden_state, (new_hidden_state, new_cell_state)) + """ + batch_size = x.size(0) + + # Initialize states to zeros if not provided + if state is None: + h_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) + c_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) + else: + h_prev, c_prev = state + + # Combine input and previous hidden state for gate calculations + combined = torch.cat((x, h_prev), dim=1) + + # Gate calculations + forget = self.sigmoid(self.forget_gate(combined)) + input = self.sigmoid(self.input_gate(combined)) + candidate = self.tanh(self.candidate_memory(combined)) + output = self.sigmoid(self.output_gate(combined)) + + # State updates + c_next = forget * c_prev + input * candidate # Update cell state + h_next = output * self.tanh(c_next) # Create hidden state + + return h_next, (h_next, c_next) + + def get_gate_states(self, x, state=None): + """Returns internal gate states for visualization.""" + batch_size = x.size(0) + + if state is None: + h_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) + c_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) + else: + h_prev, c_prev = state + + combined = torch.cat((x, h_prev), dim=1) + + # Get all gate states + forget = self.sigmoid(self.forget_gate(combined)) + input = self.sigmoid(self.input_gate(combined)) + candidate = self.tanh(self.candidate_memory(combined)) + output = self.sigmoid(self.output_gate(combined)) + + # Update states + c_next = forget * c_prev + input * candidate + h_next = output * self.tanh(c_next) + + return { + 'forget_gate': forget, + 'input_gate': input, + 'candidate_memory': candidate, + 'output_gate': output, + 'cell_state': c_next, + 'hidden_state': h_next + } + + +# Example usage with a simple sequence task +class LSTMPredictor(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.lstm = LSTM(input_size, hidden_size) + self.output_layer = nn.Linear(hidden_size, output_size) + + def forward(self, x_sequence): + outputs = [] + h = None + + # Process each timestep + for t in range(x_sequence.size(1)): + x_t = x_sequence[:, t, :] + h_t, h = self.lstm(x_t, h) + out_t = self.output_layer(h_t) + outputs.append(out_t) + + return torch.stack(outputs, dim=1) + + +def test_lstm_model(model, test_sequences, test_targets): + """ + Test the LSTM model and visualize its predictions. + + Args: + model: Trained LSTMPredictor model + test_sequences: Input sequences of shape (batch_size, seq_length, input_size) + test_targets: Target values of shape (batch_size, seq_length, 1) + + Returns: + dict: Dictionary containing test metrics + """ + model.eval() # Set to evaluation mode + with torch.no_grad(): + # Get predictions + predictions = model(test_sequences) + + # Calculate test loss + test_loss = nn.MSELoss()(predictions, test_targets) + + # Calculate metrics + mae = torch.mean(torch.abs(predictions - test_targets)) + mse = torch.mean((predictions - test_targets) ** 2) + rmse = torch.sqrt(mse) + + return { + 'test_loss': test_loss.item(), + 'mae': mae.item(), + 'mse': mse.item(), + 'rmse': rmse.item(), + } + +def demo_lstm(): + # Create synthetic sequence data + seq_length = 10 + batch_size = 32 + input_size = 5 + hidden_size = 10 + output_size = 1 + + # Create model + model = LSTMPredictor(input_size, hidden_size, output_size) + + # Generate random sequences + x = torch.randn(batch_size, seq_length, input_size) + # Target: sum of input features at each timestep + y = torch.sum(x, dim=2, keepdim=True) + + # Training setup + criterion = nn.MSELoss() + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + print("Training LSTM...") + for epoch in range(1000): + optimizer.zero_grad() + + # Forward pass + y_pred = model(x) + loss = criterion(y_pred, y) + + # Backward pass + loss.backward() + optimizer.step() + + if (epoch + 1) % 10 == 0: + print(f"Epoch {epoch+1}/100, Loss: {loss.item():.4f}") + + # Generate test data + test_x = torch.randn(batch_size, seq_length, input_size) + test_y = torch.sum(test_x, dim=2, keepdim=True) + + # Test the model + print("\nTesting LSTM...") + metrics = test_lstm_model(model, test_x, test_y) + + # Print metrics + print("\nTest Results:") + for metric_name, value in metrics.items(): + print(f"{metric_name}: {value:.4f}") + + +if __name__ == "__main__": + demo_lstm() From 12391c1c81089cbceb8b3ab3713ad2551945ce6e Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 09:32:29 +0100 Subject: [PATCH 07/18] docs: add comprehensive comments to LSTM character predictor Adds detailed explanations and technical documentation to the LSTM-based text generation model, including: - Architecture overview and component relationships - Detailed explanations of LSTM memory and sequence processing - Character embedding and vocabulary handling - Temperature-controlled text generation sampling - Tensor shape transformations and data flow The comments follow the project's documentation standards and mirror the detailed explanations in the XOR network implementation, making the code more accessible for learning purposes. Technical notes: - Follows Google-style docstrings - Includes implementation details and design decisions - Explains the role of each hyperparameter - Documents tensor shapes and transformations --- .../04_lstm_next_char_pred.py | 187 ++++++++++++++++++ 1 file changed, 187 insertions(+) create mode 100644 src/journey_to_transformer/04_lstm_next_char_pred.py diff --git a/src/journey_to_transformer/04_lstm_next_char_pred.py b/src/journey_to_transformer/04_lstm_next_char_pred.py new file mode 100644 index 0000000..2401d17 --- /dev/null +++ b/src/journey_to_transformer/04_lstm_next_char_pred.py @@ -0,0 +1,187 @@ +import torch +import torch.nn as nn +import string + + +class TextPredictor(nn.Module): + """ + Neural network for predicting the next character in a sequence. + Uses LSTM (Long Short-Term Memory) architecture for understanding patterns in text. + """ + def __init__(self, vocab_size, embedding_dim=32, hidden_size=128): + super().__init__() + self.hidden_size = hidden_size + + # Embedding layer: converts character indices to dense vectors + # - Each character gets a learned vector representation + # - Similar to word embeddings but for individual characters + # - embedding_dim controls how detailed these representations are + self.embedding = nn.Embedding(vocab_size, embedding_dim) + + # LSTM layer: processes sequences and maintains memory + # - input_size: size of embedded character vectors + # - hidden_size: how much information to remember + # - num_layers=2: stacked LSTMs for more complex patterns + # - batch_first=True: expect data in (batch, sequence, features) format + # - dropout=0.2: randomly drop 20% of connections for regularization + self.lstm = nn.LSTM( + input_size=embedding_dim, + hidden_size=hidden_size, + num_layers=2, + batch_first=True, + dropout=0.2, + ) + + # Final layer: convert LSTM output to character probabilities + # - Takes LSTM's hidden state + # - Outputs scores for each possible character + self.fc = nn.Linear(hidden_size, vocab_size) + + def forward(self, x, hidden=None): + # 1. Convert character indices to embeddings + embeds = self.embedding(x) + + # 2. Process sequence through LSTM + # - Returns processed sequence and updated hidden state + # - hidden state carries memory between batches + lstm_out, hidden = self.lstm(embeds, hidden) + + # 3. Convert LSTM output to character predictions + output = self.fc(lstm_out) + return output, hidden + + +# Text processing utilities +class TextProcessor: + """ + Handles conversion between text and the numerical format needed by the network. + Think of it as a translator between human-readable text and network-readable numbers. + """ + def __init__(self): + # Create character mappings using all printable ASCII characters + # - Includes letters, numbers, punctuation, and whitespace + # - char_to_idx: converts characters to unique numbers + # - idx_to_char: converts numbers back to characters + self.chars = string.printable + self.char_to_idx = {ch: i for i, ch in enumerate(self.chars)} + self.idx_to_char = dict(enumerate(self.chars)) + self.vocab_size = len(self.chars) + + def encode(self, text): + """Convert text string to tensor of indices.""" + return torch.tensor([self.char_to_idx[ch] for ch in text]) + + def decode(self, indices): + """Convert tensor of indices back to text string.""" + return "".join([self.idx_to_char[idx.item()] for idx in indices]) + + +def generate_text(model, processor, start_text="Hello", length=100, temperature=0.8): + """ + Generate new text by sampling from the model's predictions. + + Parameters: + - start_text: initial text to seed the generation + - length: how many characters to generate + - temperature: controls randomness of sampling + - Lower (e.g., 0.5): more conservative, predictable text + - Higher (e.g., 1.2): more creative, potentially chaotic text + """ + model.eval() # Switch to evaluation mode + current_text = start_text + hidden = None # LSTM's memory state + + with torch.no_grad(): # Don't track gradients during generation + for _ in range(length): + # 1. Prepare input sequence + x = processor.encode(current_text) + x = x.unsqueeze(0) # Add batch dimension + + # 2. Get model's predictions + output, hidden = model(x, hidden) + + # 3. Apply temperature to adjust prediction randomness + # - Higher temperature = more uniform probabilities + # - Lower temperature = more peaked probabilities + probs = torch.softmax(output[0, -1] / temperature, dim=0) + + # 4. Sample next character from probability distribution + next_char_idx = torch.multinomial(probs, 1) + next_char = processor.decode([next_char_idx]) + + # 5. Add to generated text + current_text += next_char + + return current_text + + +def train_model(): + # Sample training text (you can replace this with your own text) + text = """The quick brown fox jumps over the lazy dog. + Smalltalk is a fantastic programming language. + LSTMs are great for processing sequential data. + Neural networks learn from examples.""" + + # Setup + processor = TextProcessor() + model = TextPredictor(processor.vocab_size) + criterion = nn.CrossEntropyLoss() + optimizer = torch.optim.Adam(model.parameters(), lr=0.001) + + # Prepare data + sequence_length = 20 + sequences = [] + targets = [] + + # Create training sequences + for i in range(len(text) - sequence_length): + sequences.append(text[i : i + sequence_length]) + targets.append(text[i + sequence_length]) + + print("Training the model...") + for epoch in range(100): + model.train() + total_loss = 0 + + for seq, target in zip(sequences, targets): + # Prepare data + x = processor.encode(seq).unsqueeze(0) # Shape: [1, seq_len] + y = processor.encode(target) # Shape: [1] + + # Forward pass + output, _ = model(x) # output shape: [1, seq_len, vocab_size] + + # Get only the last prediction and reshape + last_output = output[:, -1, :] # Shape: [1, vocab_size] + + # Loss calculation + loss = criterion(last_output, y) + + # Backward pass + optimizer.zero_grad() + loss.backward() + optimizer.step() + + total_loss += loss.item() + + if (epoch + 1) % 10 == 0: + print(f"Epoch {epoch+1}/100, Loss: {total_loss/len(sequences):.4f}") + # Generate sample text + sample = generate_text(model, processor, "The quick brown ", length=50) + print(f"Sample text: {sample}\n") + + return model, processor + + +if __name__ == "__main__": + # Train the model + model, processor = train_model() + + # Generate some text + print("\nGenerating text with different temperatures:") + for temp in [0.5, 0.8, 1.2]: + print(f"\nTemperature: {temp}") + generated = generate_text( + model, processor, "The quick brown ", length=100, temperature=temp + ) + print(generated) From aef28ac65ab2f1ac7bc314f50b79c619211a86e7 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 10:47:06 +0100 Subject: [PATCH 08/18] docs(rnn): add comprehensive educational comments to mood predictor - Add detailed docstrings explaining RNN concepts and historical significance - Include architectural explanations for network components - Document relationship between mathematical operations and intuitive concepts - Add inline comments explaining the purpose of each layer and transformation - Follow educational style similar to XOR network example Part of the "Journey to Transformer" tutorial series. --- src/journey_to_transformer/03_lstm.py | 236 ----------------- .../03_rnn_mood_pred.py | 239 ++++++++++++++++++ .../04_lstm_next_char_pred.py | 40 ++- 3 files changed, 278 insertions(+), 237 deletions(-) delete mode 100644 src/journey_to_transformer/03_lstm.py create mode 100644 src/journey_to_transformer/03_rnn_mood_pred.py diff --git a/src/journey_to_transformer/03_lstm.py b/src/journey_to_transformer/03_lstm.py deleted file mode 100644 index b9823b2..0000000 --- a/src/journey_to_transformer/03_lstm.py +++ /dev/null @@ -1,236 +0,0 @@ -""" -Long Short-Term Memory (LSTM) Networks and Their Significance - -LSTMs were introduced in 1997 by Hochreiter & Schmidhuber to solve the vanishing gradient -problem in traditional RNNs. They're particularly good at learning long-term dependencies -in sequential data. - -Key Components of an LSTM: -1. Forget Gate: Decides what information to throw away from the cell state -2. Input Gate: Decides which new information to store in the cell state -3. Candidate Memory: Creates new candidate values that could be added to the state -4. Output Gate: Decides what parts of the cell state to output - -The LSTM's power comes from its cell state (C_t), which acts like a conveyor belt. -Information can flow along it unchanged, and the network can learn to add or remove -information from the cell state, regulated by the gates. - -The gates are the key innovation: -- They use sigmoid functions that output numbers between 0 and 1 -- These numbers are used as filters (0 = "let nothing through", 1 = "let everything through") -- The network learns what information is important to keep or throw away - -Mathematical Formulation: -f_t = Ļƒ(W_f Ā· [h_{t-1}, x_t] + b_f) # Forget gate -i_t = Ļƒ(W_i Ā· [h_{t-1}, x_t] + b_i) # Input gate -CĢƒ_t = tanh(W_c Ā· [h_{t-1}, x_t] + b_c) # Candidate memory -o_t = Ļƒ(W_o Ā· [h_{t-1}, x_t] + b_o) # Output gate -C_t = f_t * C_{t-1} + i_t * CĢƒ_t # Cell state update -h_t = o_t * tanh(C_t) # Hidden state update - -Where: -- Ļƒ is the sigmoid function -- * is element-wise multiplication -- [h_{t-1}, x_t] is concatenation of previous hidden state and current input -""" - -import torch -import torch.nn as nn - - -class LSTM(nn.Module): - """LSTM implementation.""" - - def __init__(self, input_size, hidden_size): - """ - Initialize LSTM components. - - Args: - input_size: Dimension of input features - hidden_size: Dimension of hidden state and cell state - """ - super().__init__() - self.hidden_size = hidden_size - - # Each gate is a linear transformation followed by a sigmoid - # They take concatenated (previous_hidden, current_input) as input - self.forget_gate = nn.Linear(input_size + hidden_size, hidden_size) - self.input_gate = nn.Linear(input_size + hidden_size, hidden_size) - self.candidate_memory = nn.Linear(input_size + hidden_size, hidden_size) - self.output_gate = nn.Linear(input_size + hidden_size, hidden_size) - - # Activation functions - self.sigmoid = nn.Sigmoid() # For gates (0-1 range) - self.tanh = nn.Tanh() # For state updates (-1 to 1 range) - - def forward(self, x, state=None): - """ - Process one timestep through the LSTM. - - Args: - x: Input tensor of shape (batch_size, input_size) - state: Tuple of (hidden_state, cell_state) or None for initial step - - Returns: - tuple: (new_hidden_state, (new_hidden_state, new_cell_state)) - """ - batch_size = x.size(0) - - # Initialize states to zeros if not provided - if state is None: - h_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) - c_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) - else: - h_prev, c_prev = state - - # Combine input and previous hidden state for gate calculations - combined = torch.cat((x, h_prev), dim=1) - - # Gate calculations - forget = self.sigmoid(self.forget_gate(combined)) - input = self.sigmoid(self.input_gate(combined)) - candidate = self.tanh(self.candidate_memory(combined)) - output = self.sigmoid(self.output_gate(combined)) - - # State updates - c_next = forget * c_prev + input * candidate # Update cell state - h_next = output * self.tanh(c_next) # Create hidden state - - return h_next, (h_next, c_next) - - def get_gate_states(self, x, state=None): - """Returns internal gate states for visualization.""" - batch_size = x.size(0) - - if state is None: - h_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) - c_prev = torch.zeros(batch_size, self.hidden_size).to(x.device) - else: - h_prev, c_prev = state - - combined = torch.cat((x, h_prev), dim=1) - - # Get all gate states - forget = self.sigmoid(self.forget_gate(combined)) - input = self.sigmoid(self.input_gate(combined)) - candidate = self.tanh(self.candidate_memory(combined)) - output = self.sigmoid(self.output_gate(combined)) - - # Update states - c_next = forget * c_prev + input * candidate - h_next = output * self.tanh(c_next) - - return { - 'forget_gate': forget, - 'input_gate': input, - 'candidate_memory': candidate, - 'output_gate': output, - 'cell_state': c_next, - 'hidden_state': h_next - } - - -# Example usage with a simple sequence task -class LSTMPredictor(nn.Module): - def __init__(self, input_size, hidden_size, output_size): - super().__init__() - self.lstm = LSTM(input_size, hidden_size) - self.output_layer = nn.Linear(hidden_size, output_size) - - def forward(self, x_sequence): - outputs = [] - h = None - - # Process each timestep - for t in range(x_sequence.size(1)): - x_t = x_sequence[:, t, :] - h_t, h = self.lstm(x_t, h) - out_t = self.output_layer(h_t) - outputs.append(out_t) - - return torch.stack(outputs, dim=1) - - -def test_lstm_model(model, test_sequences, test_targets): - """ - Test the LSTM model and visualize its predictions. - - Args: - model: Trained LSTMPredictor model - test_sequences: Input sequences of shape (batch_size, seq_length, input_size) - test_targets: Target values of shape (batch_size, seq_length, 1) - - Returns: - dict: Dictionary containing test metrics - """ - model.eval() # Set to evaluation mode - with torch.no_grad(): - # Get predictions - predictions = model(test_sequences) - - # Calculate test loss - test_loss = nn.MSELoss()(predictions, test_targets) - - # Calculate metrics - mae = torch.mean(torch.abs(predictions - test_targets)) - mse = torch.mean((predictions - test_targets) ** 2) - rmse = torch.sqrt(mse) - - return { - 'test_loss': test_loss.item(), - 'mae': mae.item(), - 'mse': mse.item(), - 'rmse': rmse.item(), - } - -def demo_lstm(): - # Create synthetic sequence data - seq_length = 10 - batch_size = 32 - input_size = 5 - hidden_size = 10 - output_size = 1 - - # Create model - model = LSTMPredictor(input_size, hidden_size, output_size) - - # Generate random sequences - x = torch.randn(batch_size, seq_length, input_size) - # Target: sum of input features at each timestep - y = torch.sum(x, dim=2, keepdim=True) - - # Training setup - criterion = nn.MSELoss() - optimizer = torch.optim.Adam(model.parameters(), lr=0.01) - - print("Training LSTM...") - for epoch in range(1000): - optimizer.zero_grad() - - # Forward pass - y_pred = model(x) - loss = criterion(y_pred, y) - - # Backward pass - loss.backward() - optimizer.step() - - if (epoch + 1) % 10 == 0: - print(f"Epoch {epoch+1}/100, Loss: {loss.item():.4f}") - - # Generate test data - test_x = torch.randn(batch_size, seq_length, input_size) - test_y = torch.sum(test_x, dim=2, keepdim=True) - - # Test the model - print("\nTesting LSTM...") - metrics = test_lstm_model(model, test_x, test_y) - - # Print metrics - print("\nTest Results:") - for metric_name, value in metrics.items(): - print(f"{metric_name}: {value:.4f}") - - -if __name__ == "__main__": - demo_lstm() diff --git a/src/journey_to_transformer/03_rnn_mood_pred.py b/src/journey_to_transformer/03_rnn_mood_pred.py new file mode 100644 index 0000000..2335d1a --- /dev/null +++ b/src/journey_to_transformer/03_rnn_mood_pred.py @@ -0,0 +1,239 @@ +import torch +import torch.nn as nn +import matplotlib.pyplot as plt + + +""" +RNN Mood Predictor: Understanding Sequential Data and Memory + +This example demonstrates how Recurrent Neural Networks (RNNs) can process sequences +of events and maintain a "memory" of past events to make predictions. It's like how +your mood throughout the day is influenced by the sequence of events you experience. + +Key Concepts: +1. Sequential Processing - RNNs handle data that comes in sequences (like events in a day) +2. Hidden State - The network maintains a "memory" of previous events +3. Time Steps - Each event is processed one at a time, updating the memory +4. Non-linear Transformations - Using activation functions to model complex patterns + +Historical Significance: +- RNNs were a breakthrough in handling sequential data +- They enabled applications like: + * Natural language processing + * Time series prediction + * Music generation + * Speech recognition + +The network uses: +- Input layer: Transforms each event into a hidden representation +- RNN cell: Updates the memory based on current event and previous state +- Output layer: Makes predictions based on current memory state +""" + +# Our Simple RNN - like a friend who remembers your day's events! +class MoodPredictor(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.hidden_size = hidden_size # size of the memory + + # Transform input events + self.input_layer = nn.Linear( + input_size, hidden_size + ) # used to transform the input events + + # The "memory" layer - remembers previous events + self.rnn_cell = nn.RNNCell( + hidden_size, hidden_size + ) # used to update the memory + + # Final prediction layer + self.output_layer = nn.Linear( + hidden_size, output_size + ) # used to make a prediction + + # Activation functions + self.tanh = nn.Tanh() # used to squash the values between -1 and 1 + self.sigmoid = nn.Sigmoid() # used to squash the values between 0 and 1 + + def forward(self, x, hidden=None): + # For first event of day, start with neutral state + if hidden is None: + hidden = torch.zeros(x.size(0), self.hidden_size).to(x.device) + + # Lists to store predictions at each time step + outputs = [] + + # Process each event in the sequence + for t in range(x.size(1)): + # Get current event + current_input = x[:, t, :] + + # Transform input + transformed = self.tanh(self.input_layer(current_input)) + + # Update memory with new event + hidden = self.rnn_cell(transformed, hidden) + + # Make prediction + output = self.sigmoid(self.output_layer(hidden)) + outputs.append(output) + + # Stack all predictions together + outputs = torch.stack(outputs, dim=1) + return outputs, hidden + + +# Let's create some example data! +def generate_day_sequences(num_sequences=100): + """ + Generate synthetic day sequences to train our model. + + Each day is represented as a sequence of 5 events: + - Events are one-hot encoded: [good, neutral, bad] + - Final mood is calculated based on the balance of good vs bad events + - Some randomness is added to make it more realistic + + This is like how your actual day might have a mix of events that + collectively influence your final mood. + """ + sequences = [] + labels = [] + + for _ in range(num_sequences): + # Generate random day sequence + day = torch.zeros(5, 3) + for t in range(5): + # Random event type (one-hot encoded) + event_type = torch.randint(0, 3, (1,)) + day[t, event_type] = 1 + + # Calculate mood based on events (with some randomness) + good_events = day[:, 0].sum() + bad_events = day[:, 2].sum() + mood = torch.sigmoid(torch.tensor([(good_events - bad_events) / 2])) + + sequences.append(day) + labels.append(mood) + + return torch.stack(sequences), torch.stack(labels) + + +# Training time! +def train_and_test(): + """ + Train the mood predictor and evaluate its performance. + + The training process: + 1. Split data into training and test sets + 2. Train model for 100 epochs + 3. Use Binary Cross Entropy loss (good for 0-1 predictions) + 4. Use Adam optimizer (adaptive learning rates) + 5. Evaluate on test set + 6. Visualize training progress + + This mimics how we might train a real mood prediction system, + though real-world data would be much more complex! + """ + # Generate data + X, y = generate_day_sequences() + + # Split into train and test + train_size = int(0.8 * len(X)) + X_train, X_test = X[:train_size], X[train_size:] + y_train, y_test = y[:train_size], y[train_size:] + + # Create model + model = MoodPredictor(input_size=3, hidden_size=12, output_size=1) + + # Training setup + criterion = nn.BCELoss() + optimizer = torch.optim.Adam(model.parameters(), lr=0.01) + + # Training loop + print("Training the mood predictor...") + losses = [] + + for epoch in range(100): + optimizer.zero_grad() + + # Forward pass + outputs, _ = model(X_train) + loss = criterion(outputs[:, -1], y_train) # Only care about final prediction + + # Backward pass + loss.backward() + optimizer.step() + + losses.append(loss.item()) + + if (epoch + 1) % 10 == 0: + print(f"Epoch {epoch+1}/100, Loss: {loss.item():.4f}") + + # Test the model + model.eval() + with torch.no_grad(): + _calculate_test_loss_and_accuracy(model, X_test, criterion, y_test) + # Plot training progress + plt.figure(figsize=(10, 5)) + plt.plot(losses) + plt.title("Training Progress") + plt.xlabel("Epoch") + plt.ylabel("Loss") + plt.grid(True) + plt.show() + + return model + + +def _calculate_test_loss_and_accuracy(model, X_test, criterion, y_test): + test_outputs, _ = model(X_test) + test_predictions = test_outputs[:, -1] + test_loss = criterion(test_predictions, y_test) + + # Convert predictions to binary decisions with a threshold of 0.5 + binary_preds = (test_predictions >= 0.5).float() + binary_targets = (y_test >= 0.5).float() + accuracy = (binary_preds == binary_targets).float().mean() + + print(f"\nTest Loss: {test_loss:.4f}") + print(f"Accuracy: {accuracy:.2%}") + + +# Let's run it! +if __name__ == "__main__": + model = train_and_test() + + # Try a specific day sequence + good_day = torch.tensor( + [ + [1, 0, 0], # Good morning + [1, 0, 0], # Nice lunch + [0, 1, 0], # Normal afternoon + [0, 0, 1], # Minor setback + [1, 0, 0], # Great evening + ], + dtype=torch.float32, # Specify float32 data type + ).unsqueeze(0) + + bad_day = torch.tensor( + [ + [0, 0, 1], # Bad morning + [0, 1, 0], # Meh lunch + [0, 0, 1], # Bad afternoon + [0, 0, 1], # Bad evening + [1, 0, 0], # Good night + ], + dtype=torch.float32, # Add float32 data type + ).unsqueeze( + 0 + ) # Add batch dimension + + with torch.no_grad(): + predictions, _ = model(good_day) + final_mood = predictions[0, -1].item() + print(f"\nPredicted mood for the good day: {final_mood:.2%}") + + with torch.no_grad(): + predictions, _ = model(bad_day) + final_mood = predictions[0, -1].item() + print(f"Predicted mood for the bad day: {final_mood:.2%}") diff --git a/src/journey_to_transformer/04_lstm_next_char_pred.py b/src/journey_to_transformer/04_lstm_next_char_pred.py index 2401d17..b2d953d 100644 --- a/src/journey_to_transformer/04_lstm_next_char_pred.py +++ b/src/journey_to_transformer/04_lstm_next_char_pred.py @@ -1,3 +1,39 @@ +""" +Long Short-Term Memory (LSTM) Networks and Their Significance + +LSTMs were introduced in 1997 by Hochreiter & Schmidhuber to solve the vanishing gradient +problem in traditional RNNs. They're particularly good at learning long-term dependencies +in sequential data. + +Key Components of an LSTM: +1. Forget Gate: Decides what information to throw away from the cell state +2. Input Gate: Decides which new information to store in the cell state +3. Candidate Memory: Creates new candidate values that could be added to the state +4. Output Gate: Decides what parts of the cell state to output + +The LSTM's power comes from its cell state (C_t), which acts like a conveyor belt. +Information can flow along it unchanged, and the network can learn to add or remove +information from the cell state, regulated by the gates. + +The gates are the key innovation: +- They use sigmoid functions that output numbers between 0 and 1 +- These numbers are used as filters (0 = "let nothing through", 1 = "let everything through") +- The network learns what information is important to keep or throw away + +Mathematical Formulation: +f_t = Ļƒ(W_f Ā· [h_{t-1}, x_t] + b_f) # Forget gate +i_t = Ļƒ(W_i Ā· [h_{t-1}, x_t] + b_i) # Input gate +CĢƒ_t = tanh(W_c Ā· [h_{t-1}, x_t] + b_c) # Candidate memory +o_t = Ļƒ(W_o Ā· [h_{t-1}, x_t] + b_o) # Output gate +C_t = f_t * C_{t-1} + i_t * CĢƒ_t # Cell state update +h_t = o_t * tanh(C_t) # Hidden state update + +Where: +- Ļƒ is the sigmoid function +- * is element-wise multiplication +- [h_{t-1}, x_t] is concatenation of previous hidden state and current input +""" + import torch import torch.nn as nn import string @@ -8,6 +44,7 @@ class TextPredictor(nn.Module): Neural network for predicting the next character in a sequence. Uses LSTM (Long Short-Term Memory) architecture for understanding patterns in text. """ + def __init__(self, vocab_size, embedding_dim=32, hidden_size=128): super().__init__() self.hidden_size = hidden_size @@ -57,6 +94,7 @@ class TextProcessor: Handles conversion between text and the numerical format needed by the network. Think of it as a translator between human-readable text and network-readable numbers. """ + def __init__(self): # Create character mappings using all printable ASCII characters # - Includes letters, numbers, punctuation, and whitespace @@ -79,7 +117,7 @@ def decode(self, indices): def generate_text(model, processor, start_text="Hello", length=100, temperature=0.8): """ Generate new text by sampling from the model's predictions. - + Parameters: - start_text: initial text to seed the generation - length: how many characters to generate From 642475a2eff145d7723cbcaa1378ae6d79d1c2a1 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 12:03:58 +0100 Subject: [PATCH 09/18] docs(word2vec): add comprehensive documentation and comments - Add detailed module docstring explaining Word2Vec theory and implementation - Document model architecture and training process - Add explanatory comments for key algorithms and data structures - Include examples of semantic relationships in embeddings - Explain negative sampling and context window concepts - Add inline comments for code clarity and maintainability This documentation helps developers understand both the theoretical foundations and practical implementation details of the Word2Vec model. --- .../04_rnn_vs_lstm_mem.py | 144 +++++++++++ ...char_pred.py => 05_lstm_next_char_pred.py} | 0 src/journey_to_transformer/06_word2vec.py | 243 ++++++++++++++++++ 3 files changed, 387 insertions(+) create mode 100644 src/journey_to_transformer/04_rnn_vs_lstm_mem.py rename src/journey_to_transformer/{04_lstm_next_char_pred.py => 05_lstm_next_char_pred.py} (100%) create mode 100644 src/journey_to_transformer/06_word2vec.py diff --git a/src/journey_to_transformer/04_rnn_vs_lstm_mem.py b/src/journey_to_transformer/04_rnn_vs_lstm_mem.py new file mode 100644 index 0000000..b6f7bfc --- /dev/null +++ b/src/journey_to_transformer/04_rnn_vs_lstm_mem.py @@ -0,0 +1,144 @@ +import torch +import torch.nn as nn +import matplotlib.pyplot as plt + + +class SimpleRNN(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.hidden_size = hidden_size + self.rnn_cell = nn.RNNCell(input_size, hidden_size) + self.output = nn.Linear(hidden_size, output_size) + + def forward(self, x, hidden=None): + if hidden is None: + hidden = torch.zeros(x.size(0), self.hidden_size) + + outputs = [] + for t in range(x.size(1)): + hidden = self.rnn_cell(x[:, t, :], hidden) + output = self.output(hidden) + outputs.append(output) + return torch.stack(outputs, 1) + + +class SimpleLSTM(nn.Module): + def __init__(self, input_size, hidden_size, output_size): + super().__init__() + self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True) + self.output = nn.Linear(hidden_size, output_size) + + def forward(self, x): + lstm_out, _ = self.lstm(x) + return self.output(lstm_out) + + +def create_tricky_memory_test(sequence_length=100, batch_size=32): + """ + Creates a MUCH harder memory test: + - Longer sequences (100 timesteps) + - Multiple important events to remember + - Random noise to distract the network + - Multiple sequences at once (batch_size) + """ + # Initialize input and target sequences + x = torch.zeros(batch_size, sequence_length, 5) # 5 input features now! + y = torch.zeros(batch_size, sequence_length, 1) + + for b in range(batch_size): + # Place important events (1s) at random positions in first channel + important_positions = torch.randint(0, sequence_length // 2, (2,)) + x[b, important_positions, 0] = 1 + + # Add random noise in other channels + x[b, :, 1:] = torch.randn(sequence_length, 4) * 0.5 + + # Target: Remember the important events forever + for pos in important_positions: + y[b, pos:, 0] = 1 + + return x, y + + +# Training function with visualization +def train_and_compare(sequence_length=100, hidden_size=32, epochs=200): + # Create models + rnn_model = SimpleRNN(input_size=5, hidden_size=hidden_size, output_size=1) + lstm_model = SimpleLSTM(input_size=5, hidden_size=hidden_size, output_size=1) + + # Training setup + criterion = nn.BCEWithLogitsLoss() + rnn_optimizer = torch.optim.Adam(rnn_model.parameters(), lr=0.01) + lstm_optimizer = torch.optim.Adam(lstm_model.parameters(), lr=0.01) + + # Training history + rnn_losses = [] + lstm_losses = [] + + print("Training both models...") + for epoch in range(epochs): + # Generate new random sequences each epoch + x, y = create_tricky_memory_test(sequence_length) + + # Train RNN + rnn_optimizer.zero_grad() + rnn_out = rnn_model(x) + rnn_loss = criterion(rnn_out, y) + rnn_loss.backward() + rnn_optimizer.step() + rnn_losses.append(rnn_loss.item()) + + # Train LSTM + lstm_optimizer.zero_grad() + lstm_out = lstm_model(x) + lstm_loss = criterion(lstm_out, y) + lstm_loss.backward() + lstm_optimizer.step() + lstm_losses.append(lstm_loss.item()) + + if (epoch + 1) % 20 == 0: + print(f"Epoch {epoch+1}/{epochs}") + print(f"RNN Loss: {rnn_loss.item():.4f}") + print(f"LSTM Loss: {lstm_loss.item():.4f}\n") + + # Plot training progress + plt.figure(figsize=(10, 5)) + plt.plot(rnn_losses, label="RNN") + plt.plot(lstm_losses, label="LSTM") + plt.title("Training Loss Over Time") + plt.xlabel("Epoch") + plt.ylabel("Loss") + plt.legend() + plt.grid(True) + plt.show() + + # Test with a single sequence for visualization + x_test, y_test = create_tricky_memory_test(sequence_length, batch_size=1) + + with torch.no_grad(): + rnn_test = torch.sigmoid(rnn_model(x_test)) + lstm_test = torch.sigmoid(lstm_model(x_test)) + + # Plot test sequence predictions + plt.figure(figsize=(15, 5)) + + plt.subplot(1, 2, 1) + plt.plot(x_test[0, :, 0].numpy(), label="Important Events", marker="o") + plt.plot(rnn_test[0, :, 0].numpy(), label="RNN Prediction", alpha=0.7) + plt.title("RNN Memory Test") + plt.legend() + plt.grid(True) + + plt.subplot(1, 2, 2) + plt.plot(x_test[0, :, 0].numpy(), label="Important Events", marker="o") + plt.plot(lstm_test[0, :, 0].numpy(), label="LSTM Prediction", alpha=0.7) + plt.title("LSTM Memory Test") + plt.legend() + plt.grid(True) + + plt.tight_layout() + plt.show() + + +# Run the comparison! +train_and_compare() diff --git a/src/journey_to_transformer/04_lstm_next_char_pred.py b/src/journey_to_transformer/05_lstm_next_char_pred.py similarity index 100% rename from src/journey_to_transformer/04_lstm_next_char_pred.py rename to src/journey_to_transformer/05_lstm_next_char_pred.py diff --git a/src/journey_to_transformer/06_word2vec.py b/src/journey_to_transformer/06_word2vec.py new file mode 100644 index 0000000..83ab0be --- /dev/null +++ b/src/journey_to_transformer/06_word2vec.py @@ -0,0 +1,243 @@ +""" +Word2Vec: Understanding Words Through Context + +Word2Vec, introduced by Mikolov et al. at Google in 2013, revolutionized how computers understand +words by learning their meaning from context. The key insight was: words that appear in similar +contexts probably have similar meanings. + +For example, in these sentences: +- "The cat drinks milk" +- "The dog drinks water" +We can guess that 'cat' and 'dog' are similar because they appear in similar contexts. + +The model works by: +1. Converting each word to a dense vector (embedding) +2. Learning to predict context words from target words (or vice versa) +3. Similar words end up with similar vectors + +Two main architectures: +- Skip-gram: Predict context words from target word +- CBOW (Continuous Bag of Words): Predict target word from context words + +This implementation uses Skip-gram with negative sampling: +- For each word, look at nearby words (within a window) +- Learn to predict these context words (positive samples) +- Also learn to NOT predict random other words (negative samples) + +The resulting word embeddings capture semantic relationships: +king - man + woman ā‰ˆ queen +paris - france + italy ā‰ˆ rome +""" + +import torch +import torch.nn as nn +import torch.optim as optim +import numpy as np +from collections import Counter, deque +import random + + +class Word2Vec(nn.Module): + """ + Neural network for learning word embeddings. + Uses two embedding layers: + - target_embeddings: for the main word we're looking at + - context_embeddings: for the surrounding words + """ + + def __init__(self, vocab_size, embedding_dim): + super().__init__() + # Two separate embedding layers: + # - When a word is the target, we use target_embeddings + # - When a word is in the context, we use context_embeddings + # This asymmetry helps learn richer representations + self.target_embeddings = nn.Embedding(vocab_size, embedding_dim) + self.context_embeddings = nn.Embedding(vocab_size, embedding_dim) + + # Initialize with small random values to break symmetry + # Without this, all words would start too similar + self.target_embeddings.weight.data.uniform_(-0.1, 0.1) + self.context_embeddings.weight.data.uniform_(-0.1, 0.1) + + def forward(self, target_word, context_word): + # Get vector representations + target_embed = self.target_embeddings(target_word) + context_embed = self.context_embeddings(context_word) + + # Compute similarity using dot product + # Similar words should have vectors pointing in similar directions + similarity = torch.sum(target_embed * context_embed, dim=1) + + return torch.sigmoid(similarity) + + def get_embedding(self, word_idx): + # For using the trained model, we only need target embeddings + # Context embeddings are just for training + return self.target_embeddings(torch.tensor([word_idx])).detach() + + +class Word2VecTrainer: + """ + Handles the training process for Word2Vec: + 1. Creates vocabulary from text + 2. Generates training pairs (target word + context) + 3. Trains the model using negative sampling + """ + + def __init__(self, text, embedding_dim=64, window_size=2, min_count=5): + self.window_size = window_size # How many words to look at on each side + + # Create vocabulary from text + words = text.lower().split() + word_counts = Counter(words) + + # Filter out rare words (appear less than min_count times) + # This reduces noise and speeds up training + filtered_words = [ + (word, count) for word, count in word_counts.items() if count >= min_count + ] + + # Create word-to-index mappings + self.vocab = {word: idx for idx, (word, _) in enumerate(filtered_words)} + self.idx_to_word = {idx: word for word, idx in self.vocab.items()} + self.vocab_size = len(self.vocab) + + # Generate training pairs + self.training_pairs = self._create_training_pairs(words) + + # Initialize model and training tools + self.model = Word2Vec(self.vocab_size, embedding_dim) + self.optimizer = optim.Adam(self.model.parameters()) + self.criterion = nn.BCELoss() + + def _create_training_pairs(self, words): + """ + Creates training pairs using sliding window approach: + - For each word (target), look at nearby words (context) + - Create positive pairs (target + actual context word) + - Create negative pairs (target + random word) + """ + pairs = [] + window = deque(maxlen=2 * self.window_size + 1) + + for word in words: + if word in self.vocab: + window.append(word) + if len(window) == 2 * self.window_size + 1: + target = window[self.window_size] # Middle word + # Get context words (words before and after target) + context = ( + list(window)[: self.window_size] + + list(window)[self.window_size + 1 :] + ) + + for ctx_word in context: + if ctx_word in self.vocab: + # Positive pair: target word + context word (label = 1) + pairs.append( + (self.vocab[target], self.vocab[ctx_word], 1.0) + ) + + # Negative pair: target word + random word (label = 0) + # Keep sampling until we get a word not in current context + neg_idx = random.randint(0, self.vocab_size - 1) + while self.idx_to_word[neg_idx] in context + [target]: + neg_idx = random.randint(0, self.vocab_size - 1) + + pairs.append((self.vocab[target], neg_idx, 0.0)) + return pairs + + def train(self, epochs=100, batch_size=24): + """ + Trains the model using mini-batch gradient descent: + 1. Split data into batches + 2. For each batch: + - Make predictions + - Calculate loss + - Update model weights + """ + print(f"Training Word2Vec model with {self.vocab_size} words...") + for epoch in range(epochs): + total_loss = 0 + # Shuffle pairs to prevent learning order dependencies + random.shuffle(self.training_pairs) + + # Process in batches for efficiency + for i in range(0, len(self.training_pairs), batch_size): + batch = self.training_pairs[i : i + batch_size] + targets, contexts, labels = zip(*batch) + + # Convert to PyTorch tensors + target_tensor = torch.tensor(targets) + context_tensor = torch.tensor(contexts) + label_tensor = torch.tensor(labels, dtype=torch.float32) + + # Training step + self.optimizer.zero_grad() # Reset gradients + outputs = self.model(target_tensor, context_tensor) # Forward pass + loss = self.criterion(outputs, label_tensor) # Calculate loss + loss.backward() # Backward pass + self.optimizer.step() # Update weights + + total_loss += loss.item() + + # Print progress + avg_loss = total_loss / (len(self.training_pairs) / batch_size) + print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}") + + def get_similar_words(self, word, n=5): + """ + Finds words with similar meanings by: + 1. Getting the target word's embedding + 2. Computing similarity with all other words + 3. Returning the most similar ones + """ + if word not in self.vocab: + return [] + + # Get embedding for input word + word_embedding = self.model.get_embedding(self.vocab[word]) + + # Compare with all other words using cosine similarity + similarities = [] + for other_word, idx in self.vocab.items(): + if other_word != word: + other_embedding = self.model.get_embedding(idx) + similarity = torch.cosine_similarity(word_embedding, other_embedding) + similarities.append((other_word, similarity.item())) + + # Return top N most similar words + return sorted(similarities, key=lambda x: x[1], reverse=True)[:n] + + +if __name__ == "__main__": + # Example text with related concepts + text = """ + The quick brown fox jumps over the lazy dog. + The fox is quick and brown and jumps high. + The dog is lazy and sleeps all day. + The quick rabbit jumps over the fence. + The brown bear likes honey and fish. + Fish swim in the river all day. + Dogs and foxes are related animals. + Bears and foxes live in the forest. + """ + + # Create and train model + trainer = Word2VecTrainer( + text, + embedding_dim=64, # Size of word vectors + window_size=2, # Words to consider as context + min_count=2, # Minimum word frequency + ) + + trainer.train(epochs=100, batch_size=24) + + # Test the model by finding similar words + test_words = ["quick", "fox", "dog", "river", "bear"] + for word in test_words: + similar = trainer.get_similar_words(word) + print(f"\nWords similar to '{word}':") + for similar_word, similarity in similar: + if similarity > 0.4: + print(f" {similar_word}: {similarity:.3f}") From 16a105bde38133ddf265bb724397dd93bd3e7384 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 12:58:18 +0100 Subject: [PATCH 10/18] docs(softmax): enhance educational comments and documentation - Add comprehensive module docstring explaining softmax history and importance - Include detailed function docstrings with mathematical explanations - Add scenario-based examples with explanatory comments - Improve print statements with educational context - Structure code sections with clear learning objectives This commit improves the educational value of the softmax implementation by providing deeper context and clearer explanations of the concepts. --- src/journey_to_transformer/07_softmax.py | 101 +++++++++++++++++++++++ 1 file changed, 101 insertions(+) create mode 100644 src/journey_to_transformer/07_softmax.py diff --git a/src/journey_to_transformer/07_softmax.py b/src/journey_to_transformer/07_softmax.py new file mode 100644 index 0000000..261a716 --- /dev/null +++ b/src/journey_to_transformer/07_softmax.py @@ -0,0 +1,101 @@ +""" +Understanding Softmax: The Neural Network's Decision Maker + +Softmax is a crucial function in neural networks that converts raw scores (logits) into +probabilities. It's used when we want our network to make decisions between multiple options. + +Key Properties of Softmax: +1. Converts any real numbers into probabilities (0-1) +2. Ensures all outputs sum to 1.0 +3. Maintains relative differences (bigger inputs = bigger probabilities) +4. Handles both positive and negative numbers + +Historical Significance: +- Introduced in 1959 by R. Duncan Luce in "Individual Choice Behavior" +- Became fundamental in neural networks during the 1980s +- Critical for modern classification tasks + +Why We Need Softmax: +- Raw neural network outputs can be any number +- We often need probabilities for decision making +- Helps with training stability +- Makes outputs interpretable +""" + +import torch +import torch.nn as nn + +# Raw scores for where to get lunch +scores = torch.tensor([10.0, 2.0, 5.0]) # Pizza, Salad, Tacos +print("Raw scores:", scores) + +def softmax(x): + """ + Converts raw scores into probabilities using the softmax function: + P(i) = exp(x[i]) / sum(exp(x)) + + Why exp()? + - Always positive (we can't have negative probabilities) + - Maintains relative differences + - Differentiable (important for training) + """ + exp_x = torch.exp(x) # Step 1: Convert to positive numbers + return exp_x / exp_x.sum() # Step 2: Normalize to sum to 1 + +# Apply softmax to our lunch scores +probabilities = softmax(scores) +print("\nAfter softmax (probabilities):", probabilities) +print("Notice they sum to 1:", probabilities.sum()) + +class SimpleClassifier(nn.Module): + """ + A basic neural network classifier that demonstrates softmax in action. + + Architecture: + - Input layer (2 features) + - Single linear layer + - Output layer (3 classes) + """ + def __init__(self): + super().__init__() + self.layer = nn.Linear(2, 3) # 2 inputs, 3 classes + # Note: PyTorch's CrossEntropyLoss includes softmax! + + def forward(self, x): + # Raw scores (logits) + scores = self.layer(x) + + # Compare outputs before and after softmax + raw_output = scores # Raw network outputs (can be any number) + probability_output = softmax(scores) # Converted to probabilities + + return raw_output, probability_output + +# Demonstrate with real data +model = SimpleClassifier() +# Two samples with two features each +input_data = torch.tensor([[2.0, 1.0], [1.0, 3.0]]) + +raw, probs = model(input_data) +print("\nRaw network outputs (can be any number):") +print(raw) +print("\nAfter softmax (nice probabilities between 0-1):") +for i in range(len(probs)): + print(f"Sample {i+1}: {probs[i]}") +print("\nEach row sums to:", probs.sum(dim=1)) # Always 1! + +# Demonstrate how softmax handles different scenarios +print("\nScenario 1: Similar inputs") +small_diffs = torch.tensor([2.0, 2.1, 2.2]) +print("Input:", small_diffs) +print("Output (notice gentle preferences):", softmax(small_diffs)) + +print("\nScenario 2: Very different inputs") +big_diffs = torch.tensor([2.0, 4.0, 2.2]) +print("Input:", big_diffs) +print("Output (notice strong preference):", softmax(big_diffs)) + +print("\nScenario 3: Mixed positive/negative") +mixed_numbers = torch.tensor([-1.0, 5.0, 2.0]) +print("Input:", mixed_numbers) +print("Output (still works!):", softmax(mixed_numbers)) From 8112c0cfa6be65169cfb811b58ce4f4caaffa1fa Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Tue, 19 Nov 2024 13:20:51 +0100 Subject: [PATCH 11/18] feat(attention): implement basic attention mechanism demo Add educational implementation of attention mechanisms showing: - Word embeddings and vocabulary mapping - Simple dot product attention scoring - Sentence-level search functionality This commit provides a foundational example for understanding attention mechanisms, a key component of transformer architectures. Key features: - CoolAttention class with word embeddings - Sentence encoding and attention search - Visualization of attention scores - Detailed documentation of concepts and history Technical details: - 5-dimensional word embeddings - Case-sensitive word matching - Dot product attention scoring --- src/journey_to_transformer/08_attention.py | 133 +++++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 src/journey_to_transformer/08_attention.py diff --git a/src/journey_to_transformer/08_attention.py b/src/journey_to_transformer/08_attention.py new file mode 100644 index 0000000..7ae3e34 --- /dev/null +++ b/src/journey_to_transformer/08_attention.py @@ -0,0 +1,133 @@ +""" +Understanding Attention Mechanisms in Neural Networks + +Attention mechanisms are a fundamental concept in modern deep learning, especially in +transformers. This example demonstrates a simple attention mechanism that can: +1. Encode sentences into vector representations +2. Calculate attention scores between words +3. Find relevant sentences based on attention + +Historical Significance: +- Introduced in 2014 by Bahdanau et al. for machine translation +- Revolutionary because it allowed models to "focus" on relevant parts of input +- Led directly to the transformer architecture (2017) and modern LLMs + +Key Concepts Demonstrated: +1. Word Embeddings: Converting words to vectors +2. Attention Scores: Measuring relevance between vectors +3. Dot Product Attention: Simplest form of attention mechanism + +The network below uses: +- Word embeddings (5 dimensions per word) +- Simple dot product attention +- Mean pooling for sentence-level scores +""" + +import torch +import torch.nn as nn + + +class CoolAttention(nn.Module): + def __init__(self): + super().__init__() + + # Our dataset: simple sentences about food preferences + # Each sentence follows pattern: [Person] [Verb] [Food] + self.story = [ + "Alice loves pizza", + "Bob hates broccoli", + "Charlie eats cookies", + "Alice likes cake", + "Bob loves sushi", + ] + + # Vocabulary mapping: convert words to unique indices + # Organized by semantic categories (people, verbs, foods) + self.word2idx = { + # People embeddings (indices 0-2) + "Alice": 0, + "Bob": 1, + "Charlie": 2, + # Verb embeddings (indices 3-6) + "loves": 3, + "hates": 4, + "likes": 5, + "eats": 6, + # Food embeddings (indices 7-11) + "pizza": 7, + "broccoli": 8, + "cookies": 9, + "cake": 10, + "sushi": 11, + } + + # Create learnable word embeddings + # - Each word gets a 5-dimensional vector + # - These vectors are randomly initialized and could be trained + # - 5 dimensions is arbitrary (could be larger for more complex relationships) + self.embeddings = nn.Embedding(len(self.word2idx), 5) + + def encode_sentence(self, sentence): + """ + Convert a sentence into its vector representation. + + Args: + sentence (str): Input sentence to encode + + Returns: + torch.Tensor: Tensor of word embeddings (shape: [num_words, embedding_dim]) + """ + # Split sentence into words and convert to indices + words = sentence.split() + indices = [self.word2idx[word] for word in words] + # Look up embeddings for each word + return self.embeddings(torch.tensor(indices)) + + def attention_search(self, person): + """ + Find sentences relevant to a specific person using attention. + + Args: + person (str): Person to search for + + Returns: + list: Sorted list of (sentence, attention_score) tuples + """ + results = [] + + # Step 1: Convert all sentences to vector representations + encoded_sentences = [self.encode_sentence(s) for s in self.story] + + # Step 2: Calculate attention scores for each sentence + for i, sentence_embedding in enumerate(encoded_sentences): + # Calculate attention using dot product between: + # - First word of sentence (usually the person) + # - Embedding of the search query (person) + score = torch.mean( + sentence_embedding[0] + * self.embeddings(torch.tensor([self.word2idx[person]])) + ) + results.append((self.story[i], score.item())) + + # Step 3: Sort results by attention score (highest first) + results.sort(key=lambda x: x[1], reverse=True) + return results + + +# Demo the attention mechanism +attention = CoolAttention() + +# Search for Alice's food preferences +print("šŸ” Searching for Alice's food preferences...") +results = attention.attention_search("Alice") +for sentence, score in results: + # Visualize attention scores with stars + attention_emojis = "šŸŒŸ" * int(score * 5) + print(f"{attention_emojis} {sentence}") + +# Search for Bob's food preferences +print("\nšŸ” Now searching for Bob's food preferences...") +results = attention.attention_search("Bob") +for sentence, score in results: + attention_emojis = "šŸŒŸ" * int(score * 5) + print(f"{attention_emojis} {sentence}") From 8e524d4219050ebc7003dbd4177b867270e64075 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Thu, 21 Nov 2024 16:46:50 +0100 Subject: [PATCH 12/18] refactor: Add tokenizer vocabulary file Added a new file, tokenizer_vocab.json, which contains the vocabulary mapping for the tokenizer used in the project. This file includes character-to-index mappings and special tokens such as PAD, BOS, EOS, and UNK. --- src/journey_to_transformer/09_mini_gpt.py | 456 ++++++++++++++++++++++ tokenizer_vocab.json | 63 +++ 2 files changed, 519 insertions(+) create mode 100644 src/journey_to_transformer/09_mini_gpt.py create mode 100644 tokenizer_vocab.json diff --git a/src/journey_to_transformer/09_mini_gpt.py b/src/journey_to_transformer/09_mini_gpt.py new file mode 100644 index 0000000..9bf4be6 --- /dev/null +++ b/src/journey_to_transformer/09_mini_gpt.py @@ -0,0 +1,456 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math +import json +from collections import Counter, defaultdict +import re + + +class MultiHeadAttention(nn.Module): + def __init__(self, config): + super().__init__() + self.num_heads = config.num_heads + self.head_size = config.head_size + self.dropout = config.dropout + + # Create the query, key, and value projections for all heads + self.query = nn.Linear(config.n_embd, config.n_embd) + self.key = nn.Linear(config.n_embd, config.n_embd) + self.value = nn.Linear(config.n_embd, config.n_embd) + + # Output projection + self.proj = nn.Linear(config.n_embd, config.n_embd) + + # Causal mask to ensure we only attend to previous tokens + self.register_buffer( + "mask", torch.tril(torch.ones(config.block_size, config.block_size)) + ) + + def forward(self, x): + B, T, C = x.shape # batch, sequence length, embedding dim + + # Split into heads and move head dimension to be next to batch + q = self.query(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) + k = self.key(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) + v = self.value(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) + + # Compute attention scores ("affinities") + att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) + att = att.masked_fill(self.mask[:T, :T] == 0, float("-inf")) + att = F.softmax(att, dim=-1) + att = F.dropout(att, p=self.dropout, training=self.training) + + # Apply attention to values + out = att @ v + + # Restore time as dimension 1 + out = out.transpose(1, 2).contiguous().view(B, T, C) + + return self.proj(out) + + +class FeedForward(nn.Module): + def __init__(self, config): + super().__init__() + self.net = nn.Sequential( + # First we expand + nn.Linear(config.n_embd, 4 * config.n_embd), + nn.ReLU(), + # Then we shrink back down + nn.Linear(4 * config.n_embd, config.n_embd), + nn.Dropout(config.dropout), + ) + + def forward(self, x): + return self.net(x) + + +class TransformerBlock(nn.Module): + def __init__(self, config): + super().__init__() + self.attention = MultiHeadAttention(config) + self.feed_forward = FeedForward(config) + self.ln1 = nn.LayerNorm(config.n_embd) + self.ln2 = nn.LayerNorm(config.n_embd) + + def forward(self, x): + # Attention with residual connection + x = x + self.attention(self.ln1(x)) + # Feed forward with residual connection + x = x + self.feed_forward(self.ln2(x)) + return x + + +class MiniGPT(nn.Module): + def __init__(self, config): + super().__init__() + # Store config as instance variable + self.config = config + + # Token embedding table + self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd) + # Position embedding table + self.position_embedding = nn.Embedding(config.block_size, config.n_embd) + + # Transformer blocks + self.blocks = nn.ModuleList( + [TransformerBlock(config) for _ in range(config.n_layer)] + ) + + # Final layer norm + self.ln_f = nn.LayerNorm(config.n_embd) + + # Language model head + self.lm_head = nn.Linear(config.n_embd, config.vocab_size) + + # Initialize weights + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, nn.Linear): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + if module.bias is not None: + torch.nn.init.zeros_(module.bias) + elif isinstance(module, nn.Embedding): + torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) + + def forward(self, idx, targets=None): + B, T = idx.shape + + # Get token embeddings + tok_emb = self.token_embedding(idx) + # Get position embeddings + pos_emb = self.position_embedding(torch.arange(T, device=idx.device)) + # Combine them + x = tok_emb + pos_emb + + # Apply transformer blocks + for block in self.blocks: + x = block(x) + + # Apply final layer norm + x = self.ln_f(x) + + # Get logits + logits = self.lm_head(x) + + # If we have targets, compute the loss + if targets is not None: + loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) + return logits, loss + + return logits + + @torch.no_grad() + def generate(self, idx, max_new_tokens, temperature=1.0, sample_fn=None): + # idx is (B, T) array of indices in the current context + for _ in range(max_new_tokens): + # Crop context to block_size + context = idx[:, -self.config.block_size:] + # Get predictions + logits = self(context) + # Focus only on the last time step + logits = logits[:, -1, :] + + # Use custom sampling function if provided, otherwise default sampling + if sample_fn is not None: + idx_next = sample_fn(logits) + else: + # Default sampling logic + logits = logits / temperature + probs = F.softmax(logits, dim=-1) + idx_next = torch.multinomial(probs, num_samples=1) + + # Append sampled index to the running sequence + idx = torch.cat((idx, idx_next), dim=1) + + return idx + + +# Configuration class to hold hyperparameters +class GPTConfig: + def __init__( + self, + vocab_size, + block_size, + n_layer=6, + n_embd=384, + num_heads=6, + head_size=64, + dropout=0.1, + ): + self.vocab_size = vocab_size # Size of vocabulary + self.block_size = block_size # Maximum sequence length + self.n_layer = n_layer # Number of transformer blocks + self.n_embd = n_embd # Embedding dimension + self.num_heads = num_heads # Number of attention heads + self.head_size = head_size # Size of each attention head + self.dropout = dropout # Dropout probability + + +import torch +import torch.nn as nn +from torch.nn import functional as F +import numpy as np +from tqdm import tqdm + + +class CharacterTokenizer: + def __init__(self): + # Special tokens with meaningful names + self.special_tokens = { + 'PAD': '<|pad|>', + 'BOS': '<|bos|>', # Beginning of sequence + 'EOS': '<|eos|>', # End of sequence + 'UNK': '<|unk|>' # Unknown token + } + + # Initialize vocabularies + self.char_to_idx = {token: idx for idx, token in enumerate(self.special_tokens.values())} + self.idx_to_char = {idx: token for idx, token in enumerate(self.special_tokens.values())} + self.vocab_size = len(self.special_tokens) + + # Store special token indices for easy access + self.pad_idx = self.char_to_idx[self.special_tokens['PAD']] + self.bos_idx = self.char_to_idx[self.special_tokens['BOS']] + self.eos_idx = self.char_to_idx[self.special_tokens['EOS']] + self.unk_idx = self.char_to_idx[self.special_tokens['UNK']] + + def fit(self, text): + """Build vocabulary from text.""" + # Find all unique characters + unique_chars = sorted(set(text)) + + # Add to vocabulary if not already present + for char in unique_chars: + if char not in self.char_to_idx: + idx = len(self.char_to_idx) + self.char_to_idx[char] = idx + self.idx_to_char[idx] = char + + self.vocab_size = len(self.char_to_idx) + return self + + def encode(self, text, add_special_tokens=True): + """Convert text to token indices.""" + indices = [] + + if add_special_tokens: + indices.append(self.bos_idx) + + for char in text: + # Use UNK token for unknown characters + idx = self.char_to_idx.get(char, self.unk_idx) + indices.append(idx) + + if add_special_tokens: + indices.append(self.eos_idx) + + return indices + + def decode(self, indices, remove_special_tokens=True): + """Convert token indices back to text.""" + chars = [] + + for idx in indices: + char = self.idx_to_char.get(idx, self.special_tokens['UNK']) + if remove_special_tokens and char in self.special_tokens.values(): + continue + chars.append(char) + + return ''.join(chars) + + def batch_encode(self, texts, max_length=None, padding=True): + """Encode a batch of texts.""" + encoded = [self.encode(text) for text in texts] + + if max_length is None and padding: + max_length = max(len(seq) for seq in encoded) + + if padding: + # Pad sequences to max_length + encoded = [ + seq + [self.pad_idx] * (max_length - len(seq)) + for seq in encoded + ] + + return encoded + + def save_vocab(self, path): + """Save vocabulary to file.""" + vocab_data = { + 'char_to_idx': self.char_to_idx, + 'special_tokens': self.special_tokens + } + with open(path, 'w') as f: + json.dump(vocab_data, f, indent=2) + + @classmethod + def load_vocab(cls, path): + """Load vocabulary from file.""" + with open(path) as f: + vocab_data = json.load(f) + + tokenizer = cls() + tokenizer.char_to_idx = vocab_data['char_to_idx'] + tokenizer.special_tokens = vocab_data['special_tokens'] + tokenizer.idx_to_char = {idx: char for char, idx in tokenizer.char_to_idx.items()} + tokenizer.vocab_size = len(tokenizer.char_to_idx) + + return tokenizer + + +def get_batch(data, batch_size, block_size, device="cpu"): + """Generate a small batch of data for training""" + ix = torch.randint(len(data) - block_size, (batch_size,)) + x = torch.stack([torch.tensor(data[i : i + block_size]) for i in ix]) + y = torch.stack([torch.tensor(data[i + 1 : i + block_size + 1]) for i in ix]) + x, y = x.to(device), y.to(device) + return x, y + + +def train_model( + model, train_data, config, epochs=10, batch_size=32, learning_rate=3e-4 +): + model.train() + optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate) + + # Create progress bar for epochs + pbar = tqdm(range(epochs), desc="Training") + + losses = [] + for epoch in pbar: + # Get random batch and compute loss + X, Y = get_batch(train_data, batch_size, config.block_size) + logits, loss = model(X, Y) + + # Backward pass and optimize + optimizer.zero_grad() + loss.backward() + optimizer.step() + + losses.append(loss.item()) + + # Update progress bar + pbar.set_postfix({"loss": f"{loss.item():.4f}"}) + + return losses + + +def generate_text(model, tokenizer, start_text, max_new_tokens=50, temperature=0.7): + model.eval() + context = torch.tensor(tokenizer.encode(start_text), dtype=torch.long).unsqueeze(0) + + def sample(logits, top_k=40): + # Filter out bad tokens + v, _ = torch.topk(logits, min(top_k, logits.size(-1))) + logits[logits < v[:, [-1]]] = float('-inf') + + # Apply temperature + probs = F.softmax(logits / temperature, dim=-1) + + # Sample from the filtered distribution + return torch.multinomial(probs, num_samples=1) + + generated = model.generate( + context, + max_new_tokens=max_new_tokens, + temperature=temperature, + sample_fn=sample + ) + + return tokenizer.decode(generated[0].tolist()) + + +# Fun training data - a mix of movie quotes! +movie_quotes = """ +To infinity and beyond! +I'll be back. +May the Force be with you. +Life is like a box of chocolates. +Here's looking at you, kid. +There's no place like home. +I am your father. Or your mother. +E.T. phone home. Or not. +I see dead people and I'm not afraid. +You're gonna need a bigger boat. +Elementary, my dear Watson. +I'll have what she's having. +You can't handle the truth! +Houston, we have a problem. +Do, or do not. There is no try. +I feel the need... the need for speed! +They may take our lives, but they'll never take our freedom! +Why so serious? +I'm king of the world! +Hasta la vista, baby. +My name is Bond, James Bond. +I'm going to make him an offer he can't refuse. +You're gonna need a bigger boat. +Let's put a smile on that face. +I'm the king of the world! +What's the matter with you people? +I'm not even supposed to be here today. +Give me a break! Give peace a chance. +All right, Mr. DeMille, I'm ready for my close-up. +C'mon, let's go bowling! +Big Lebowski was a great movie. +Ich bin ein Berliner. +Dude, where's my car? +Positively fourth street. +A little bit of South Philly never hurt nobody. +""" + +if __name__ == "__main__": + # Create and fit tokenizer + tokenizer = CharacterTokenizer() + tokenizer.fit(movie_quotes) + print(f"Vocabulary size: {tokenizer.vocab_size}") + + # Print some stats + print("\nSample tokens:") + sample_tokens = list(tokenizer.char_to_idx.keys())[:20] + for token in sample_tokens: + print(f"'{token}': {tokenizer.char_to_idx[token]}") + + # Test encoding/decoding + test_text = "Life is like a box of chocolates" + encoded = tokenizer.encode(test_text) + decoded = tokenizer.decode(encoded) + print(f"\nOriginal: {test_text}") + print(f"Encoded: {encoded}") + print(f"Decoded: {decoded}") + + # Convert text to tokens with special tokens + data = tokenizer.encode(movie_quotes) + + # Optional: Save vocabulary for later use + tokenizer.save_vocab('tokenizer_vocab.json') + + # Create model config + config = GPTConfig( + vocab_size=tokenizer.vocab_size, + block_size=64, + n_layer=6, + n_embd=256, + num_heads=8, + head_size=32, + dropout=0.2 + ) + + # Create model + model = MiniGPT(config) + print("Training model...") + + # Train model + losses = train_model(model, data, config, epochs=500, batch_size=8) + + # Generate some text! + print("\nGenerating text...\n") + prompts = ["I am", "Life is", "May the", "To infinity", "My name is"] + + for prompt in prompts: + generated = generate_text(model, tokenizer, prompt, max_new_tokens=50) + print(f"Prompt: '{prompt}'") + print(f"Generated (clean): {generated}") diff --git a/tokenizer_vocab.json b/tokenizer_vocab.json new file mode 100644 index 0000000..df41db2 --- /dev/null +++ b/tokenizer_vocab.json @@ -0,0 +1,63 @@ +{ + "char_to_idx": { + "<|pad|>": 0, + "<|bos|>": 1, + "<|eos|>": 2, + "<|unk|>": 3, + "\n": 4, + " ": 5, + "!": 6, + "'": 7, + ",": 8, + "-": 9, + ".": 10, + "?": 11, + "A": 12, + "B": 13, + "C": 14, + "D": 15, + "E": 16, + "F": 17, + "G": 18, + "H": 19, + "I": 20, + "J": 21, + "L": 22, + "M": 23, + "O": 24, + "P": 25, + "S": 26, + "T": 27, + "W": 28, + "Y": 29, + "a": 30, + "b": 31, + "c": 32, + "d": 33, + "e": 34, + "f": 35, + "g": 36, + "h": 37, + "i": 38, + "k": 39, + "l": 40, + "m": 41, + "n": 42, + "o": 43, + "p": 44, + "r": 45, + "s": 46, + "t": 47, + "u": 48, + "v": 49, + "w": 50, + "x": 51, + "y": 52 + }, + "special_tokens": { + "PAD": "<|pad|>", + "BOS": "<|bos|>", + "EOS": "<|eos|>", + "UNK": "<|unk|>" + } +} \ No newline at end of file From 12cb3afd00c5197f18d6e3c9f2cf7e676ee1eef0 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Fri, 22 Nov 2024 13:04:14 +0100 Subject: [PATCH 13/18] refactor: Remove tokenizer vocabulary file --- src/journey_to_transformer/09_mini_gpt.py | 228 +++++++++++----------- tokenizer_vocab.json | 63 ------ 2 files changed, 112 insertions(+), 179 deletions(-) delete mode 100644 tokenizer_vocab.json diff --git a/src/journey_to_transformer/09_mini_gpt.py b/src/journey_to_transformer/09_mini_gpt.py index 9bf4be6..495e38c 100644 --- a/src/journey_to_transformer/09_mini_gpt.py +++ b/src/journey_to_transformer/09_mini_gpt.py @@ -1,28 +1,62 @@ +""" +MiniGPT: A Small But Powerful Transformer Implementation + +This implementation demonstrates core concepts of the transformer architecture: +1. Multi-head self-attention for capturing relationships between tokens +2. Position embeddings to maintain sequence order information +3. Feed-forward networks for processing token representations +4. Layer normalization and residual connections for stable training + +Historical Significance: +- Transformers revolutionized NLP when introduced in "Attention Is All You Need" (2017) +- GPT (Generative Pre-trained Transformer) showed that transformers could be used for + general language understanding +- The architecture scales remarkably well, leading to models like GPT-3 and GPT-4 + +Key Components: +1. Token Embeddings: Convert discrete tokens to continuous vectors +2. Position Embeddings: Add position information to tokens +3. Self-Attention: Learn relationships between tokens +4. Feed-Forward: Process token representations +5. Layer Norm: Stabilize training +""" + import torch import torch.nn as nn import torch.nn.functional as F import math import json -from collections import Counter, defaultdict -import re +from tqdm import tqdm class MultiHeadAttention(nn.Module): + """ + Multi-head attention mechanism that allows the model to jointly attend to information + from different representation subspaces at different positions. + + Key Concepts: + - Query, Key, Value: Different projections of input for attention computation + - Multiple heads: Allow attention to focus on different aspects of the input + - Causal masking: Ensures model only looks at past tokens (for autoregressive generation) + """ + def __init__(self, config): super().__init__() self.num_heads = config.num_heads self.head_size = config.head_size self.dropout = config.dropout - # Create the query, key, and value projections for all heads - self.query = nn.Linear(config.n_embd, config.n_embd) + # Create separate projections for Q,K,V + # Each head gets its own portion of the embedding dimension + self.query = nn.Linear(config.n_embd, config.n_embd) self.key = nn.Linear(config.n_embd, config.n_embd) self.value = nn.Linear(config.n_embd, config.n_embd) - # Output projection + # Final projection to combine all heads self.proj = nn.Linear(config.n_embd, config.n_embd) - # Causal mask to ensure we only attend to previous tokens + # Causal mask ensures autoregressive property + # Each token can only attend to previous tokens and itself self.register_buffer( "mask", torch.tril(torch.ones(config.block_size, config.block_size)) ) @@ -30,23 +64,25 @@ def __init__(self, config): def forward(self, x): B, T, C = x.shape # batch, sequence length, embedding dim - # Split into heads and move head dimension to be next to batch + # Split heads and transpose for parallel attention computation q = self.query(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) k = self.key(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) v = self.value(x).view(B, T, self.num_heads, self.head_size).transpose(1, 2) - # Compute attention scores ("affinities") + # Scaled dot-product attention + # Scale factor prevents softmax saturation with large embedding dimensions att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) - att = att.masked_fill(self.mask[:T, :T] == 0, float("-inf")) - att = F.softmax(att, dim=-1) - att = F.dropout(att, p=self.dropout, training=self.training) + att = att.masked_fill( + self.mask[:T, :T] == 0, float("-inf") + ) # Apply causal mask + att = F.softmax(att, dim=-1) # Convert to probabilities + att = F.dropout(att, p=self.dropout, training=self.training) # Apply dropout - # Apply attention to values + # Combine attention weights with values out = att @ v - # Restore time as dimension 1 + # Restore original dimensions and project out = out.transpose(1, 2).contiguous().view(B, T, C) - return self.proj(out) @@ -147,12 +183,12 @@ def generate(self, idx, max_new_tokens, temperature=1.0, sample_fn=None): # idx is (B, T) array of indices in the current context for _ in range(max_new_tokens): # Crop context to block_size - context = idx[:, -self.config.block_size:] + context = idx[:, -self.config.block_size :] # Get predictions logits = self(context) # Focus only on the last time step logits = logits[:, -1, :] - + # Use custom sampling function if provided, otherwise default sampling if sample_fn is not None: idx_next = sample_fn(logits) @@ -161,7 +197,7 @@ def generate(self, idx, max_new_tokens, temperature=1.0, sample_fn=None): logits = logits / temperature probs = F.softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) - + # Append sampled index to the running sequence idx = torch.cat((idx, idx_next), dim=1) @@ -177,127 +213,102 @@ def __init__( n_layer=6, n_embd=384, num_heads=6, - head_size=64, dropout=0.1, ): - self.vocab_size = vocab_size # Size of vocabulary - self.block_size = block_size # Maximum sequence length - self.n_layer = n_layer # Number of transformer blocks - self.n_embd = n_embd # Embedding dimension - self.num_heads = num_heads # Number of attention heads - self.head_size = head_size # Size of each attention head - self.dropout = dropout # Dropout probability - - -import torch -import torch.nn as nn -from torch.nn import functional as F -import numpy as np -from tqdm import tqdm + self.vocab_size = vocab_size + self.block_size = block_size + self.n_layer = n_layer + self.n_embd = n_embd + self.num_heads = num_heads + self.head_size = n_embd // num_heads # Derived from n_embd + self.dropout = dropout class CharacterTokenizer: def __init__(self): - # Special tokens with meaningful names + # Simplified special tokens - keep only what we use self.special_tokens = { - 'PAD': '<|pad|>', - 'BOS': '<|bos|>', # Beginning of sequence - 'EOS': '<|eos|>', # End of sequence - 'UNK': '<|unk|>' # Unknown token + "BOS": "<|bos|>", # Beginning of sequence + "EOS": "<|eos|>", # End of sequence } - # Initialize vocabularies self.char_to_idx = {token: idx for idx, token in enumerate(self.special_tokens.values())} self.idx_to_char = {idx: token for idx, token in enumerate(self.special_tokens.values())} self.vocab_size = len(self.special_tokens) - # Store special token indices for easy access - self.pad_idx = self.char_to_idx[self.special_tokens['PAD']] - self.bos_idx = self.char_to_idx[self.special_tokens['BOS']] - self.eos_idx = self.char_to_idx[self.special_tokens['EOS']] - self.unk_idx = self.char_to_idx[self.special_tokens['UNK']] + # Store only needed special token indices + self.bos_idx = self.char_to_idx[self.special_tokens["BOS"]] + self.eos_idx = self.char_to_idx[self.special_tokens["EOS"]] def fit(self, text): """Build vocabulary from text.""" - # Find all unique characters - unique_chars = sorted(set(text)) - - # Add to vocabulary if not already present - for char in unique_chars: + for char in sorted(set(text)): if char not in self.char_to_idx: idx = len(self.char_to_idx) self.char_to_idx[char] = idx self.idx_to_char[idx] = char - self.vocab_size = len(self.char_to_idx) return self def encode(self, text, add_special_tokens=True): """Convert text to token indices.""" indices = [] - if add_special_tokens: indices.append(self.bos_idx) - - for char in text: - # Use UNK token for unknown characters - idx = self.char_to_idx.get(char, self.unk_idx) - indices.append(idx) - + indices.extend(self.char_to_idx[char] for char in text) if add_special_tokens: indices.append(self.eos_idx) - return indices def decode(self, indices, remove_special_tokens=True): """Convert token indices back to text.""" chars = [] + special_values = set(self.special_tokens.values()) for idx in indices: - char = self.idx_to_char.get(idx, self.special_tokens['UNK']) - if remove_special_tokens and char in self.special_tokens.values(): - continue - chars.append(char) - - return ''.join(chars) - + char = self.idx_to_char[idx] + if not (remove_special_tokens and char in special_values): + chars.append(char) + return "".join(chars) + def batch_encode(self, texts, max_length=None, padding=True): """Encode a batch of texts.""" encoded = [self.encode(text) for text in texts] - + if max_length is None and padding: max_length = max(len(seq) for seq in encoded) - + if padding: # Pad sequences to max_length encoded = [ - seq + [self.pad_idx] * (max_length - len(seq)) - for seq in encoded + seq + [self.pad_idx] * (max_length - len(seq)) for seq in encoded ] - + return encoded - + def save_vocab(self, path): """Save vocabulary to file.""" vocab_data = { - 'char_to_idx': self.char_to_idx, - 'special_tokens': self.special_tokens + "char_to_idx": self.char_to_idx, + "special_tokens": self.special_tokens, } - with open(path, 'w') as f: + with open(path, "w") as f: json.dump(vocab_data, f, indent=2) - + @classmethod def load_vocab(cls, path): """Load vocabulary from file.""" with open(path) as f: vocab_data = json.load(f) - + tokenizer = cls() - tokenizer.char_to_idx = vocab_data['char_to_idx'] - tokenizer.special_tokens = vocab_data['special_tokens'] - tokenizer.idx_to_char = {idx: char for char, idx in tokenizer.char_to_idx.items()} + tokenizer.char_to_idx = vocab_data["char_to_idx"] + tokenizer.special_tokens = vocab_data["special_tokens"] + tokenizer.idx_to_char = { + idx: char for char, idx in tokenizer.char_to_idx.items() + } tokenizer.vocab_size = len(tokenizer.char_to_idx) - + return tokenizer @@ -338,28 +349,31 @@ def train_model( return losses -def generate_text(model, tokenizer, start_text, max_new_tokens=50, temperature=0.7): +def generate_text(model, tokenizer, start_text, max_new_tokens=50, temperature=0.7, top_k=10): model.eval() context = torch.tensor(tokenizer.encode(start_text), dtype=torch.long).unsqueeze(0) - - def sample(logits, top_k=40): - # Filter out bad tokens - v, _ = torch.topk(logits, min(top_k, logits.size(-1))) - logits[logits < v[:, [-1]]] = float('-inf') - + + def sample(logits, top_k=top_k): # Apply temperature - probs = F.softmax(logits / temperature, dim=-1) + logits = logits / temperature - # Sample from the filtered distribution + # Apply top-k filtering + k = min(top_k, logits.size(-1)) # Safety check + values, _ = torch.topk(logits, k) + min_value = values[:, -1].unsqueeze(-1) + logits = torch.where(logits < min_value, float('-inf'), logits) + + # Get probabilities and sample + probs = F.softmax(logits, dim=-1) return torch.multinomial(probs, num_samples=1) - + generated = model.generate( - context, + context, max_new_tokens=max_new_tokens, temperature=temperature, - sample_fn=sample + sample_fn=sample, ) - + return tokenizer.decode(generated[0].tolist()) @@ -396,7 +410,7 @@ def sample(logits, top_k=40): All right, Mr. DeMille, I'm ready for my close-up. C'mon, let's go bowling! Big Lebowski was a great movie. -Ich bin ein Berliner. +Ich bin ein Berliner, while my name is Billy Turf. Dude, where's my car? Positively fourth street. A little bit of South Philly never hurt nobody. @@ -408,35 +422,17 @@ def sample(logits, top_k=40): tokenizer.fit(movie_quotes) print(f"Vocabulary size: {tokenizer.vocab_size}") - # Print some stats - print("\nSample tokens:") - sample_tokens = list(tokenizer.char_to_idx.keys())[:20] - for token in sample_tokens: - print(f"'{token}': {tokenizer.char_to_idx[token]}") - - # Test encoding/decoding - test_text = "Life is like a box of chocolates" - encoded = tokenizer.encode(test_text) - decoded = tokenizer.decode(encoded) - print(f"\nOriginal: {test_text}") - print(f"Encoded: {encoded}") - print(f"Decoded: {decoded}") - - # Convert text to tokens with special tokens + # Convert text to tokens data = tokenizer.encode(movie_quotes) - # Optional: Save vocabulary for later use - tokenizer.save_vocab('tokenizer_vocab.json') - - # Create model config + # Create model config with simplified parameters config = GPTConfig( vocab_size=tokenizer.vocab_size, block_size=64, n_layer=6, n_embd=256, num_heads=8, - head_size=32, - dropout=0.2 + dropout=0.2, ) # Create model @@ -444,7 +440,7 @@ def sample(logits, top_k=40): print("Training model...") # Train model - losses = train_model(model, data, config, epochs=500, batch_size=8) + losses = train_model(model, data, config, epochs=750, batch_size=8) # Generate some text! print("\nGenerating text...\n") @@ -453,4 +449,4 @@ def sample(logits, top_k=40): for prompt in prompts: generated = generate_text(model, tokenizer, prompt, max_new_tokens=50) print(f"Prompt: '{prompt}'") - print(f"Generated (clean): {generated}") + print(f"Generated: {generated}") diff --git a/tokenizer_vocab.json b/tokenizer_vocab.json deleted file mode 100644 index df41db2..0000000 --- a/tokenizer_vocab.json +++ /dev/null @@ -1,63 +0,0 @@ -{ - "char_to_idx": { - "<|pad|>": 0, - "<|bos|>": 1, - "<|eos|>": 2, - "<|unk|>": 3, - "\n": 4, - " ": 5, - "!": 6, - "'": 7, - ",": 8, - "-": 9, - ".": 10, - "?": 11, - "A": 12, - "B": 13, - "C": 14, - "D": 15, - "E": 16, - "F": 17, - "G": 18, - "H": 19, - "I": 20, - "J": 21, - "L": 22, - "M": 23, - "O": 24, - "P": 25, - "S": 26, - "T": 27, - "W": 28, - "Y": 29, - "a": 30, - "b": 31, - "c": 32, - "d": 33, - "e": 34, - "f": 35, - "g": 36, - "h": 37, - "i": 38, - "k": 39, - "l": 40, - "m": 41, - "n": 42, - "o": 43, - "p": 44, - "r": 45, - "s": 46, - "t": 47, - "u": 48, - "v": 49, - "w": 50, - "x": 51, - "y": 52 - }, - "special_tokens": { - "PAD": "<|pad|>", - "BOS": "<|bos|>", - "EOS": "<|eos|>", - "UNK": "<|unk|>" - } -} \ No newline at end of file From 5b73b994997a0ae8431b6d5924a7a85d27798900 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Fri, 22 Nov 2024 14:24:10 +0100 Subject: [PATCH 14/18] refactor: Add pgmpy to requirements.txt --- requirements.txt | 1 + src/20_bayesian_medical_diagnosis.py | 288 +++++++++++++++++++++++++++ 2 files changed, 289 insertions(+) create mode 100644 src/20_bayesian_medical_diagnosis.py diff --git a/requirements.txt b/requirements.txt index fd399cc..edd7314 100644 --- a/requirements.txt +++ b/requirements.txt @@ -21,6 +21,7 @@ matplotlib plotly datasets huggingface-hub +pgmpy # Bayesian Networks # Image Processing pillow diff --git a/src/20_bayesian_medical_diagnosis.py b/src/20_bayesian_medical_diagnosis.py new file mode 100644 index 0000000..df28ad6 --- /dev/null +++ b/src/20_bayesian_medical_diagnosis.py @@ -0,0 +1,288 @@ +import json +import logging +from typing import Dict, List, Tuple, Optional +from langchain_community.chat_models import ChatOllama +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain.chains import LLMChain +from pgmpy.models import BayesianNetwork +from pgmpy.factors.discrete import TabularCPD + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +MODEL_NAME = "hermes3:latest" + + +class BayesianLLM: + def __init__(self, model_name: str = MODEL_NAME): + """Initialize the BayesianLLM system""" + self.model_name = model_name + self.llm = ChatOllama(model=model_name) + self.nodes: Dict[str, List[str]] = {} + self.network: Optional[BayesianNetwork] = None + self.patient_story: str = "" + + def create_node(self, description: str) -> Tuple[str, List[str]]: + """Create a node with states based on LLM description""" + prompt = ChatPromptTemplate.from_messages([ + ( + "system", + "You are a helpful assistant that creates nodes for Bayesian networks. Return only valid JSON.", + ), + ( + "user", + """Create a node for a Bayesian network based on this description: + "{description}" + + Return a JSON object with: + 1. A short snake_case name for the node + 2. A list of 5 possible states for this node + + Return ONLY the JSON object, no additional text or formatting: + {{"name": "node_name", "states": ["state1", "state2", "state3", "state4", "state5"]}}""", + ), + ]) + + print(f"šŸ”„ Creating node from description: '{description}'") + print("šŸ“¤ Sending request to LLM...") + + try: + chain = prompt | self.llm | StrOutputParser() + content = chain.invoke({"description": description}) + + # Clean up the response + content = content.strip() + # Remove any markdown formatting + if "```" in content: + content = content.split("```")[1] + if "json" in content.split("\n")[0]: + content = "\n".join(content.split("\n")[1:]) + # Remove any trailing backticks + content = content.replace("`", "").strip() + + try: + node_info = json.loads(content) + if not isinstance(node_info, dict) or "name" not in node_info or "states" not in node_info: + raise ValueError("Invalid JSON structure") + return node_info["name"], node_info["states"] + except json.JSONDecodeError as e: + logger.error(f"Failed to parse JSON: {content}") + logger.error(f"JSON error: {e}") + raise + except ValueError as e: + logger.error(f"Invalid JSON structure: {content}") + raise + + except Exception as e: + logger.error(f"Unexpected error in create_node: {e}") + logger.error(f"Failed description: {description}") + raise + + def extract_relationships(self, text: str) -> List[Tuple[str, str]]: + """Extract causal relationships between nodes""" + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful assistant that identifies causal relationships in text.", + ), + ( + "user", + """ + From this text, identify causal relationships between concepts: + {text} + + Use ONLY these exact node names in your response: + {nodes} + + Return a JSON array of objects with cause and effect properties: + [ + {{"cause": "node_name1", "effect": "node_name2"}} + ] + """, + ), + ] + ) + + try: + chain = prompt | self.llm | StrOutputParser() + # Pass both text and nodes as variables to the prompt + content = chain.invoke({"text": text, "nodes": list(self.nodes.keys())}) + + # Handle markdown formatting if present + if "```" in content: + content = content.split("```")[1].strip() + if content.startswith("json\n"): + content = content[5:] + + relationships = json.loads(content) + + # Map and validate relationships + valid_relationships = [ + (rel["cause"], rel["effect"]) + for rel in relationships + if rel["cause"] in self.nodes and rel["effect"] in self.nodes + ] + + logger.info(f"Extracted relationships: {valid_relationships}") + return valid_relationships + + except Exception as e: + logger.error(f"Failed to extract relationships: {e}") + return [] + + def build_network(self): + """Build the Bayesian network structure""" + print("\nšŸ”— Building network structure...") + self.network = BayesianNetwork() + + # Add nodes + for node in self.nodes: + self.network.add_node(node) + + # Add edges from relationships + medical_text = """ + In medical diagnosis, patient age and overall health status influence symptom severity. + The presence of fever often leads to fatigue symptoms. + Both symptoms and test results help determine the final diagnosis. + """ + + relationships = self.extract_relationships(medical_text) + print(f"Found {len(relationships)} relationships") + + for cause, effect in relationships: + self.network.add_edge(cause, effect) + + def extract_medical_concepts(self, story: str) -> List[str]: + """Extract relevant medical concepts from patient story""" + prompt = ChatPromptTemplate.from_messages([ + ("system", "You are a medical expert that identifies key medical concepts."), + ("user", """ + From this patient story, identify all key medical concepts that should be modeled: + {story} + + Return a JSON array of descriptions: + ["concept1", "concept2", "concept3", "concept4", "concept5"] + """) + ]) + + chain = prompt | self.llm | StrOutputParser() + content = chain.invoke({"story": story}) + return json.loads(content) + + def extract_evidence(self, story: str) -> Dict[str, str]: + """Extract evidence from patient story matching node states""" + prompt = ChatPromptTemplate.from_messages([ + ("system", "You are a medical expert that extracts patient information."), + ("user", """ + From this patient story, extract relevant states for our nodes. + Story: {story} + + Available nodes and states: + {nodes_and_states} + + Return a JSON object mapping node names to their states based on the story. + Only include nodes where there is clear evidence in the story. + """) + ]) + + nodes_str = "\n".join([f"{name}: {states}" for name, states in self.nodes.items()]) + chain = prompt | self.llm | StrOutputParser() + content = chain.invoke({"story": story, "nodes_and_states": nodes_str}) + return json.loads(content) + + def setup_medical_network(self, story: str): + """Set up a medical diagnosis network from patient story""" + self.patient_story = story + print("\nšŸ“‹ Setting up medical diagnosis network from patient story...") + + # Extract concepts from story + concepts = self.extract_medical_concepts(story) + + # Create nodes for each concept + print("\nšŸ—ļø Creating nodes...") + for i, desc in enumerate(concepts, 1): + print(f"\nNode {i}/{len(concepts)}\n") + name, states = self.create_node(desc) + self.nodes[name] = states + print(f"āœ… Created node: {name} with states: {states}") + + self.build_network() + + def generate_explanation(self, evidence: Dict[str, str]) -> str: + """Generate a natural language explanation of the network state given evidence""" + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are a medical expert that explains Bayesian network states. + Explain the relationships between variables and likely outcomes based on evidence.""", + ), + ( + "user", + """Given this Bayesian network structure and evidence, explain the likely medical implications: + + Network Nodes: {nodes} + + Current Evidence: {evidence} + + Please provide: + 1. An interpretation of the evidence + 2. Likely implications for other variables + 3. Key relationships between variables that are relevant + 4. A list of likely diagnoses + 5. Severity of symptoms + 6. Suggestions for next steps (if needed) + + Keep the explanation clear and medical-focused.""", + ), + ] + ) + + try: + chain = prompt | self.llm | StrOutputParser() + + # Format nodes for better readability + nodes_str = "\n".join( + [ + f"- {name}: {', '.join(states)}" + for name, states in self.nodes.items() + ] + ) + + # Format evidence for better readability + evidence_str = "\n".join( + [f"- {node}: {state}" for node, state in evidence.items()] + ) + + explanation = chain.invoke({"nodes": nodes_str, "evidence": evidence_str}) + + return explanation + + except Exception as e: + logger.error(f"Failed to generate explanation: {e}") + return "Unable to generate explanation due to an error." + + +def main(): + print("\nšŸš€ Initializing BayesianLLM system...") + llm = BayesianLLM() + + patient_story = """ + I am Lydia and I'm not feeling well. I feel so somber and tired. + """ + + llm.setup_medical_network(patient_story) + + # Extract evidence from story + evidence = llm.extract_evidence(patient_story) + print(f"\nExtracted Evidence: {evidence}") + + print("\nšŸ“ Generating explanation...") + explanation = llm.generate_explanation(evidence) + print(f"\nExplanation:\n{explanation}") + + +if __name__ == "__main__": + main() From 375ad6be76073180c7ffe04fa198738df2f09384 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Fri, 22 Nov 2024 14:39:48 +0100 Subject: [PATCH 15/18] refactor: Update BayesianLLM class to use actual patient story for extracting relationships --- src/20_bayesian_medical_diagnosis.py | 13 ++----------- 1 file changed, 2 insertions(+), 11 deletions(-) diff --git a/src/20_bayesian_medical_diagnosis.py b/src/20_bayesian_medical_diagnosis.py index df28ad6..be12b0a 100644 --- a/src/20_bayesian_medical_diagnosis.py +++ b/src/20_bayesian_medical_diagnosis.py @@ -141,14 +141,8 @@ def build_network(self): for node in self.nodes: self.network.add_node(node) - # Add edges from relationships - medical_text = """ - In medical diagnosis, patient age and overall health status influence symptom severity. - The presence of fever often leads to fatigue symptoms. - Both symptoms and test results help determine the final diagnosis. - """ - - relationships = self.extract_relationships(medical_text) + # Add edges from relationships using the actual patient story + relationships = self.extract_relationships(self.patient_story) print(f"Found {len(relationships)} relationships") for cause, effect in relationships: @@ -231,9 +225,6 @@ def generate_explanation(self, evidence: Dict[str, str]) -> str: 1. An interpretation of the evidence 2. Likely implications for other variables 3. Key relationships between variables that are relevant - 4. A list of likely diagnoses - 5. Severity of symptoms - 6. Suggestions for next steps (if needed) Keep the explanation clear and medical-focused.""", ), From 05980575f3b7a60b21e9fddf119659ecf4d28d56 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Fri, 22 Nov 2024 14:45:32 +0100 Subject: [PATCH 16/18] refactor: Update BayesianLLM class to use actual patient story for extracting relationships --- ...s.py => 20_bayes_medical_explanability.py} | 93 ++++++++++++++++++- 1 file changed, 89 insertions(+), 4 deletions(-) rename src/{20_bayesian_medical_diagnosis.py => 20_bayes_medical_explanability.py} (70%) diff --git a/src/20_bayesian_medical_diagnosis.py b/src/20_bayes_medical_explanability.py similarity index 70% rename from src/20_bayesian_medical_diagnosis.py rename to src/20_bayes_medical_explanability.py index be12b0a..3b9e409 100644 --- a/src/20_bayesian_medical_diagnosis.py +++ b/src/20_bayes_medical_explanability.py @@ -1,18 +1,26 @@ import json import logging -from typing import Dict, List, Tuple, Optional +from typing import Dict, List, Tuple, Optional, NamedTuple from langchain_community.chat_models import ChatOllama from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain.chains import LLMChain from pgmpy.models import BayesianNetwork from pgmpy.factors.discrete import TabularCPD +from dataclasses import dataclass logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) MODEL_NAME = "hermes3:latest" +@dataclass +class DiagnosticReasoning: + conclusion: str + confidence: float + evidence_path: List[str] + alternative_explanations: List[Tuple[str, float]] + supporting_literature: List[str] class BayesianLLM: def __init__(self, model_name: str = MODEL_NAME): @@ -255,6 +263,70 @@ def generate_explanation(self, evidence: Dict[str, str]) -> str: logger.error(f"Failed to generate explanation: {e}") return "Unable to generate explanation due to an error." + def generate_diagnostic_reasoning(self, evidence: Dict[str, str]) -> DiagnosticReasoning: + """Generate detailed diagnostic reasoning with evidence paths and confidence levels""" + prompt = ChatPromptTemplate.from_messages([ + ("system", """You are a medical expert that provides detailed diagnostic reasoning. + Format your response as JSON with the following structure: + {{ + "conclusion": "Primary diagnostic conclusion", + "confidence": 0.XX, + "evidence_path": ["step1", "step2", "step3"], + "alternative_explanations": [["alternative1", 0.XX], ["alternative2", 0.XX]], + "supporting_literature": ["reference1", "reference2"] + }}"""), + ("user", """Given this Bayesian network and evidence, provide detailed diagnostic reasoning: + Network Structure: {network_structure} + Evidence: {evidence} + Nodes and States: {nodes_states} + + Provide step-by-step reasoning, confidence levels, and alternative explanations.""") + ]) + + try: + chain = prompt | self.llm | StrOutputParser() + + # Format network structure + network_structure = [f"{cause} ā†’ {effect}" for cause, effect in self.network.edges()] + nodes_states = {node: states for node, states in self.nodes.items()} + + result = json.loads(chain.invoke({ + "network_structure": network_structure, + "evidence": evidence, + "nodes_states": nodes_states + })) + + return DiagnosticReasoning(**result) + except Exception as e: + logger.error(f"Failed to generate diagnostic reasoning: {e}") + raise + + def explain_decision_path(self, diagnosis: DiagnosticReasoning) -> str: + """Generate a human-readable explanation of the diagnostic decision path""" + prompt = ChatPromptTemplate.from_messages([ + ("system", "You are a medical expert explaining diagnostic reasoning to other medical professionals."), + ("user", """Create a detailed explanation of this diagnostic reasoning: + Conclusion: {conclusion} + Confidence: {confidence} + Evidence Path: {evidence_path} + Alternatives: {alternatives} + + Format the explanation with: + 1. Primary conclusion and confidence level + 2. Step-by-step reasoning path + 3. Key evidence relationships + 4. Alternative considerations + 5. Relevant medical literature""") + ]) + + chain = prompt | self.llm | StrOutputParser() + return chain.invoke({ + "conclusion": diagnosis.conclusion, + "confidence": diagnosis.confidence, + "evidence_path": diagnosis.evidence_path, + "alternatives": diagnosis.alternative_explanations + }) + def main(): print("\nšŸš€ Initializing BayesianLLM system...") @@ -270,9 +342,22 @@ def main(): evidence = llm.extract_evidence(patient_story) print(f"\nExtracted Evidence: {evidence}") - print("\nšŸ“ Generating explanation...") - explanation = llm.generate_explanation(evidence) - print(f"\nExplanation:\n{explanation}") + # Generate detailed diagnostic reasoning + diagnosis = llm.generate_diagnostic_reasoning(evidence) + print("\nšŸ“Š Diagnostic Analysis:") + print(f"Primary Conclusion: {diagnosis.conclusion} (Confidence: {diagnosis.confidence*100:.1f}%)") + print("\nReasoning Path:") + for step in diagnosis.evidence_path: + print(f"- {step}") + + print("\nAlternative Explanations:") + for alt, conf in diagnosis.alternative_explanations: + print(f"- {alt} ({conf*100:.1f}% confidence)") + + # Generate detailed explanation + print("\nšŸ“ Detailed Medical Explanation:") + explanation = llm.explain_decision_path(diagnosis) + print(explanation) if __name__ == "__main__": From 42cbba7b4d8a2333340ea66db9928d4d9ece7900 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Fri, 22 Nov 2024 15:04:42 +0100 Subject: [PATCH 17/18] refactor: Add diagnostics folder to .gitignore --- .gitignore | 1 + src/20_bayes_medical_explanability.py | 457 ++++++++++++++++++++------ 2 files changed, 359 insertions(+), 99 deletions(-) diff --git a/.gitignore b/.gitignore index 52376fc..a249be0 100644 --- a/.gitignore +++ b/.gitignore @@ -23,6 +23,7 @@ cache/ checkpoints/ training_summary/ visualizations/ +diagnostics/ diff --git a/src/20_bayes_medical_explanability.py b/src/20_bayes_medical_explanability.py index 3b9e409..c473f6e 100644 --- a/src/20_bayes_medical_explanability.py +++ b/src/20_bayes_medical_explanability.py @@ -1,26 +1,31 @@ import json import logging from typing import Dict, List, Tuple, Optional, NamedTuple -from langchain_community.chat_models import ChatOllama +from langchain_ollama import ChatOllama from langchain_core.output_parsers import StrOutputParser from langchain_core.prompts import ChatPromptTemplate from langchain.chains import LLMChain from pgmpy.models import BayesianNetwork from pgmpy.factors.discrete import TabularCPD from dataclasses import dataclass +import csv +from datetime import datetime +from pathlib import Path logging.basicConfig(level=logging.INFO) +logging.getLogger("httpx").setLevel(logging.WARNING) logger = logging.getLogger(__name__) MODEL_NAME = "hermes3:latest" + @dataclass class DiagnosticReasoning: conclusion: str confidence: float evidence_path: List[str] alternative_explanations: List[Tuple[str, float]] - supporting_literature: List[str] + class BayesianLLM: def __init__(self, model_name: str = MODEL_NAME): @@ -30,17 +35,94 @@ def __init__(self, model_name: str = MODEL_NAME): self.nodes: Dict[str, List[str]] = {} self.network: Optional[BayesianNetwork] = None self.patient_story: str = "" + self.log_file = Path("diagnostics/diagnostic_logs.csv") + self._initialize_log_file() + + def _initialize_log_file(self): + """Initialize the CSV log file with headers if it doesn't exist""" + if not self.log_file.exists(): + logger.info(f"Creating log file: {self.log_file}") + if not self.log_file.parent.exists(): + self.log_file.parent.mkdir(parents=True, exist_ok=True) + logger.info(f"Created parent directory: {self.log_file.parent}") + with open(self.log_file, "w", newline="", encoding="utf-8") as f: + writer = csv.writer(f) + writer.writerow( + [ + "timestamp", + "patient_story", + "extracted_evidence", + "primary_conclusion", + "confidence", + "evidence_path", + "alternative_explanations", + "network_structure", + ] + ) + + def log_diagnostic_process( + self, evidence: Dict[str, str], diagnosis: DiagnosticReasoning + ) -> None: + """Log the diagnostic process to CSV""" + try: + # Convert network structure to string representation + network_structure = ( + [f"{cause} ā†’ {effect}" for cause, effect in self.network.edges()] + if self.network + else [] + ) + + # Prepare the log entry + log_entry = { + "timestamp": datetime.now().isoformat(), + "patient_story": self.patient_story.strip(), + "extracted_evidence": json.dumps(evidence), + "primary_conclusion": diagnosis.conclusion, + "confidence": str(diagnosis.confidence), # Convert float to string + "evidence_path": json.dumps(diagnosis.evidence_path), + "alternative_explanations": json.dumps( + diagnosis.alternative_explanations + ), + "network_structure": json.dumps(network_structure), + } + + logger.debug(f"Preparing to log entry: {log_entry}") + + # Write to CSV + with open(self.log_file, "a", newline="", encoding="utf-8") as f: + writer = csv.DictWriter( + f, + fieldnames=[ + "timestamp", + "patient_story", + "extracted_evidence", + "primary_conclusion", + "confidence", + "evidence_path", + "alternative_explanations", + "network_structure", + ], + ) + + writer.writerow(log_entry) + + logger.info(f"Successfully logged diagnostic process to {self.log_file}") + + except Exception as e: + logger.error(f"Failed to log diagnostic process: {e}", exc_info=True) + raise def create_node(self, description: str) -> Tuple[str, List[str]]: """Create a node with states based on LLM description""" - prompt = ChatPromptTemplate.from_messages([ - ( - "system", - "You are a helpful assistant that creates nodes for Bayesian networks. Return only valid JSON.", - ), - ( - "user", - """Create a node for a Bayesian network based on this description: + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a helpful assistant that creates nodes for Bayesian networks. Return only valid JSON.", + ), + ( + "user", + """Create a node for a Bayesian network based on this description: "{description}" Return a JSON object with: @@ -49,8 +131,9 @@ def create_node(self, description: str) -> Tuple[str, List[str]]: Return ONLY the JSON object, no additional text or formatting: {{"name": "node_name", "states": ["state1", "state2", "state3", "state4", "state5"]}}""", - ), - ]) + ), + ] + ) print(f"šŸ”„ Creating node from description: '{description}'") print("šŸ“¤ Sending request to LLM...") @@ -71,7 +154,11 @@ def create_node(self, description: str) -> Tuple[str, List[str]]: try: node_info = json.loads(content) - if not isinstance(node_info, dict) or "name" not in node_info or "states" not in node_info: + if ( + not isinstance(node_info, dict) + or "name" not in node_info + or "states" not in node_info + ): raise ValueError("Invalid JSON structure") return node_info["name"], node_info["states"] except json.JSONDecodeError as e: @@ -158,26 +245,81 @@ def build_network(self): def extract_medical_concepts(self, story: str) -> List[str]: """Extract relevant medical concepts from patient story""" - prompt = ChatPromptTemplate.from_messages([ - ("system", "You are a medical expert that identifies key medical concepts."), - ("user", """ - From this patient story, identify all key medical concepts that should be modeled: - {story} - - Return a JSON array of descriptions: - ["concept1", "concept2", "concept3", "concept4", "concept5"] - """) - ]) - - chain = prompt | self.llm | StrOutputParser() - content = chain.invoke({"story": story}) - return json.loads(content) + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are a medical expert that identifies key medical concepts. + Return ONLY a JSON array of descriptions, no additional text or formatting. + Example: ["concept1", "concept2", "concept3"]""", + ), + ( + "user", + """From this patient story, identify all key medical concepts that should be modeled: + {story} + + Return ONLY the JSON array, no explanation or additional text.""", + ), + ] + ) + + try: + chain = prompt | self.llm | StrOutputParser() + content = chain.invoke({"story": story}) + + # Clean up the response + content = content.strip() + + # Remove any markdown formatting if present + if "```json" in content: + content = content.split("```json")[1] + if "```" in content: + content = content.split("```")[0] + + # Remove any trailing or leading whitespace or special characters + content = content.strip("`\n\r\t ") + + logger.debug(f"Cleaned medical concepts response: {content}") + + try: + concepts = json.loads(content) + if not isinstance(concepts, list): + raise ValueError("Response is not a list") + + # Ensure all elements are strings + concepts = [str(concept) for concept in concepts] + + if not concepts: + logger.warning( + "No medical concepts extracted, using fallback concepts" + ) + return ["mood state", "energy level", "fatigue symptoms"] + + logger.info(f"Extracted medical concepts: {concepts}") + return concepts + + except json.JSONDecodeError as e: + logger.error(f"Failed to parse JSON response: {content}") + logger.error(f"JSON error: {e}") + # Provide fallback concepts + return ["mood state", "energy level", "fatigue symptoms"] + + except Exception as e: + logger.error(f"Failed to extract medical concepts: {e}") + # Provide fallback concepts + return ["mood state", "energy level", "fatigue symptoms"] def extract_evidence(self, story: str) -> Dict[str, str]: """Extract evidence from patient story matching node states""" - prompt = ChatPromptTemplate.from_messages([ - ("system", "You are a medical expert that extracts patient information."), - ("user", """ + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a medical expert that extracts patient information.", + ), + ( + "user", + """ From this patient story, extract relevant states for our nodes. Story: {story} @@ -186,10 +328,14 @@ def extract_evidence(self, story: str) -> Dict[str, str]: Return a JSON object mapping node names to their states based on the story. Only include nodes where there is clear evidence in the story. - """) - ]) - - nodes_str = "\n".join([f"{name}: {states}" for name, states in self.nodes.items()]) + """, + ), + ] + ) + + nodes_str = "\n".join( + [f"{name}: {states}" for name, states in self.nodes.items()] + ) chain = prompt | self.llm | StrOutputParser() content = chain.invoke({"story": story, "nodes_and_states": nodes_str}) return json.loads(content) @@ -198,10 +344,10 @@ def setup_medical_network(self, story: str): """Set up a medical diagnosis network from patient story""" self.patient_story = story print("\nšŸ“‹ Setting up medical diagnosis network from patient story...") - + # Extract concepts from story concepts = self.extract_medical_concepts(story) - + # Create nodes for each concept print("\nšŸ—ļø Creating nodes...") for i, desc in enumerate(concepts, 1): @@ -263,49 +409,115 @@ def generate_explanation(self, evidence: Dict[str, str]) -> str: logger.error(f"Failed to generate explanation: {e}") return "Unable to generate explanation due to an error." - def generate_diagnostic_reasoning(self, evidence: Dict[str, str]) -> DiagnosticReasoning: + def generate_diagnostic_reasoning( + self, evidence: Dict[str, str] + ) -> DiagnosticReasoning: """Generate detailed diagnostic reasoning with evidence paths and confidence levels""" - prompt = ChatPromptTemplate.from_messages([ - ("system", """You are a medical expert that provides detailed diagnostic reasoning. - Format your response as JSON with the following structure: + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + """You are a medical expert that provides detailed diagnostic reasoning. + You must respond ONLY with a JSON object in this exact format: {{ "conclusion": "Primary diagnostic conclusion", "confidence": 0.XX, "evidence_path": ["step1", "step2", "step3"], "alternative_explanations": [["alternative1", 0.XX], ["alternative2", 0.XX]], - "supporting_literature": ["reference1", "reference2"] - }}"""), - ("user", """Given this Bayesian network and evidence, provide detailed diagnostic reasoning: + }} + Do not include any additional text, markdown formatting, or explanations.""", + ), + ( + "user", + """Based on this evidence and network structure, provide diagnostic reasoning: Network Structure: {network_structure} Evidence: {evidence} - Nodes and States: {nodes_states} - - Provide step-by-step reasoning, confidence levels, and alternative explanations.""") - ]) + Nodes and States: {nodes_states}""", + ), + ] + ) try: chain = prompt | self.llm | StrOutputParser() - - # Format network structure - network_structure = [f"{cause} ā†’ {effect}" for cause, effect in self.network.edges()] + + network_structure = [ + f"{cause} ā†’ {effect}" for cause, effect in self.network.edges() + ] nodes_states = {node: states for node, states in self.nodes.items()} - - result = json.loads(chain.invoke({ - "network_structure": network_structure, - "evidence": evidence, - "nodes_states": nodes_states - })) - - return DiagnosticReasoning(**result) + + response = chain.invoke( + { + "network_structure": network_structure, + "evidence": evidence, + "nodes_states": nodes_states, + } + ) + + # Clean up the response + response = response.strip() + + # Remove any markdown formatting if present + if "```json" in response: + response = response.split("```json")[1] + if "```" in response: + response = response.split("```")[0] + + # Remove any trailing or leading whitespace or special characters + response = response.strip("`\n\r\t ") + + logger.debug(f"Cleaned response: {response}") + + try: + result = json.loads(response) + + # Validate required fields + required_fields = { + "conclusion", + "confidence", + "evidence_path", + "alternative_explanations", + } + if not all(field in result for field in required_fields): + missing = required_fields - set(result.keys()) + raise ValueError(f"Missing required fields: {missing}") + + # Ensure confidence is float + result["confidence"] = float(result["confidence"]) + + # Ensure alternative_explanations format is correct + result["alternative_explanations"] = [ + [str(alt), float(conf)] + for alt, conf in result["alternative_explanations"] + ] + + return DiagnosticReasoning(**result) + + except json.JSONDecodeError as e: + logger.error(f"Failed to parse JSON response: {response}") + logger.error(f"JSON error: {e}") + # Provide a fallback response + return DiagnosticReasoning( + conclusion="Unable to generate proper diagnosis due to system error", + confidence=0.0, + evidence_path=["System encountered an error in processing"], + alternative_explanations=[], + ) + except Exception as e: logger.error(f"Failed to generate diagnostic reasoning: {e}") raise def explain_decision_path(self, diagnosis: DiagnosticReasoning) -> str: """Generate a human-readable explanation of the diagnostic decision path""" - prompt = ChatPromptTemplate.from_messages([ - ("system", "You are a medical expert explaining diagnostic reasoning to other medical professionals."), - ("user", """Create a detailed explanation of this diagnostic reasoning: + prompt = ChatPromptTemplate.from_messages( + [ + ( + "system", + "You are a medical expert explaining diagnostic reasoning to other medical professionals.", + ), + ( + "user", + """Create a detailed explanation of this diagnostic reasoning: Conclusion: {conclusion} Confidence: {confidence} Evidence Path: {evidence_path} @@ -315,50 +527,97 @@ def explain_decision_path(self, diagnosis: DiagnosticReasoning) -> str: 1. Primary conclusion and confidence level 2. Step-by-step reasoning path 3. Key evidence relationships - 4. Alternative considerations - 5. Relevant medical literature""") - ]) + 4. Alternative considerations""", + ), + ] + ) chain = prompt | self.llm | StrOutputParser() - return chain.invoke({ - "conclusion": diagnosis.conclusion, - "confidence": diagnosis.confidence, - "evidence_path": diagnosis.evidence_path, - "alternatives": diagnosis.alternative_explanations - }) + return chain.invoke( + { + "conclusion": diagnosis.conclusion, + "confidence": diagnosis.confidence, + "evidence_path": diagnosis.evidence_path, + "alternatives": diagnosis.alternative_explanations, + } + ) + + def verify_log_file(self): + """Verify that the log file exists and contains data""" + try: + if not self.log_file.exists(): + logger.error("Log file does not exist!") + return False + + with open(self.log_file, "r", encoding="utf-8") as f: + reader = csv.DictReader(f) + rows = list(reader) + logger.info(f"Log file contains {len(rows)} entries") + if rows: + logger.debug(f"Last entry: {rows[-1]}") + return True + except Exception as e: + logger.error(f"Error verifying log file: {e}", exc_info=True) + return False def main(): - print("\nšŸš€ Initializing BayesianLLM system...") - llm = BayesianLLM() - - patient_story = """ - I am Lydia and I'm not feeling well. I feel so somber and tired. - """ - - llm.setup_medical_network(patient_story) - - # Extract evidence from story - evidence = llm.extract_evidence(patient_story) - print(f"\nExtracted Evidence: {evidence}") - - # Generate detailed diagnostic reasoning - diagnosis = llm.generate_diagnostic_reasoning(evidence) - print("\nšŸ“Š Diagnostic Analysis:") - print(f"Primary Conclusion: {diagnosis.conclusion} (Confidence: {diagnosis.confidence*100:.1f}%)") - print("\nReasoning Path:") - for step in diagnosis.evidence_path: - print(f"- {step}") - - print("\nAlternative Explanations:") - for alt, conf in diagnosis.alternative_explanations: - print(f"- {alt} ({conf*100:.1f}% confidence)") - - # Generate detailed explanation - print("\nšŸ“ Detailed Medical Explanation:") - explanation = llm.explain_decision_path(diagnosis) - print(explanation) + try: + print("\nšŸš€ Initializing BayesianLLM system...") + llm = BayesianLLM() + + patient_story = """ + I am Lydia and I'm not feeling well. I feel so somber and tired. + """ + + # Store patient story + llm.patient_story = patient_story + + # Setup network + llm.setup_medical_network(patient_story) + + # Extract evidence from story + evidence = llm.extract_evidence(patient_story) + logger.info(f"Extracted Evidence: {evidence}") + + # Generate detailed diagnostic reasoning + diagnosis = llm.generate_diagnostic_reasoning(evidence) + logger.info(f"Generated diagnosis: {diagnosis}") + + # Log the diagnostic process + llm.log_diagnostic_process(evidence, diagnosis) + + print("\nšŸ“Š Diagnostic Analysis:") + print( + f"Primary Conclusion: {diagnosis.conclusion} (Confidence: {diagnosis.confidence*100:.1f}%)" + ) + print("\nReasoning Path:") + for step in diagnosis.evidence_path: + print(f"- {step}") + + print("\nAlternative Explanations:") + for alt, conf in diagnosis.alternative_explanations: + print(f"- {alt} ({conf*100:.1f}% confidence)") + + # Generate detailed explanation + print("\nšŸ“ Detailed Medical Explanation:") + explanation = llm.explain_decision_path(diagnosis) + print(explanation) + + print(f"\nāœ… Diagnostic process has been logged to: {llm.log_file}") + + # Verify the log file + llm.verify_log_file() + + except Exception as e: + logger.error(f"Error in main: {e}", exc_info=True) + raise if __name__ == "__main__": + # Set up logging + logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + ) main() From 2d439ed67cdc9aab95fb30c2919252d386dc53a8 Mon Sep 17 00:00:00 2001 From: Leon van Bokhorst Date: Fri, 22 Nov 2024 15:06:36 +0100 Subject: [PATCH 18/18] refactor: Update BayesianLLM class to use actual patient story for extracting relationships --- src/20_bayes_medical_explanability.py | 166 +++++++++++++++++++++++++- 1 file changed, 162 insertions(+), 4 deletions(-) diff --git a/src/20_bayes_medical_explanability.py b/src/20_bayes_medical_explanability.py index c473f6e..6d023c5 100644 --- a/src/20_bayes_medical_explanability.py +++ b/src/20_bayes_medical_explanability.py @@ -1,3 +1,86 @@ +""" +Medical Bayesian Networks and Large Language Models: A Historical Perspective + +Historical Significance: +------------------------ +Bayesian networks in medicine trace back to the 1980s with systems like MYCIN and +INTERNIST-1. These early expert systems demonstrated both the potential and limitations +of rule-based medical reasoning: + +1. Early Systems (1970s-1980s): + - MYCIN: Used certainty factors for bacterial infections + - INTERNIST-1: Attempted comprehensive internal medicine diagnosis + - Key limitation: Rigid, rule-based reasoning + +2. Bayesian Revolution (1990s): + - Introduction of probabilistic graphical models + - QMR-DT: First major Bayesian medical diagnosis system + - Enabled handling of uncertainty and incomplete information + +3. Modern Integration (2020s): + - Combination of Bayesian networks with LLMs + - Natural language understanding meets probabilistic reasoning + - Explainable AI becomes crucial for medical applications + +Key Innovations in This Implementation: +------------------------------------- +1. Hybrid Architecture: + - Bayesian networks provide probabilistic reasoning + - LLMs enable natural language understanding + - Combines structured and unstructured data processing + +2. Explainability: + - Every decision has a traceable reasoning path + - Natural language explanations for medical professionals + - Audit trail for accountability + +3. Medical Knowledge Integration: + - Dynamic knowledge structure creation + - Causal relationship extraction + - Evidence-based reasoning paths + +Technical Components: +------------------- +1. Bayesian Network: + - Nodes: Medical conditions/symptoms + - Edges: Causal relationships + - CPTs: Conditional probabilities + +2. LLM Integration: + - Structure learning from text + - Evidence extraction + - Natural language generation + +3. Logging System: + - Diagnostic process tracking + - Decision auditing + - Quality control + +This system represents a step toward more interpretable and reliable medical AI, +addressing key challenges in healthcare automation: +- Uncertainty handling +- Decision transparency +- Knowledge integration +- Clinical workflow integration + +Usage Example: +------------- +patient_story = ''' +I am experiencing severe fatigue, especially in the mornings, +along with persistent headaches and occasional dizziness. +''' + +system = BayesianLLM() +system.setup_medical_network(patient_story) +diagnosis = system.generate_diagnostic_reasoning(evidence) + +The system will: +1. Extract relevant medical concepts +2. Build a Bayesian network structure +3. Generate probabilistic diagnoses +4. Provide natural language explanations +""" + import json import logging from typing import Dict, List, Tuple, Optional, NamedTuple @@ -28,6 +111,20 @@ class DiagnosticReasoning: class BayesianLLM: + """ + A Bayesian network-based medical diagnosis system that uses LLMs for: + 1. Network structure learning + 2. Evidence extraction + 3. Diagnostic reasoning + 4. Natural language explanations + + Key Components: + - LLM Integration: Uses Ollama for natural language understanding + - Bayesian Network: Captures causal relationships between medical concepts + - Logging System: Tracks diagnostic processes for accountability + - Explanation Generation: Provides human-readable reasoning paths + """ + def __init__(self, model_name: str = MODEL_NAME): """Initialize the BayesianLLM system""" self.model_name = model_name @@ -63,7 +160,26 @@ def _initialize_log_file(self): def log_diagnostic_process( self, evidence: Dict[str, str], diagnosis: DiagnosticReasoning ) -> None: - """Log the diagnostic process to CSV""" + """ + Creates an audit trail of diagnostic decisions. + + Purpose: + 1. Accountability: Track decision-making process + 2. Learning: Analyze patterns in successful diagnoses + 3. Quality Control: Monitor system performance + + Stores: + - Timestamp: When diagnosis was made + - Patient Story: Original description + - Evidence: What was observed + - Reasoning: How conclusions were reached + - Network State: System configuration + + This is crucial for: + - Medical documentation + - System improvement + - Potential legal requirements + """ try: # Convert network structure to string representation network_structure = ( @@ -113,7 +229,20 @@ def log_diagnostic_process( raise def create_node(self, description: str) -> Tuple[str, List[str]]: - """Create a node with states based on LLM description""" + """ + Converts natural language descriptions into Bayesian network nodes. + + The Process: + 1. Takes a medical concept description (e.g., "patient's fatigue level") + 2. Uses LLM to generate: + - A standardized node name (snake_case) + - 5 possible states for that node + 3. Returns structured format for network building + + Example: + Input: "patient's fatigue level" + Output: ("fatigue_level", ["none", "mild", "moderate", "severe", "extreme"]) + """ prompt = ChatPromptTemplate.from_messages( [ ( @@ -175,7 +304,21 @@ def create_node(self, description: str) -> Tuple[str, List[str]]: raise def extract_relationships(self, text: str) -> List[Tuple[str, str]]: - """Extract causal relationships between nodes""" + """ + Identifies causal relationships between medical concepts. + + The Process: + 1. Analyzes patient story for cause-effect relationships + 2. Maps relationships to existing network nodes + 3. Validates relationships against known nodes + + Example: + "Fatigue is causing decreased activity" -> + [("fatigue_level", "activity_level")] + + This forms the structure of our Bayesian network, showing how + different medical conditions influence each other. + """ prompt = ChatPromptTemplate.from_messages( [ ( @@ -412,7 +555,22 @@ def generate_explanation(self, evidence: Dict[str, str]) -> str: def generate_diagnostic_reasoning( self, evidence: Dict[str, str] ) -> DiagnosticReasoning: - """Generate detailed diagnostic reasoning with evidence paths and confidence levels""" + """ + Produces structured diagnostic analysis using LLM reasoning. + + The Process: + 1. Takes observed evidence (symptoms, test results, etc.) + 2. Uses network structure to understand relationships + 3. Generates: + - Primary diagnosis with confidence + - Step-by-step reasoning path + - Alternative explanations with probabilities + + This mimics medical differential diagnosis where doctors: + - Consider multiple possibilities + - Weigh evidence strength + - Rule out alternatives systematically + """ prompt = ChatPromptTemplate.from_messages( [ (