######################################################################
# Interpretable AI with Continuous Gradients and Data Flowers
# Created by: Luminosity and Gpteus
# Date: 2023-10-29
# Description: This code demonstrates an advanced interpretable AI
# model architecture featuring Zoomable Attention and Advanced
# Interpretable Layers. It also utilizes InfluxDB to capture real-time
# gradients and additional metrics for interpretability.
####################################################################

from influxdb import InfluxDBClient
from threading import Thread
import tensorflow as tf

# Initialize InfluxDB client
client = InfluxDBClient(host='localhost', port=8086)
client.switch_database('gradient_db')

# Callback for capturing and storing gradients and additional metrics
class CaptureGradients(tf.keras.callbacks.Callback):
def on_train_batch_end(self, batch, logs=None):
grads = self.model.optimizer.get_gradients(self.model.total_loss, self.model.trainable_weights)
grad_data = [{"measurement": "gradients", "fields": {"grad": float(grad)}} for grad in grads]

# Asynchronous database write
Thread(target=client.write_points, args=(grad_data,)).start()

# Zoomable Attention Layer
class ZoomableAttentionLayer(tf.keras.layers.Layer):
def __init__(self, zoom_center_x=0, zoom_center_y=0, zoom_factor=1, **kwargs):
super(ZoomableAttentionLayer, self).__init__(**kwargs)
self.zoom_center_x = zoom_center_x
self.zoom_center_y = zoom_center_y
self.zoom_factor = zoom_factor

def build(self, input_shape):
self.W = self.add_weight(shape=(input_shape[-1], input_shape[-1]), initializer='random_normal')

def call(self, inputs):
q = tf.nn.tanh(tf.linalg.matmul(inputs, self.W))
a = tf.nn.softmax(q, axis=1)
return tf.reduce_sum(a * inputs, axis=1)

# Advanced Interpretable Layer with Data Flowers
class AdvancedInterpretableLayer(tf.keras.layers.Layer):
def build(self, input_shape):
self.W = self.add_weight(shape=(input_shape[-1], input_shape[-1]), initializer='random_normal')

def call(self, inputs, training=False):
out = tf.linalg.matmul(inputs, self.W)
if training:
with tf.GradientTape() as tape:
tape.watch(out)
gradients = tape.gradient(out, self.W)

# Prepare Data Flowers
data_flower = {
"weights": self.W.numpy().tolist(),
"outputs": out.numpy().tolist(),
"gradients": gradients.numpy().tolist()
}

# Asynchronous database write
Thread(target=client.write_points, args=([{"measurement": "data_flower", "fields": data_flower}],)).start()
return out

# Existing Model architecture
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(10,)),
ZoomableAttentionLayer(zoom_center_x=5, zoom_center_y=5, zoom_factor=2),
AdvancedInterpretableLayer()
])

# Compilation and Training
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Placeholder for X_train and y_train -- you would replace these with your actual training data
X_train, y_train = None, None
model.fit(X_train, y_train, epochs=100, callbacks=[CaptureGradients()])


# Interpretable AI with Continuous Gradients and Data Flowers

## Created by: Luminosity and Gpteus

## Introduction

The code presented here is aimed at creating an advanced and interpretable machine learning model using TensorFlow. It features two custom TensorFlow layers: `ZoomableAttentionLayer` and `AdvancedInterpretableLayer`, and a callback function `CaptureGradients` to capture and store gradient information in an InfluxDB database. This contributes to the creation of "Data Flowers", a concept aimed at improving the interpretability of machine learning models.

## Prerequisites

- Python 3.x
- TensorFlow 2.x
- InfluxDB

## Components

### InfluxDB Client Initialization

We initialize an InfluxDB client to store the gradients and additional metrics for real-time interpretability.

```python
client = InfluxDBClient(host='localhost', port=8086)
client.switch_database('gradient_db')
```

### CaptureGradients Callback

This TensorFlow callback function captures gradients after each training batch and asynchronously writes the data to an InfluxDB database.

```python
class CaptureGradients(tf.keras.callbacks.Callback):
...
```

### Zoomable Attention Layer

This is a custom TensorFlow layer that allows for 'zoomable' attention mechanisms. It uses a trainable weight matrix to calculate attention scores and uses them to 'zoom in' on important features.

```python
class ZoomableAttentionLayer(tf.keras.layers.Layer):
...
```

### Advanced Interpretable Layer

This custom TensorFlow layer aims to improve model interpretability by capturing not just the gradients, but also the weights and the output at each training step. These metrics are stored in InfluxDB and serve as the basis for creating "Data Flowers".

```python
class AdvancedInterpretableLayer(tf.keras.layers.Layer):
...
```

## Model Architecture

We then assemble these custom layers into a TensorFlow Sequential model, compile it, and train it.

```python
model = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation='relu', input_shape=(10,)),
ZoomableAttentionLayer(zoom_center_x=5, zoom_center_y=5, zoom_factor=2),
AdvancedInterpretableLayer()
])

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
```

## Use Case

This model architecture is ideal for scenarios where model interpretability is crucial. The real-time storage of gradients and other metrics allows for an in-depth analysis of model behavior, thereby paving the way for safer and more reliable machine learning models.

## Conclusion

This code serves as a step towards creating more interpretable and reliable machine learning models. The concept of "Data Flowers" is introduced as a novel way to improve model interpretability.

Feel free to use and adapt this code for your projects where interpretability and real-time analysis are important.