### TensorBoard Setup and Logging

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

This snippet shows how to set up TensorBoard and log data. Ensure TensorBoard is installed and the log directory is correctly specified.

```python
from tensorboardX import SummaryWriter
writer = SummaryWriter("runs/pokebnn-demo")

```

--------------------------------

### TensorBoard Logging Setup

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Configures and initializes TensorBoard for logging training metrics. Ensure TensorBoard is installed and accessible.

```python
3b/V4GBl5mSAx8plW3+V/Q27kL2yntTEhu2rd5f7ykBICA+3+sNO8Fa8gg3bVu8v95SI+gTKtvFTsKNiW5WvbKe1A
```

```python
MgI+f1ypn71zzSg3bVu8v95SI+fMq2+VM/ob9K57YT7mg
```

```python
AAR+7at3l/vKQ3bVu8v8AeUgFG6nL8Me8oSAx+jzKtq5WFGx/HHvbCe2Ehu2rd5f7ykArnsJvwyJ/iGxIDH63Mq24m8aNh+ORPbCeMNiQ3bVu8v8AeUgJAR8TrzUPiMfUoN21bvL/AHlIj4kyrb8VD+htuRn2wn3FAMgFnWOtM3wZzyTFPdtW7y/3lItKvNq29M3Gi4fi7ntlPamAlovsZn92n6hVEVGm1bczX9C/mJ9sp9wVd21bvL/eUgD3X+J4HI8tkSAx96ZVt/ov9DbdySPbKe3ZEhu2rd5f7ykBICPoPWtr4znlqDdtW7y/3lIj6FMq29bWFGx6Zz2ynt1AMgEfVOr03wwvNuBu2rd5f7ykWFUmVbX07Gje2y9sJ7msBPgI/dtW7y/3lIbtq3eX+8pAKX7Jqfhn/tNiQEBTJlW3TU8KN7b4wnuTYv8AdtW7y/3lIC4ndQT++Z84kXAiZUupqS2lyk5EG+ySlboSeUtYnbh2RL
```

--------------------------------

### Install AQT Library

Source: https://github.com/google/aqt/blob/main/aqt/jax/v2/examples/examples.ipynb

Installs the AQT library using pip. This is a prerequisite for using the library.

```python
# install the AQT library
!pip install aqtp
```

--------------------------------

### TensorBoard Initialization and Basic Logging

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Initializes a TensorBoard writer and logs a single scalar value. This is a minimal example to show TensorBoard setup.

```python
from torch.utils.tensorboard import SummaryWriter

writer = SummaryWriter('runs/poke_bnn_demo')

# Log a scalar value
writer.add_scalar('Test/Scalar', 0.5, 1)

writer.close()
```

--------------------------------

### Data Processing and Visualization Setup

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Imports necessary libraries for data manipulation and visualization. This setup is crucial for preparing and displaying training metrics.

```python
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure

from IPython.display import display
from IPython.display import Image

from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.svm import SVC

from sklearn.decomposition import PCA
from sklearn.manifold import TSNE

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F

from torch.utils.data import Dataset, DataLoader

from torch.utils.tensorboard import SummaryWriter
from tensorboard.backend.event_processing import event_accumulator

from poke_bnn.data import PokeBNNDataset
from poke_bnn.model import PokeBNN
from poke_bnn.train import Trainer
from poke_bnn.utils import plot_confusion_matrix, plot_roc_curve, plot_pr_curve, plot_training_curves
```

--------------------------------

### TensorBoard Visualization Setup

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Sets up TensorBoard logging for visualizing training metrics. Requires the `tensorboard` and `tensorflow` libraries.

```python
from torch.utils.tensorboard import SummaryWriter

# Create a TensorBoard writer object
writer = SummaryWriter('runs/poke_bnn_demo')
```

--------------------------------

### Example of TensorBoard Usage in Training Loop

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

A complete example demonstrating how to integrate TensorBoard logging within a typical PyTorch training loop. It includes logging scalars, graphs, and histograms.

```python
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter

# Define a simple model
class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, 1)
        self.relu = nn.ReLU()
        self.fc1 = nn.Linear(16 * 26 * 26, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        return x

model = SimpleModel()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()

# Initialize TensorBoard writer
writer = SummaryWriter('runs/poke_bnn_demo')

# Dummy input and target
input_tensor = torch.randn(1, 1, 28, 28)
target_tensor = torch.randint(0, 10, (1,))

# Log the model graph
writer.add_graph(model, input_to_model=input_tensor)

# Dummy training loop
num_epochs = 5
for epoch in range(num_epochs):
    # Simulate training step
    optimizer.zero_grad()
    outputs = model(input_tensor)
    loss = criterion(outputs, target_tensor)
    loss.backward()
    optimizer.step()

    # Log metrics
    accuracy = (torch.argmax(outputs, dim=1) == target_tensor).float().mean()
    writer.add_scalar('Loss/train', loss.item(), epoch)
    writer.add_scalar('Accuracy/train', accuracy.item(), epoch)

    # Log histograms for weights and biases
    writer.add_histogram('Weights/conv1', model.conv1.weight, epoch)
    writer.add_histogram('Biases/conv1', model.conv1.bias, epoch)

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}, Accuracy: {accuracy.item():.4f}')

# Close the writer
writer.close()
print('Training finished and TensorBoard logs saved to runs/poke_bnn_demo')

```

--------------------------------

### Example: Add Audio to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs audio data to TensorBoard. Useful for tasks involving audio generation or analysis.

```python
writer.add_audio('generated_audio', audio_tensor, epoch, sample_rate=16000)
```

--------------------------------

### Example Data Generation (Placeholder)

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

This snippet represents placeholder code for generating example data or metrics that would typically be logged to TensorBoard. It is not functional code.

```python
train_loss = 0.1
train_acc = 0.95
epoch = 1
sample_input = torch.rand(1, 3, 32, 32)
images = torch.rand(4, 3, 32, 32)
hparams_text = "Learning Rate: 0.001, Optimizer: Adam"
```

--------------------------------

### Example: Add Video to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs video data to TensorBoard. Applicable for tasks involving video generation or processing.

```python
writer.add_video('generated_video', video_tensor, epoch, fps=4)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object, Run Name, Metric Components, Weights, Tag and Global Step (Redundant)

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

This example demonstrates adding a complete custom hyperparameter summary with redundant parameters. It's recommended to use parameters consistently.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_learning_rate_variant', 0.0005)], {'metrics/custom_accuracy_w': 0.72, 'metrics/custom_loss_w': 0.28}, run_name='custom_lr00005_variant_weighted_tag', global_step=step, global_step=step)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object, Metric Components, Weights, Tag and Global Step (Redundant)

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

This example demonstrates adding a custom hyperparameter summary with redundant parameters. It's recommended to use parameters consistently.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_feature_set', 'B')], {'metrics/custom_precision_w': 0.88, 'metrics/custom_recall_w': 0.82}, run_name='custom_feature_set_B_weighted_tag', global_step=step, global_step=step)
```

--------------------------------

### Example: Add Hyperparameter Summary with Metric Components

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a hyperparameter summary where metrics are broken down into components. Allows for more granular analysis of performance.

```python
writer.add_hparams({'kernel': 'rbf', 'gamma': 'scale'}, {'metrics/accuracy': 0.92, 'metrics/precision': 0.91, 'metrics/recall': 0.93})
```

--------------------------------

### Initialize TensorBoard Logger

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Initializes the TensorBoard logger for tracking training metrics. Ensure TensorBoard is installed and configured.

```python
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter()
```

--------------------------------

### Complex Data Generation and Plotting Example

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

A more elaborate example of generating and plotting data, potentially for more complex training scenarios.

```python
import numpy as np
import matplotlib.pyplot as plt

def generate_complex_data(num_points=100):
    x = np.linspace(0, 10, num_points)
    y1 = np.sin(x) * np.exp(-x/5) + np.random.normal(0, 0.1, num_points)
    y2 = np.cos(x) * np.exp(-x/5) + np.random.normal(0, 0.1, num_points)
    return x, y1, y2

def plot_complex_curves(x, y1, y2):
    plt.figure(figsize=(12, 7))
    plt.plot(x, y1, label='Decaying Sine', color='blue', linestyle='--')
    plt.plot(x, y2, label='Decaying Cosine', color='red', linestyle='-.')
    plt.xlabel('Time')
    plt.ylabel('Amplitude')
    plt.title('Complex Decaying Curves')
    plt.legend()
    plt.grid(True)
    plt.show()

x_complex, y1_complex, y2_complex = generate_complex_data()
plot_complex_curves(x_complex, y1_complex, y2_complex)
```

--------------------------------

### Example: Add Hyperparameter Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a summary of hyperparameters to TensorBoard. Useful for comparing different training runs with varying configurations.

```python
writer.add_hparams({'lr': 0.01, 'batch_size': 32}, {'accuracy': 0.9, 'loss': 0.1})
```

--------------------------------

### Example: Add Custom Audio Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom audio data to TensorBoard. Useful for tasks involving custom audio generation or analysis.

```python
writer.add_audio('Custom/Signal', custom_audio_tensor, global_step=current_step, sample_rate=44100)
```

--------------------------------

### Example of Data Generation and Plotting

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Combines data generation and plotting into a single executable block for demonstration purposes.

```python
import numpy as np
import matplotlib.pyplot as plt

def generate_random_data(num_points=100):
    x = np.linspace(0, 10, num_points)
    y1 = np.sin(x) + np.random.normal(0, 0.2, num_points)
    y2 = np.cos(x) + np.random.normal(0, 0.2, num_points)
    return x, y1, y2

def plot_curves(x, y1, y2):
    plt.figure(figsize=(10, 6))
    plt.plot(x, y1, label='Metric 1')
    plt.plot(x, y2, label='Metric 2')
    plt.xlabel('Epoch')
    plt.ylabel('Value')
    plt.title('Training Curves')
    plt.legend()
    plt.grid(True)
    plt.show()

x, y1, y2 = generate_random_data()
plot_curves(x, y1, y2)
```

--------------------------------

### Example: Add Custom Video Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom video data to TensorBoard. Applicable for tasks involving custom video generation or processing.

```python
writer.add_video('Custom/Reconstruction', custom_video_tensor, global_step=current_step, fps=10)
```

--------------------------------

### Example: Add Image to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs an image to TensorBoard. Useful for visualizing sample inputs, outputs, or generated images during training.

```python
writer.add_image('sample_image', img_tensor, global_step=epoch)
```

--------------------------------

### Example: Add Custom Video Summary with Dataformats, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom video with specified data format, tag, and global step. Useful for videos with different channel orders at different experiment stages.

```python
writer.add_video('Custom/Tag/Video_CHW', custom_video_chw_tensor, global_step=step, fps=15, dataformats='CHW')
```

--------------------------------

### Example: Add Custom Mesh Summary with Textures

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom mesh with texture coordinates and textures to TensorBoard. Enables visualization of 3D objects with detailed surface appearance.

```python
writer.add_mesh('Custom/Textured_Object', vertices=custom_verts, faces=custom_faces, textures=custom_textures, global_step=step)
```

--------------------------------

### Example: Add Custom Audio Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom audio data with a specific tag and global step. Useful for tasks involving custom audio generation or analysis at different experiment stages.

```python
writer.add_audio('Custom/Tag/Signal', custom_audio_tensor, global_step=step, sample_rate=44100)
```

--------------------------------

### Example: Add Custom Image Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom image with a specific tag and global step. Useful for visualizing custom outputs or states at different stages of an experiment.

```python
writer.add_image('Custom/Tag/Image', custom_image_tensor, global_step=step)
```

--------------------------------

### Example: Add Custom Video Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom video data with a specific tag and global step. Applicable for tasks involving custom video generation or processing at different experiment stages.

```python
writer.add_video('Custom/Tag/Reconstruction', custom_video_tensor, global_step=step, fps=10)
```

--------------------------------

### Example: Add Custom Mesh Summary with Colors

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom mesh with vertex colors to TensorBoard. Allows for visualizing 3D objects with color information.

```python
writer.add_mesh('Custom/Colored_Object', vertices=custom_verts, faces=custom_faces, colors=custom_colors, global_step=step)
```

--------------------------------

### Example: Add Histogram to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs histogram data for weights or activations to TensorBoard. Helps in analyzing the distribution of model parameters.

```python
writer.add_histogram('conv1.weight', model.conv1.weight, epoch)
```

--------------------------------

### Example: Add Scalar with Walltime

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a scalar value with explicit wall time. Useful for precise timing analysis.

```python
writer.add_scalar('Custom/Metric', value, global_step=step, walltime=timestamp)
```

--------------------------------

### Example: Add Custom Video Summary with Dataformats

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom video summary to TensorBoard specifying the data format. Useful for videos with different channel orders.

```python
writer.add_video('Custom/Video_CHW', custom_video_chw_tensor, global_step=step, fps=15, dataformats='CHW')
```

--------------------------------

### Example: Add Custom Image Summary with Dataformats, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom image with specified data format, tag, and global step. Useful for images with different channel orders at different experiment stages.

```python
writer.add_image('Custom/Tag/Image_CHW', custom_image_chw_tensor, global_step=step, dataformats='CHW')
```

--------------------------------

### Example: Add Mesh to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs 3D mesh data to TensorBoard. Useful for visualizing 3D models or reconstructions.

```python
writer.add_mesh('object_mesh', vertices=verts, faces=faces, global_step=epoch)
```

--------------------------------

### Example: Add Custom Mesh Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom 3D mesh data to TensorBoard. Useful for visualizing custom 3D objects or scenes.

```python
writer.add_mesh('Custom/Object', vertices=custom_verts, faces=custom_faces, global_step=current_step)
```

--------------------------------

### Example: Add Custom Mesh Summary with Colors, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom mesh with vertex colors, tag, and global step. Allows for visualizing 3D objects with color information at different experiment stages.

```python
writer.add_mesh('Custom/Tag/Colored_Object', vertices=custom_verts, faces=custom_faces, colors=custom_colors, global_step=step)
```

--------------------------------

### Execute Cloud TPU Training Script

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/README.md

Run the provided bash script to automate the creation of a TPU VM and the training of PokeBNN models. Ensure the gcloud CLI tool is installed and configured.

```bash
source cloudtpu_train_pokebnn.sh
```

--------------------------------

### Example: Add Hyperparameter Summary with HParam object and Metric Components

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a hyperparameter summary using HParam objects where metrics are detailed components. Enables granular performance analysis.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('model_type', 'CNN'), HParam('num_layers', 3)], {'metrics/accuracy': 0.95, 'metrics/f1_score': 0.94})
```

--------------------------------

### Example: Add Custom Mesh Summary with Textures, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom mesh with texture coordinates, textures, tag, and global step. Enables visualization of 3D objects with detailed surface appearance at different experiment stages.

```python
writer.add_mesh('Custom/Tag/Textured_Object', vertices=custom_verts, faces=custom_faces, textures=custom_textures, global_step=step)
```

--------------------------------

### Example: Add Custom Mesh Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom 3D mesh data with a specific tag and global step. Useful for visualizing custom 3D objects or scenes at different experiment stages.

```python
writer.add_mesh('Custom/Tag/Object', vertices=custom_verts, faces=custom_faces, global_step=step)
```

--------------------------------

### Example: Add Custom Histogram Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom histogram summary to TensorBoard. Helps in analyzing the distribution of custom data or model parameters.

```python
writer.add_histogram('Custom/Weights', custom_weights_tensor, global_step=current_step)
```

--------------------------------

### Example: Add Custom Scalar Summary with Walltime

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom scalar value with explicit wall time. Useful for precise timing analysis of custom metrics.

```python
writer.add_scalar('Custom/Timing', custom_time_value, global_step=step, walltime=custom_timestamp)
```

--------------------------------

### Example: Add Custom Embedding Projector Data with Weights

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom embedding data with associated weights for visualization in the TensorBoard embedding projector. Useful for understanding the importance of different features.

```python
writer.add_embedding(custom_features, metadata=custom_metadata, weight=custom_weights, global_step=step)
```

--------------------------------

### Example: Add Scalar with Walltime and Display Name

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Combines explicit wall time and a custom display name for scalar logging. Provides detailed control over metric tracking.

```python
writer.add_scalar('Performance', latency, step, walltime=time.time(), display_name='Inference Latency')
```

--------------------------------

### Example: Add Hyperparameter Summary with Run Name

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a hyperparameter summary with a specific run name. Helps in organizing and identifying different experiments in TensorBoard.

```python
writer.add_hparams({'lr': 0.001, 'optimizer': 'Adam'}, {'accuracy': 0.85, 'loss': 0.15}, run_name='exp_adam_lr0001')
```

--------------------------------

### Example: Add Custom Text Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom text data with a specific tag and global step. Can be used for logging experiment notes or debugging information at different stages.

```python
writer.add_text('Custom/Tag/Notes', custom_text_log, global_step=step)
```

--------------------------------

### Example: Add Embedding Projector Data

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs embedding data for visualization in the TensorBoard embedding projector. Requires embedding vectors and optionally labels/metadata.

```python
writer.add_embedding(features, metadata=labels, label_img=images, global_step=epoch)
```

--------------------------------

### Example: Add Hyperparameter Summary with HParam object and Run Name

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a hyperparameter summary using HParam objects and a specific run name for better organization and identification of experiments.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('dropout', 0.5), HParam('activation', 'relu')], {'accuracy': 0.75, 'loss': 0.25}, run_name='exp_dropout05_relu')
```

--------------------------------

### Example of Logging Training Curve Data

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

This snippet demonstrates how to log training and validation loss and accuracy over epochs using TensorBoard's SummaryWriter. It requires a PyTorch model and a SummaryWriter instance.

```python
from torch.utils.tensorboard import SummaryWriter
import torch

# Assume model, train_loader, val_loader, criterion, optimizer are defined
writer = SummaryWriter()

for epoch in range(num_epochs):
    # Training loop
    model.train()
    running_loss = 0.0
    for i, data in enumerate(train_loader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()

    avg_train_loss = running_loss / len(train_loader)
    writer.add_scalar('Loss/train', avg_train_loss, epoch)

    # Validation loop
    model.eval()
    val_loss = 0.0
    correct = 0
    total = 0
    with torch.no_grad():
        for data in val_loader:
            inputs, labels = data
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    avg_val_loss = val_loss / len(val_loader)
    val_accuracy = 100 * correct / total
    writer.add_scalar('Loss/val', avg_val_loss, epoch)
    writer.add_scalar('Accuracy/val', val_accuracy, epoch)

writer.close()
```

--------------------------------

### Example: Add Custom Image Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom image summary to TensorBoard. Useful for visualizing specific aspects of the model's output or internal states.

```python
writer.add_image('Custom/Output', custom_image_tensor, global_step=current_step)
```

--------------------------------

### Example: Add Custom Embedding Projector Data

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom embedding data for visualization in the TensorBoard embedding projector. Allows for visualizing custom feature representations.

```python
writer.add_embedding(custom_features, metadata=custom_metadata, label_img=custom_images, global_step=current_step)
```

--------------------------------

### Example: Add Custom Scalar Summary with Walltime and Display Name

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Combines explicit wall time and a custom display name for custom scalar logging. Provides detailed control over custom metric tracking.

```python
writer.add_scalar('Custom Performance', custom_latency, step, walltime=time.time(), display_name='Custom Inference Latency')
```

--------------------------------

### Example: Add Hyperparameter Summary with HParam object, Run Name, and Metric Components

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a comprehensive hyperparameter summary using HParam objects, including a run name and detailed metric components for advanced experiment tracking.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('learning_rate', 0.005), HParam('batch_size', 128)], {'metrics/accuracy': 0.89, 'metrics/precision': 0.88, 'metrics/recall': 0.90}, run_name='exp_lr0005_bs128')
```

--------------------------------

### Example: Add Custom HParams Summary with Metric Components

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary with detailed metric components. Allows for granular analysis of custom experiment performance.

```python
writer.add_hparams({'feature_set': 'A'}, {'metrics/custom_accuracy': 0.8, 'metrics/custom_error': 0.2})
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object, Metric Components, and Weights

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary using HParam objects with detailed metric components and weights. Enables granular, weighted performance analysis of custom experiments.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_feature_set', 'B')], {'metrics/custom_precision_w': 0.88, 'metrics/custom_recall_w': 0.82})
```

--------------------------------

### Example: Add Custom Image Summary with Dataformats

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom image summary to TensorBoard specifying the data format. Useful for images with different channel orders (e.g., CHW vs HWC).

```python
writer.add_image('Custom/Image_CHW', custom_image_chw_tensor, global_step=step, dataformats='CHW')
```

--------------------------------

### Example: Add Custom Histogram Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom histogram with a specific tag and global step. Helps in analyzing the distribution of custom data or parameters at different experiment stages.

```python
writer.add_histogram('Custom/Tag/Weights', custom_weights_tensor, global_step=step)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object and Weights

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary using HParam objects, including weights for metrics. Allows for weighted performance evaluation.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_weight_param', 0.8)], {'metrics/weighted_accuracy': 0.9, 'metrics/weighted_loss': 0.1})
```

--------------------------------

### Example: Add Custom Scalar Summary with Walltime, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom scalar value with explicit wall time, tag, and global step. Useful for precise timing analysis of custom metrics at specific experiment stages.

```python
writer.add_scalar('Custom/Tag/Timing', custom_time_value, global_step=step, walltime=custom_timestamp)
```

--------------------------------

### Example: Add Custom HParams Summary with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary with a specific tag and global step. Useful for tracking custom configurations and their performance at different experiment stages.

```python
writer.add_hparams({'custom_param': 'value'}, {'custom_metric': 0.5}, run_name='custom_run_tag', global_step=step)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object, Run Name, and Weights

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a comprehensive custom hyperparameter summary using HParam objects, including a run name and weights for metrics, for advanced custom experiment tracking.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_config_variant', 'B')], {'metrics/custom_score_weighted': 0.75}, run_name='custom_variant_B')
```

--------------------------------

### Example: Add Custom Embedding Projector Data with Label Images

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom embedding data with corresponding label images for visualization in the TensorBoard embedding projector. Helps in visually associating embeddings with their labels.

```python
writer.add_embedding(custom_features, metadata=custom_metadata, label_img=custom_label_images, global_step=step)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object, Weights, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary using HParam objects, including weights, tag, and global step for weighted performance evaluation of custom experiments.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_weight_param', 0.8)], {'metrics/weighted_accuracy': 0.9, 'metrics/weighted_loss': 0.1}, run_name='custom_weighted_tag', global_step=step)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object and Metric Components

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary using HParam objects with detailed metric components. Enables granular performance analysis of custom experiments.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_dataset', 'set1')], {'metrics/custom_precision': 0.85, 'metrics/custom_recall': 0.80})
```

--------------------------------

### Initialize TensorBoard Writer

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Sets up the TensorBoard writer to log training metrics. Ensure the log directory is correctly specified.

```python
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(log_dir="runs/pokebnn_demo")
```

--------------------------------

### Example: Add Custom Scalar Summary with Display Name, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom scalar value with a custom display name, tag, and global step. Allows for more descriptive labels for custom metrics at specific experiment stages.

```python
writer.add_scalar('Custom Metrics', custom_accuracy, epoch, display_name='Custom Training Accuracy', tag='Custom/Tag/Accuracy')
```

--------------------------------

### Low-level Tensor Quantization with `Quantizer`

Source: https://context7.com/google/aqt/llms.txt

Demonstrates creating and using a `Quantizer` for low-level tensor quantization, including calibration and fake quantization. Requires AQT numerics and calibration modules.

```python
import jax
import jax.numpy as jnp
from aqt.jax.v2 import aqt_quantizer, calibration, utils as aqt_utils
from aqt.jax.v2.numerics import int_numerics

# Build a 4-bit symmetric quantizer with AbsMax calibration
q = aqt_quantizer.Quantizer(
    numerics=int_numerics.IntSymmetric(
        bits=4,
        preserve_zero=True,
        preserve_max_val=True,
        clip=True,
        clip_gradient=True,
        round=True,
        noise_fn=None,
    ),
    calib_shared_axes=-1,   # share scale across last axis
    scale_stop_grad=True,
    calibration=calibration.AbsMaxCalibration,
    context=aqt_utils.Context(key=jax.random.PRNGKey(0), train_step=0),
)
q.init_calibration()  # must call before quant()

x = jnp.linspace(-10.0, 10.0, 10)
x_q, grad_fn = q.quant(x, calibration_axes=-1)

print("quantized ints:", x_q.qvalue)
# e.g., [-7, -6, -4, -3, -1,  0,  2,  3,  5,  6]  (4-bit range ±7)

print("scale:", x_q.scale)
# e.g., [1.428...]  (10.0 / 7.0)

print("dequantized:", x_q.dequant())
# e.g., [-9.999..., -8.571..., ...,  9.999...]  (close to original)

# Fake-quant helper: quantize then immediately dequantize
fake_quant = aqt_quantizer.make_fake_quant(q, calibration_axes=-1)
print("fake quant:", fake_quant(x))

# Convenience factory (8-bit, AbsMax calibration)
q8 = aqt_quantizer.quantizer_make(n_bits=8)
x_q8, _ = q8.quant(x, calibration_axes=-1)
print("8-bit qvalue:", x_q8.qvalue)
```

--------------------------------

### TensorBoard Integration for PokeBNN

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Code snippet demonstrating how to log training metrics to TensorBoard for visualization. Ensure TensorFlow is installed.

```python
from tensorboard.summary import SummaryWriter

log_dir = "logs/pokebnn_demo"
writer = SummaryWriter(log_dir)

# Simulate training loop and log metrics
for step in range(n_steps):
    # In a real scenario, these would be actual training metrics
    current_loss = np.mean(losses[:, step])
    current_accuracy = np.mean(accuracies[:, step])

    writer.add_scalar('Loss/train', current_loss, step)
    writer.add_scalar('Accuracy/train', current_accuracy, step)

writer.close()
print(f"TensorBoard logs written to: {log_dir}")
```

--------------------------------

### Initializing PokeBNN Model and Trainer

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Initializes the PokeBNN model, defines the optimizer and loss function, and sets up the Trainer. This prepares the model for the training process.

```python
model = PokeBNN(input_size=X_train.shape[1], hidden_size=128, num_classes=len(dataset.classes))
optimizer = optim.Adam(model.parameters(), lr=0.001)
loss_fn = nn.CrossEntropyLoss()

trainer = Trainer(model, optimizer, loss_fn, device='cpu')
writer = SummaryWriter('runs/pokebnn_demo')
```

--------------------------------

### Example: Add Figure to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a Matplotlib figure to TensorBoard. Useful for visualizing plots or complex data representations.

```python
writer.add_figure('training_curve', fig, epoch)
```

--------------------------------

### Example: Add Custom HParams Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary to TensorBoard. Useful for tracking custom configurations and their performance.

```python
writer.add_hparams({'custom_param': 'value'}, {'custom_metric': 0.5})
```

--------------------------------

### Example: Add PR Curve to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs Precision-Recall curve data to TensorBoard. Useful for evaluating binary classification models.

```python
writer.add_pr_curve('pr_curve_example', predictions, labels, epoch)
```

--------------------------------

### Example: Add Text to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs text data to TensorBoard. Can be used for logging hyperparameters, model summaries, or other textual information.

```python
writer.add_text('model_hyperparameters', hparams_str, epoch)
```

--------------------------------

### Example: Add Custom Figure Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom Matplotlib figure to TensorBoard. Useful for creating and visualizing custom plots or diagrams.

```python
writer.add_figure('Custom/Plot', custom_figure, global_step=current_step)
```

--------------------------------

### Example: Add Hyperparameter Summary with HParam object

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a hyperparameter summary using the HParam object for more structured definition of hyperparameters and metrics.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('learning_rate', 0.01), HParam('batch_size', 64)], {'accuracy': 0.88, 'loss': 0.12})
```

--------------------------------

### Example: Add Custom Scalar Summary with Walltime, Display Name, Tag, Global Step and Walltime

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Combines explicit wall time, custom display name, tag, and global step for custom scalar logging. Provides detailed control over custom metric tracking at specific experiment stages.

```python
writer.add_scalar('Custom Performance', custom_latency, step, walltime=time.time(), display_name='Custom Inference Latency', tag='Custom/Tag/Latency', walltime=custom_timestamp)
```

--------------------------------

### Example: Add Scalar with Display Name

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a scalar value with a custom display name in TensorBoard. Allows for more descriptive labels in the UI.

```python
writer.add_scalar('Metrics', accuracy, epoch, display_name='Training Accuracy')
```

--------------------------------

### Import Necessary Libraries

Source: https://github.com/google/aqt/blob/main/aqt/jax/v2/examples/examples.ipynb

Imports the required modules from AQT, Flax, and JAX for building and quantizing models.

```python
# necessary imports
import aqt.jax.v2.flax.aqt_flax as aqt
import aqt.jax.v2.config as aqt_config
import flax.linen as nn
```

--------------------------------

### Example: Add Custom Scalar Summary with Walltime, Display Name, Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Combines explicit wall time, custom display name, tag, and global step for custom scalar logging. Provides detailed control over custom metric tracking at specific experiment stages.

```python
writer.add_scalar('Custom Performance', custom_latency, step, walltime=time.time(), display_name='Custom Inference Latency', tag='Custom/Tag/Latency')
```

--------------------------------

### Create Fully Quantized Configuration (int8)

Source: https://github.com/google/aqt/blob/main/README.md

Creates an AQT configuration object that quantizes both the forward and backward passes of a model to 8-bit integers. This is a common setting for achieving good performance and accuracy.

```python
# create a config that quantizes both forward and backward passes to int8
int8_config = aqt_config.fully_quantized(fwd_bits=8, bwd_bits=8)
```

--------------------------------

### Example: Add Custom HParams Summary with Run Name

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary with a specific run name. Helps in organizing custom experiments.

```python
writer.add_hparams({'custom_setting': 10}, {'custom_result': 0.7}, run_name='custom_run_10')
```

--------------------------------

### Example: Add Custom ROC Curve Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom ROC curve to TensorBoard. Useful for evaluating custom classification models or specific scenarios.

```python
writer.add_roc_curve('Custom/ROC_Curve', custom_predictions, custom_labels, global_step=current_step)
```

--------------------------------

### Example: Add Custom PR Curve Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom Precision-Recall curve to TensorBoard. Useful for evaluating custom classification models or specific scenarios.

```python
writer.add_pr_curve('Custom/PR_Curve', custom_predictions, custom_labels, global_step=current_step)
```

--------------------------------

### Example: Add Custom Text Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom text data to TensorBoard. Can be used for logging experiment notes, debugging information, or custom messages.

```python
writer.add_text('Custom/Notes', custom_text_log, global_step=current_step)
```

--------------------------------

### Example: Add Custom Scalar Summary

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom scalar summary to TensorBoard. This allows for logging any numerical value associated with a specific step or epoch.

```python
writer.add_scalar('Custom/Metric', custom_value, global_step=current_step)
```

--------------------------------

### Generate Random Matrix Data

Source: https://github.com/google/aqt/blob/main/README.md

Generates random matrices for activations and weights, used as input for matrix multiplication examples. Ensure `gen_matrix` is defined elsewhere.

```python
batch_size = 3
channels_in = 4
channels_out = 5
a = gen_matrix(batch_size, channels_in) # Activations
w = gen_matrix(channels_in, channels_out) # Weights
```

--------------------------------

### Loading and Preparing Dataset

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Loads the PokeBNN dataset and splits it into training and testing sets. Data is then scaled for optimal model performance.

```python
dataset = PokeBNNDataset(root='data', download=True)
X = dataset.X
y = dataset.y

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```

--------------------------------

### Example: Add Custom HParams Summary with HParam object

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs a custom hyperparameter summary using HParam objects for structured definition of custom parameters and metrics.

```python
from torch.utils.tensorboard.summary import HParam
writer.add_hparams([HParam('custom_config', 'X')], {'custom_score': 0.6})
```

--------------------------------

### Example: Add Custom Embedding Projector Data with Tag and Global Step

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs custom embedding data with a specific tag and global step for visualization in the TensorBoard embedding projector. Allows for visualizing custom feature representations at different experiment stages.

```python
writer.add_embedding(custom_features, metadata=custom_metadata, global_step=step, tag='Custom/Tag/Embeddings')
```

--------------------------------

### Example: Add ROC Curve to TensorBoard

Source: https://github.com/google/aqt/blob/main/papers/pokebnn/tensorboard.ipynb

Logs Receiver Operating Characteristic (ROC) curve data to TensorBoard. Another metric for evaluating binary classification models.

```python
writer.add_roc_curve('roc_curve_example', predictions, labels, epoch)
```