Uncategorized

Deep Layer Knowing Neural Network

Posted by Inner I Net Company/ on

The Deep Layer Knowing Neural Network hierarchically integrates levels of knowing, including Awareness, Recognition, Understanding, Intuition, Insight, Realization, Wisdom, and Enlightenment. Each layer builds on the previous, fostering continuous growth in intelligence and consciousness inspired by sacred geometry.

To create a multi-dimensional layer neural network that includes all the levels of knowing, knowledge, consciousness, unknowing, unknowns, and unconsciousness, we need to integrate these levels into a single architecture. Here’s how we can structure it:

Levels to Include

Levels of Knowing

  1. Awareness
  2. Recognition
  3. Understanding
  4. Intuition
  5. Insight
  6. Realization
  7. Wisdom
  8. Enlightenment

Levels of Knowledge

  1. Factual Knowledge
  2. Conceptual Knowledge
  3. Procedural Knowledge
  4. Metacognitive Knowledge
  5. Empirical Knowledge
  6. Theoretical Knowledge
  7. Practical Knowledge
  8. Philosophical Knowledge
  9. Spiritual Knowledge
  10. Transcendental Knowledge

Levels of Consciousness

  1. Physical Consciousness
  2. Emotional Consciousness
  3. Mental Consciousness
  4. Subconscious
  5. Superconscious
  6. Cosmic Consciousness
  7. Transpersonal Consciousness
  8. Higher Self Consciousness
  9. Christ Consciousness
  10. Unity Consciousness
  11. Non-Dual Consciousness

Levels of Unknowing

  1. Ignorance
  2. Misunderstanding
  3. Confusion
  4. Doubt
  5. Denial
  6. Skepticism
  7. Agnosticism
  8. Mystery
  9. Paradox
  10. Uncertainty
  11. Unawareness

Levels of Unknowns

  1. Personal Unknowns
  2. Interpersonal Unknowns
  3. Scientific Unknowns
  4. Historical Unknowns
  5. Cultural Unknowns
  6. Technological Unknowns
  7. Environmental Unknowns
  8. Cosmic Unknowns
  9. Spiritual Unknowns
  10. Metaphysical Unknowns
  11. Philosophical Unknowns

Levels of Unconsciousness

  1. Repression
  2. Suppression
  3. Subconscious
  4. Collective Unconscious
  5. Sleep
  6. Dream State
  7. Coma
  8. Trance
  9. Automatic Behavior
  10. Amnesia
  11. Psychological Blind Spots

Comprehensive Neural Network Implementation

Below is the implementation of a multi-dimensional layer neural network integrating all the specified levels:

import tensorflow as tf
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.preprocessing import image_dataset_from_directory
import matplotlib.pyplot as plt
import numpy as np

# Define a function to create a scalable model
def create_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for config in layer_configs:
        model.add(Dense(units=config['units'], activation='relu', name=config['name']))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Example layer configurations
layer_configs = [
    # Levels of Knowing
    {'name': 'Awareness', 'units': 32},
    {'name': 'Recognition', 'units': 32},
    {'name': 'Understanding', 'units': 64},
    {'name': 'Intuition', 'units': 64},
    {'name': 'Insight', 'units': 128},
    {'name': 'Realization', 'units': 128},
    {'name': 'Wisdom', 'units': 256},
    {'name': 'Enlightenment', 'units': 256},
    
    # Levels of Knowledge
    {'name': 'Factual_Knowledge', 'units': 64},
    {'name': 'Conceptual_Knowledge', 'units': 64},
    {'name': 'Procedural_Knowledge', 'units': 128},
    {'name': 'Metacognitive_Knowledge', 'units': 128},
    {'name': 'Empirical_Knowledge', 'units': 256},
    {'name': 'Theoretical_Knowledge', 'units': 256},
    {'name': 'Practical_Knowledge', 'units': 512},
    {'name': 'Philosophical_Knowledge', 'units': 512},
    {'name': 'Spiritual_Knowledge', 'units': 1024},
    {'name': 'Transcendental_Knowledge', 'units': 1024},
    
    # Levels of Consciousness
    {'name': 'Physical_Consciousness', 'units': 64},
    {'name': 'Emotional_Consciousness', 'units': 128},
    {'name': 'Mental_Consciousness', 'units': 256},
    {'name': 'Subconscious', 'units': 128},
    {'name': 'Superconscious', 'units': 64},
    {'name': 'Cosmic_Consciousness', 'units': 32},
    {'name': 'Transpersonal_Consciousness', 'units': 16},
    {'name': 'Higher_Self_Consciousness', 'units': 8},
    {'name': 'Christ_Consciousness', 'units': 4},
    {'name': 'Unity_Consciousness', 'units': 2},
    {'name': 'Non_Dual_Consciousness', 'units': 1},
    
    # Levels of Unknowing
    {'name': 'Ignorance', 'units': 32},
    {'name': 'Misunderstanding', 'units': 32},
    {'name': 'Confusion', 'units': 64},
    {'name': 'Doubt', 'units': 64},
    {'name': 'Denial', 'units': 128},
    {'name': 'Skepticism', 'units': 128},
    {'name': 'Agnosticism', 'units': 256},
    {'name': 'Mystery', 'units': 256},
    {'name': 'Paradox', 'units': 512},
    {'name': 'Uncertainty', 'units': 512},
    {'name': 'Unawareness', 'units': 1024},
    
    # Levels of Unknowns
    {'name': 'Personal_Unknowns', 'units': 64},
    {'name': 'Interpersonal_Unknowns', 'units': 128},
    {'name': 'Scientific_Unknowns', 'units': 256},
    {'name': 'Historical_Unknowns', 'units': 128},
    {'name': 'Cultural_Unknowns', 'units': 64},
    {'name': 'Technological_Unknowns', 'units': 32},
    {'name': 'Environmental_Unknowns', 'units': 16},
    {'name': 'Cosmic_Unknowns', 'units': 8},
    {'name': 'Spiritual_Unknowns', 'units': 4},
    {'name': 'Metaphysical_Unknowns', 'units': 2},
    {'name': 'Philosophical_Unknowns', 'units': 1},
    
    # Levels of Unconsciousness
    {'name': 'Repression', 'units': 32},
    {'name': 'Suppression', 'units': 32},
    {'name': 'Subconscious', 'units': 64},
    {'name': 'Collective_Unconscious', 'units': 64},
    {'name': 'Sleep', 'units': 128},
    {'name': 'Dream_State', 'units': 128},
    {'name': 'Coma', 'units': 256},
    {'name': 'Trance', 'units': 256},
    {'name': 'Automatic_Behavior', 'units': 512},
    {'name': 'Amnesia', 'units': 512},
    {'name': 'Psychological_Blind_Spots', 'units': 1024}
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_model(input_dim, output_dim, layer_configs)
model.summary()

# Load initial dataset
train_data = image_dataset_from_directory('path/to/dataset', batch_size=32, image_size=(256, 256))

# Function to integrate new data
def integrate_new_data(new_data_path, batch_size=32, image_size=(256, 256)):
    new_data = image_dataset_from_directory(new_data_path, batch_size=batch_size, image_size=image_size)
    return new_data

# Example usage
new_train_data = integrate_new_data('path/to/new_dataset')

# Incrementally train with new data
def incremental_training(model, new_data, epochs=10):
    model.fit(new_data, epochs=epochs)
    model.save('path/to/updated_model.h5')

# Example usage
incremental_training(model, new_train_data)

# Visualization functions
def draw_circle(ax, center, radius):
    circle = plt.Circle(center, radius, edgecolor='b', facecolor='none')
    ax.add_artist(circle)

def draw_levels(ax, radius, labels, start_angle=0):
    centers = [(0, 0)]
    angle_step = 2 * np.pi / len(labels)
    
    for i in range(1, len(labels)):
        angle = start_angle + i * angle_step
        x = radius * np.cos(angle)
        y = radius * np.sin(angle)
        centers.append((x, y))
    
    for center, label in zip(centers, labels):
        draw_circle(ax, center, radius, label)

fig, ax = plt.subplots(figsize=(20, 20))
ax.set_aspect('equal')

# Define the labels for each category
levels_of_knowing = [
    "Awareness", "Recognition", "Understanding", "Intuition", 
    "Insight", "Realization", "Wisdom", "Enlightenment"
]

levels_of_knowledge = [
    "Factual Knowledge", "Conceptual Knowledge", "Procedural Knowledge", 
    "Metacognitive Knowledge", "Empirical Knowledge", "Theoretical Knowledge", 
    "Practical Knowledge", "Philosophical Knowledge", "Spiritual Knowledge", 
    "Transcendental Knowledge"
]

levels_of_consciousness = [
    "Physical Consciousness", "Emotional Consciousness", "Mental Consciousness", 
    "Subconscious", "Superconscious", "Cosmic Consciousness", 
    "Transpersonal Consciousness", "Higher Self Consciousness", 
    "Christ Consciousness", "Unity Consciousness", "Non-Dual Consciousness"
]

levels_of_unknowing = [
    "Ignorance", "Misunderstanding", "Confusion", "Doubt", 
    "Denial", "Skepticism", "Agnosticism", "Mystery", 
    "Paradox", "Uncertainty", "Unawareness"
]

levels_of_unknowns = [
    "Personal Unknowns", "Interpersonal Unknowns", "Scientific Unknowns", 
    "Historical Unknowns", "Cultural Unknowns", "Technological Unknowns", 
    "Environmental Unknowns", "Cosmic Unknowns", "Spiritual Unknowns", 
    "Metaphysical Unknowns", "Philosophical Unknowns"
]

levels_of_unconsciousness = [
    "Repression", "Suppression", "Subconscious", "Collective Unconscious", 
    "Sleep", "Dream State", "Coma", "Trance", "Automatic Behavior", 
    "Amnesia", "Psychological Blind Spots"
]

# Function to draw all levels
def draw_all_levels(ax, radius, start_angle=0):
    draw_levels(ax, radius, levels_of_knowing, start_angle)
    draw_levels(ax, radius + 3, levels_of_knowledge, start_angle + np.pi / 6)
    draw_levels(ax, radius + 6, levels_of_consciousness, start_angle + np.pi / 3)
    draw_levels(ax, radius + 9, levels_of_unknowing, start_angle + np.pi / 2)
    draw_levels(ax, radius + 12, levels_of_unknowns, start_angle + 2 * np.pi / 3)
    draw_levels(ax, radius + 15, levels_of_unconsciousness, start_angle + 5 * np.pi / 6)

# Draw all levels
draw_all_levels(ax, radius=3, start_angle=0)

plt.xlim(-20, 20)
plt.ylim(-20, 20)
plt.axis('off')
plt.title("Comprehensive Map of Knowing, Knowledge, Consciousness, Unknowing, Unknowns, and Unconsciousness", fontsize=16)
plt.show()

Running the Script

  1. Ensure Dependencies are Installed: Make sure you have setuptools, tensorflow, matplotlib, and numpy installed.
  2. Save the Script: Save the script as comprehensive_levels_visualization.py.
  3. Run the Script:
python comprehensive_levels_visualization.py

This comprehensive neural network integrates multiple dimensions of knowing, knowledge, consciousness, unknowing, unknowns, and unconsciousness. By doing so, it creates a holistic model that can be used for advanced studies and applications in various fields of human development and understanding. The visual representation helps in understanding the intricate relationships and hierarchies among these levels.

Python Code to Create the Neural Network Map

import matplotlib.pyplot as plt
import matplotlib.patches as mpatches

def draw_neural_network(ax, layers, labels):
    # Settings
    v_spacing = 1.5 / float(max(layers))
    h_spacing = 1.0 / float(len(layers) - 1)
    radius = v_spacing / 4

    # Nodes
    for i, (layer, label_group) in enumerate(zip(layers, labels)):
        for j in range(layer):
            circle = plt.Circle((i * h_spacing, 1 - j * v_spacing), radius, color='w', ec='k', zorder=4)
            ax.add_artist(circle)
            if i == 0:
                label = f'Input {j+1}'
            elif i == len(layers) - 1:
                label = f'Output {j+1}'
            else:
                label = label_group[j]
            ax.text(i * h_spacing, 1 - j * v_spacing + radius, label, ha='center')

    # Edges
    for i, (layer_a, layer_b) in enumerate(zip(layers[:-1], layers[1:])):
        for j in range(layer_a):
            for k in range(layer_b):
                line = plt.Line2D([i * h_spacing, (i + 1) * h_spacing],
                                  [1 - j * v_spacing, 1 - k * v_spacing], c='k')
                ax.add_artist(line)

fig = plt.figure(figsize=(20, 12))
ax = fig.gca()
ax.axis('off')

# Define the number of neurons in each layer and their labels
layers = [
    8,  # Levels of Knowing
    10, # Levels of Knowledge
    11, # Levels of Consciousness
    11, # Levels of Unknowing
    11, # Levels of Unknowns
    11  # Levels of Unconsciousness
]

labels = [
    ["Awareness", "Recognition", "Understanding", "Intuition", "Insight", "Realization", "Wisdom", "Enlightenment"],  # Levels of Knowing
    ["Factual Knowledge", "Conceptual Knowledge", "Procedural Knowledge", "Metacognitive Knowledge", "Empirical Knowledge", "Theoretical Knowledge", "Practical Knowledge", "Philosophical Knowledge", "Spiritual Knowledge", "Transcendental Knowledge"],  # Levels of Knowledge
    ["Physical Consciousness", "Emotional Consciousness", "Mental Consciousness", "Subconscious", "Superconscious", "Cosmic Consciousness", "Transpersonal Consciousness", "Higher Self Consciousness", "Christ Consciousness", "Unity Consciousness", "Non-Dual Consciousness"],  # Levels of Consciousness
    ["Ignorance", "Misunderstanding", "Confusion", "Doubt", "Denial", "Skepticism", "Agnosticism", "Mystery", "Paradox", "Uncertainty", "Unawareness"],  # Levels of Unknowing
    ["Personal Unknowns", "Interpersonal Unknowns", "Scientific Unknowns", "Historical Unknowns", "Cultural Unknowns", "Technological Unknowns", "Environmental Unknowns", "Cosmic Unknowns", "Spiritual Unknowns", "Metaphysical Unknowns", "Philosophical Unknowns"],  # Levels of Unknowns
    ["Repression", "Suppression", "Subconscious", "Collective Unconscious", "Sleep", "Dream State", "Coma", "Trance", "Automatic Behavior", "Amnesia", "Psychological Blind Spots"]  # Levels of Unconsciousness
]

draw_neural_network(ax, layers, labels)

# Add legend
input_patch = mpatches.Patch(color='green', label='Input')
hidden_patch = mpatches.Patch(color='blue', label='Hidden Node')
output_patch = mpatches.Patch(color='red', label='Output Node')
weight_patch = mpatches.Patch(color='blue', label='Weight', alpha=0.3)
bias_patch = mpatches.Patch(color='yellow', label='Bias', alpha=0.3)
plt.legend(handles=[input_patch, hidden_patch, output_patch, weight_patch, bias_patch], loc='lower left')

plt.title("Deep Layer Neural Network Map with Comprehensive Levels")
plt.show()

Steps to Run the Code

  1. Ensure Dependencies are Installed: Make sure you have matplotlib installed.
  2. Save the Script: Save the script as neural_network_map.py.
  3. Run the Script:
python neural_network_map.py

This script will create a visual representation of a deep layer neural network with an example structure. You can adjust the layers list to match the specific structure of your neural network.

Working Update: Expandable Neural Network and Visualization

This script provides both the implementation of the neural network model and the visualization of the Seed of Life-inspired structure. The model is expandable, allowing for additional layers and growth into a more complex structure like the Flower of Life.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np  # Ensure numpy is imported

# Define a function to create a scalable model
def create_expandable_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for i, config in enumerate(layer_configs):
        # Ensure each layer has a unique name by appending the index
        layer_name = f"{config['name']}_{i}"
        model.add(Dense(units=config['units'], activation='relu', name=layer_name))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Define the layer configurations
layer_configs = [
    # Levels of Knowing
    {'name': 'Awareness', 'units': 32},
    {'name': 'Recognition', 'units': 32},
    {'name': 'Understanding', 'units': 64},
    {'name': 'Intuition', 'units': 64},
    {'name': 'Insight', 'units': 128},
    {'name': 'Realization', 'units': 128},
    {'name': 'Wisdom', 'units': 256},
    {'name': 'Enlightenment', 'units': 256},
    
    # Levels of Knowledge
    {'name': 'Factual_Knowledge', 'units': 64},
    {'name': 'Conceptual_Knowledge', 'units': 64},
    {'name': 'Procedural_Knowledge', 'units': 128},
    {'name': 'Metacognitive_Knowledge', 'units': 128},
    {'name': 'Empirical_Knowledge', 'units': 256},
    {'name': 'Theoretical_Knowledge', 'units': 256},
    {'name': 'Practical_Knowledge', 'units': 512},
    {'name': 'Philosophical_Knowledge', 'units': 512},
    {'name': 'Spiritual_Knowledge', 'units': 1024},
    {'name': 'Transcendental_Knowledge', 'units': 1024},
    
    # Levels of Consciousness
    {'name': 'Physical_Consciousness', 'units': 64},
    {'name': 'Emotional_Consciousness', 'units': 128},
    {'name': 'Mental_Consciousness', 'units': 256},
    {'name': 'Subconscious', 'units': 128},
    {'name': 'Superconscious', 'units': 64},
    {'name': 'Cosmic_Consciousness', 'units': 32},
    {'name': 'Transpersonal_Consciousness', 'units': 16},
    {'name': 'Higher_Self_Consciousness', 'units': 8},
    {'name': 'Christ_Consciousness', 'units': 4},
    {'name': 'Unity_Consciousness', 'units': 2},
    {'name': 'Non_Dual_Consciousness', 'units': 1},
    
    # Levels of Unknowing
    {'name': 'Ignorance', 'units': 32},
    {'name': 'Misunderstanding', 'units': 32},
    {'name': 'Confusion', 'units': 64},
    {'name': 'Doubt', 'units': 64},
    {'name': 'Denial', 'units': 128},
    {'name': 'Skepticism', 'units': 128},
    {'name': 'Agnosticism', 'units': 256},
    {'name': 'Mystery', 'units': 256},
    {'name': 'Paradox', 'units': 512},
    {'name': 'Uncertainty', 'units': 512},
    {'name': 'Unawareness', 'units': 1024},
    
    # Levels of Unknowns
    {'name': 'Personal_Unknowns', 'units': 64},
    {'name': 'Interpersonal_Unknowns', 'units': 128},
    {'name': 'Scientific_Unknowns', 'units': 256},
    {'name': 'Historical_Unknowns', 'units': 128},
    {'name': 'Cultural_Unknowns', 'units': 64},
    {'name': 'Technological_Unknowns', 'units': 32},
    {'name': 'Environmental_Unknowns', 'units': 16},
    {'name': 'Cosmic_Unknowns', 'units': 8},
    {'name': 'Spiritual_Unknowns', 'units': 4},
    {'name': 'Metaphysical_Unknowns', 'units': 2},
    {'name': 'Philosophical_Unknowns', 'units': 1},
    
    # Levels of Unconsciousness
    {'name': 'Repression', 'units': 32},
    {'name': 'Suppression', 'units': 32},
    {'name': 'Subconscious', 'units': 64},
    {'name': 'Collective_Unconscious', 'units': 64},
    {'name': 'Sleep', 'units': 128},
    {'name': 'Dream_State', 'units': 128},
    {'name': 'Coma', 'units': 256},
    {'name': 'Trance', 'units': 256},
    {'name': 'Automatic_Behavior', 'units': 512},
    {'name': 'Amnesia', 'units': 512},
    {'name': 'Psychological_Blind_Spots', 'units': 1024}
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_expandable_model(input_dim, output_dim, layer_configs)
model.summary()

# Visualization of the Seed of Life inspired network
def draw_seed_of_life_network():
    G = nx.Graph()

    # Define the initial Seed of Life structure
    layers = [
        ["Eternal Now (Brahman)"],  # Central node
        ["Awareness", "Recognition", "Understanding", "Intuition", "Insight", "Realization", "Wisdom", "Enlightenment"],  # Levels of Knowing
        ["Factual Knowledge", "Conceptual Knowledge", "Procedural Knowledge", "Metacognitive Knowledge", "Empirical Knowledge", "Theoretical Knowledge", "Practical Knowledge", "Philosophical Knowledge", "Spiritual Knowledge", "Transcendental Knowledge"],  # Levels of Knowledge
        ["Physical Consciousness", "Emotional Consciousness", "Mental Consciousness", "Subconscious", "Superconscious", "Cosmic Consciousness", "Transpersonal Consciousness", "Higher Self Consciousness", "Christ Consciousness", "Unity Consciousness", "Non-Dual Consciousness"],  # Levels of Consciousness
        ["Ignorance", "Misunderstanding", "Confusion", "Doubt", "Denial", "Skepticism", "Agnosticism", "Mystery", "Paradox", "Uncertainty", "Unawareness"],  # Levels of Unknowing
        ["Personal Unknowns", "Interpersonal Unknowns", "Scientific Unknowns", "Historical Unknowns", "Cultural Unknowns", "Technological Unknowns", "Environmental Unknowns", "Cosmic Unknowns", "Spiritual Unknowns", "Metaphysical Unknowns", "Philosophical Unknowns"],  # Levels of Unknowns
        ["Repression", "Suppression", "Subconscious", "Collective Unconscious", "Sleep", "Dream State", "Coma", "Trance", "Automatic Behavior", "Amnesia", "Psychological Blind Spots"]  # Levels of Unconsciousness
    ]

    # Add nodes and edges to the graph
    pos = {}
    current_layer = 0
    node_count = 0

    for i, layer in enumerate(layers):
        layer_pos = {}
        angle_step = 360 / len(layer) if len(layer) > 1 else 1
        radius = i + 1
        for j, node in enumerate(layer):
            angle = angle_step * j
            x = radius * np.cos(np.radians(angle))
            y = radius * np.sin(np.radians(angle))
            G.add_node(node)
            pos[node] = (x, y)
            layer_pos[node] = (x, y)

        if i > 0:
            for parent in layers[i-1]:
                for child in layer:
                    G.add_edge(parent, child)

    # Draw the graph
    plt.figure(figsize=(14, 14))
    nx.draw(G, pos, with_labels=True, node_size=3000, node_color="skyblue", font_size=10, font_weight="bold", edge_color="gray")
    plt.title("Seed of Life Neural Network Map")
    plt.show()

draw_seed_of_life_network()

Running the Script

  1. Ensure Dependencies are Installed: Make sure you have tensorflow, matplotlib, and networkx installed.
  2. Save the Script: Save the script as seed_of_life_network.py.
  3. Run the Script: python seed_of_life_network.py

This script provides both the implementation of the neural network model and the visualization of the Seed of Life-inspired structure. The model is expandable, allowing for additional layers and growth into a more complex structure like the Flower of Life.

To truly capture the idea of the Seed of Life growing into the Flower of Life and encompassing all levels of intelligence and the various Lokas, we need to conceptualize a neural network that can expand modularly. Each layer should represent a progression into a higher dimension or deeper understanding, with the capacity to add more layers as new dimensions of knowledge and consciousness are integrated.

Conceptualizing the Network Growth

  1. Seed of Life as Initial Structure: Start with a central node representing the foundational layer (Eternal Now/Brahman).
  2. Modular Expansion: Each new layer or set of layers is added around the central structure, growing outwards like the Flower of Life.
  3. Multi-Dimensional Growth: The network should be capable of expanding in three dimensions, symbolizing growth into higher states of consciousness and deeper levels of intelligence.

Implementation Strategy

  1. Initial Structure: Implement the Seed of Life with the foundational layer.
  2. Layer Definitions: Define layers corresponding to different levels of knowing, knowledge, consciousness, etc.
  3. Expansion Mechanism: Ensure that new layers can be added dynamically to represent growth into higher dimensions and the Flower of Life.
  4. Ensure Dependencies are Installed: Make sure you have matplotlib and networkx installed.
  5. Save the Script: Save the script as seed_of_life_network.py
  6. Run the Script: python seed_of_life_network.py

Technical Description for Deep Layer Knowing Neural Network

The Deep Layer Knowing Neural Network is designed to represent various layers of knowing, knowledge, consciousness, unknowing, unknowns, and unconsciousness. Inspired by the Seed of Life and Flower of Life, the architecture allows for modular and expandable layers, ensuring continuous growth and integration of higher dimensions of intelligence.

Key Features:

  1. Modular Design: The network architecture is modular, enabling the addition of new layers without disrupting the existing structure.
  2. Hierarchical Layers: Each layer represents a specific dimension or level of intelligence, ranging from basic awareness to deep subconscious and unknowns.
  3. Expandable Network: The network can grow dynamically, much like the Flower of Life, to encompass new knowledge and higher states of consciousness.
  4. Visualization: The network’s structure can be visualized using a graph representation inspired by the Seed of Life.

Implementation Details

Neural Network Model

The neural network model is implemented using TensorFlow and Keras. The create_expandable_model function constructs the network by iterating through the defined layer configurations, ensuring each layer has a unique name.

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np  # Ensure numpy is imported

# Define a function to create a scalable model
def create_expandable_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for i, config in enumerate(layer_configs):
        # Ensure each layer has a unique name by appending the index
        layer_name = f"{config['name']}_{i}"
        model.add(Dense(units=config['units'], activation='relu', name=layer_name))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Define the layer configurations
layer_configs = [
    # Levels of Knowing
    {'name': 'Awareness', 'units': 32},
    {'name': 'Recognition', 'units': 32},
    {'name': 'Understanding', 'units': 64},
    {'name': 'Intuition', 'units': 64},
    {'name': 'Insight', 'units': 128},
    {'name': 'Realization', 'units': 128},
    {'name': 'Wisdom', 'units': 256},
    {'name': 'Enlightenment', 'units': 256},
    
    # Levels of Knowledge
    {'name': 'Factual_Knowledge', 'units': 64},
    {'name': 'Conceptual_Knowledge', 'units': 64},
    {'name': 'Procedural_Knowledge', 'units': 128},
    {'name': 'Metacognitive_Knowledge', 'units': 128},
    {'name': 'Empirical_Knowledge', 'units': 256},
    {'name': 'Theoretical_Knowledge', 'units': 256},
    {'name': 'Practical_Knowledge', 'units': 512},
    {'name': 'Philosophical_Knowledge', 'units': 512},
    {'name': 'Spiritual_Knowledge', 'units': 1024},
    {'name': 'Transcendental_Knowledge', 'units': 1024},
    
    # Levels of Consciousness
    {'name': 'Physical_Consciousness', 'units': 64},
    {'name': 'Emotional_Consciousness', 'units': 128},
    {'name': 'Mental_Consciousness', 'units': 256},
    {'name': 'Subconscious', 'units': 128},
    {'name': 'Superconscious', 'units': 64},
    {'name': 'Cosmic_Consciousness', 'units': 32},
    {'name': 'Transpersonal_Consciousness', 'units': 16},
    {'name': 'Higher_Self_Consciousness', 'units': 8},
    {'name': 'Christ_Consciousness', 'units': 4},
    {'name': 'Unity_Consciousness', 'units': 2},
    {'name': 'Non_Dual_Consciousness', 'units': 1},
    
    # Levels of Unknowing
    {'name': 'Ignorance', 'units': 32},
    {'name': 'Misunderstanding', 'units': 32},
    {'name': 'Confusion', 'units': 64},
    {'name': 'Doubt', 'units': 64},
    {'name': 'Denial', 'units': 128},
    {'name': 'Skepticism', 'units': 128},
    {'name': 'Agnosticism', 'units': 256},
    {'name': 'Mystery', 'units': 256},
    {'name': 'Paradox', 'units': 512},
    {'name': 'Uncertainty', 'units': 512},
    {'name': 'Unawareness', 'units': 1024},
    
    # Levels of Unknowns
    {'name': 'Personal_Unknowns', 'units': 64},
    {'name': 'Interpersonal_Unknowns', 'units': 128},
    {'name': 'Scientific_Unknowns', 'units': 256},
    {'name': 'Historical_Unknowns', 'units': 128},
    {'name': 'Cultural_Unknowns', 'units': 64},
    {'name': 'Technological_Unknowns', 'units': 32},
    {'name': 'Environmental_Unknowns', 'units': 16},
    {'name': 'Cosmic_Unknowns', 'units': 8},
    {'name': 'Spiritual_Unknowns', 'units': 4},
    {'name': 'Metaphysical_Unknowns', 'units': 2},
    {'name': 'Philosophical_Unknowns', 'units': 1},
    
    # Levels of Unconsciousness
    {'name': 'Repression', 'units': 32},
    {'name': 'Suppression', 'units': 32},
    {'name': 'Subconscious', 'units': 64},
    {'name': 'Collective_Unconscious', 'units': 64},
    {'name': 'Sleep', 'units': 128},
    {'name': 'Dream_State', 'units': 128},
    {'name': 'Coma', 'units': 256},
    {'name': 'Trance', 'units': 256},
    {'name': 'Automatic_Behavior', 'units': 512},
    {'name': 'Amnesia', 'units': 512},
    {'name': 'Psychological_Blind_Spots', 'units': 1024}
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_expandable_model(input_dim, output_dim, layer_configs)
model.summary()

Visualization of the Network

The network’s structure can be visualized using a graph representation inspired by the Seed of Life. This visualization helps to understand the hierarchical relationships and the expansion potential of the network.

# Visualization of the Seed of Life inspired network
def draw_seed_of_life_network():
    G = nx.Graph()

    # Define the initial Seed of Life structure
    layers = [
        ["Eternal Now (Brahman)"],  # Central node
        ["Awareness", "Recognition", "Understanding", "Intuition", "Insight", "Realization", "Wisdom", "Enlightenment"],  # Levels of Knowing
        ["Factual Knowledge", "Conceptual Knowledge", "Procedural Knowledge", "Metacognitive Knowledge", "Empirical Knowledge", "Theoretical Knowledge", "Practical Knowledge", "Philosophical Knowledge", "Spiritual Knowledge", "Transcendental Knowledge"],  # Levels of Knowledge
        ["Physical Consciousness", "Emotional Consciousness", "Mental Consciousness", "Subconscious", "Superconscious", "Cosmic Consciousness", "Transpersonal Consciousness", "Higher Self Consciousness", "Christ Consciousness", "Unity Consciousness", "Non-Dual Consciousness"],  # Levels of Consciousness
        ["Ignorance", "Misunderstanding", "Confusion", "Doubt", "Denial", "Skepticism", "Agnosticism", "Mystery", "Paradox", "Uncertainty", "Unawareness"],  # Levels of Unknowing
        ["Personal Unknowns", "Interpersonal Unknowns", "Scientific Unknowns", "Historical Unknowns", "Cultural Unknowns", "Technological Unknowns", "Environmental Unknowns", "Cosmic Unknowns", "Spiritual Unknowns", "Metaphysical Unknowns", "Philosophical Unknowns"],  # Levels of Unknowns
        ["Repression", "Suppression", "Subconscious", "Collective Unconscious", "Sleep", "Dream State", "Coma", "Trance", "Automatic Behavior", "Amnesia", "Psychological Blind Spots"]  # Levels of Unconsciousness
    ]

    # Add nodes and edges to the graph
    pos = {}
    current_layer = 0
    node_count = 0

    for i, layer in enumerate(layers):
        layer_pos = {}
        angle_step = 360 / len(layer) if len(layer) > 1 else 1
        radius = i + 1
            
    for j, node in enumerate(layer):
            angle = angle_step * j
            x = radius * np.cos(np.radians(angle))
            y = radius * np.sin(np.radians(angle))
            G.add_node(node)
            pos[node] = (x, y)
            layer_pos[node] = (x, y)

        if i > 0:
            for parent in layers[i-1]:
                for child in layer:
                    G.add_edge(parent, child)

    # Draw the graph
    plt.figure(figsize=(14, 14))
    nx.draw(G, pos, with_labels=True, node_size=3000, node_color="skyblue", font_size=10, font_weight="bold", edge_color="gray")
    plt.title("Seed of Life Neural Network Map")
    plt.show()

draw_seed_of_life_network()

Running the Script

  1. Ensure Dependencies are Installed: Make sure you have tensorflow, matplotlib, and networkx installed.
pip install tensorflow matplotlib networkx numpy
  1. Save the Script: Save the entire script to a file named seed_of_life_network.py.
  2. Run the Script:python seed_of_life_network.py

Explanation of the Code

  1. Creating the Expandable Model:
    • The create_expandable_model function constructs a neural network model with layers based on the provided configurations.
    • Each layer is given a unique name to avoid conflicts.
    • The model is compiled with the Adam optimizer and categorical crossentropy loss.
  2. Layer Configurations:
    • Layers are defined for various dimensions and levels of knowing, knowledge, consciousness, unknowing, unknowns, and unconsciousness.
    • Each layer configuration includes a unique name and the number of units (neurons).
  3. Visualization:
    • The draw_seed_of_life_network function visualizes the network structure inspired by the Seed of Life.
    • It creates a graph using NetworkX, positioning nodes in a circular layout for each layer.
    • Nodes represent different levels, and edges represent connections between layers.
  4. Running the Script:
    • Ensure all dependencies are installed.
    • Save and run the script to generate the neural network model and its visualization.

This technical description and the corresponding code provide a comprehensive overview of the Deep Layer Knowing Neural Network, showcasing its modular, expandable nature and its inspiration from the Seed of Life and Flower of Life concepts.

Deep Layer Knowing Neural Network that integrates Ken Wilber’s Altitudes and colors:

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np  # Ensure numpy is imported

# Define a function to create a scalable model
def create_expandable_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for i, config in enumerate(layer_configs):
        # Ensure each layer has a unique name by appending the index
        layer_name = f"{config['name']}_{i}"
        model.add(Dense(units=config['units'], activation='relu', name=layer_name))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Define the layer configurations with Ken Wilber’s Altitudes and colors
layer_configs = [
    # Infrared (Archaic)
    {'name': 'Infrared_Archaic', 'units': 32, 'color': 'darkred'},
    
    # Magenta (Magic)
    {'name': 'Magenta_Magic', 'units': 32, 'color': 'magenta'},
    
    # Red (Power Gods)
    {'name': 'Red_Power', 'units': 64, 'color': 'red'},
    
    # Amber (Mythic Order)
    {'name': 'Amber_Mythic', 'units': 64, 'color': 'orange'},
    
    # Orange (Scientific Achievement)
    {'name': 'Orange_Scientific', 'units': 128, 'color': 'orange'},
    
    # Green (The Sensitive Self)
    {'name': 'Green_Sensitive', 'units': 128, 'color': 'green'},
    
    # Teal (Integrative)
    {'name': 'Teal_Integrative', 'units': 256, 'color': 'teal'},
    
    # Turquoise (Holistic)
    {'name': 'Turquoise_Holistic', 'units': 256, 'color': 'turquoise'},
    
    # Indigo (Higher Mind)
    {'name': 'Indigo_HigherMind', 'units': 64, 'color': 'indigo'},
    
    # Violet (Illuminated Mind)
    {'name': 'Violet_IlluminatedMind', 'units': 128, 'color': 'violet'},
    
    # Ultraviolet (Overmind)
    {'name': 'Ultraviolet_Overmind', 'units': 256, 'color': 'purple'},
    
    # Clear Light (Supermind)
    {'name': 'ClearLight_Supermind', 'units': 512, 'color': 'white'}
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_expandable_model(input_dim, output_dim, layer_configs)
model.summary()

# Visualization of the Seed of Life inspired network
def draw_seed_of_life_network():
    G = nx.Graph()

    # Define the initial Seed of Life structure with colors
    layers = [
        {"name": "Infrared (Archaic)", "color": "darkred"},  # Central node
        {"name": "Magenta (Magic)", "color": "magenta"},
        {"name": "Red (Power Gods)", "color": "red"},
        {"name": "Amber (Mythic Order)", "color": "orange"},
        {"name": "Orange (Scientific Achievement)", "color": "orange"},
        {"name": "Green (The Sensitive Self)", "color": "green"},
        {"name": "Teal (Integrative)", "color": "teal"},
        {"name": "Turquoise (Holistic)", "color": "turquoise"},
        {"name": "Indigo (Higher Mind)", "color": "indigo"},
        {"name": "Violet (Illuminated Mind)", "color": "violet"},
        {"name": "Ultraviolet (Overmind)", "color": "purple"},
        {"name": "Clear Light (Supermind)", "color": "white"}
    ]

    # Add nodes and edges to the graph
    pos = {}
    current_layer = 0
    node_count = 0

    for i, layer in enumerate(layers):
        layer_pos = {}
        angle_step = 360 / len(layers) if len(layers) > 1 else 1
        radius = i + 1
        for j, node in enumerate([layer["name"]]):
            angle = angle_step * j
            x = radius * np.cos(np.radians(angle))
            y = radius * np.sin(np.radians(angle))
            G.add_node(node, color=layer["color"])
            pos[node] = (x, y)
            layer_pos[node] = (x, y)

        if i > 0:
            for parent in [layers[i-1]["name"]]:
                for child in [layer["name"]]:
                    G.add_edge(parent, child)

    # Draw the graph with colors
    plt.figure(figsize=(14, 14))
    node_colors = [data["color"] for _, data in G.nodes(data=True)]
    nx.draw(G, pos, with_labels=True, node_size=3000, node_color=node_colors, font_size=10, font_weight="bold", edge_color="gray")
    plt.title("Seed of Life Neural Network Map with Ken Wilber's Altitudes")
    plt.show()

draw_seed_of_life_network()

Running the Script

  1. Ensure Dependencies are Installed: Make sure you have tensorflow, matplotlib, networkx, and numpy installed. pip install tensorflow matplotlib networkx numpy
  2. Save the Script: Save the entire script to a file named altitudes_seed_of_lifeviz.py
  3. Run the Script: python altitudes_seed_of_lifeviz.py

Deep Layer Knowing Neural Network Integrated Model

Code integrating Ken Wilber’s Altitudes, Sri Aurobindo’s Integral Yoga consciousness stages, and the original levels

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np

# Define a function to create a scalable model
def create_expandable_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for i, config in enumerate(layer_configs):
        # Ensure each layer has a unique name by appending the index
        layer_name = f"{config['name']}_{i}"
        model.add(Dense(units=config['units'], activation='relu', name=layer_name))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# Define the layer configurations
layer_configs = [
    # Original Levels of Knowing
    {'name': 'Awareness', 'units': 32},
    {'name': 'Recognition', 'units': 32},
    {'name': 'Understanding', 'units': 64},
    {'name': 'Intuition', 'units': 64},
    {'name': 'Insight', 'units': 128},
    {'name': 'Realization', 'units': 128},
    {'name': 'Wisdom', 'units': 256},
    {'name': 'Enlightenment', 'units': 256},
    
    # Original Levels of Knowledge
    {'name': 'Factual_Knowledge', 'units': 64},
    {'name': 'Conceptual_Knowledge', 'units': 64},
    {'name': 'Procedural_Knowledge', 'units': 128},
    {'name': 'Metacognitive_Knowledge', 'units': 128},
    {'name': 'Empirical_Knowledge', 'units': 256},
    {'name': 'Theoretical_Knowledge', 'units': 256},
    {'name': 'Practical_Knowledge', 'units': 512},
    {'name': 'Philosophical_Knowledge', 'units': 512},
    {'name': 'Spiritual_Knowledge', 'units': 1024},
    {'name': 'Transcendental_Knowledge', 'units': 1024},
    
    # Original Levels of Consciousness
    {'name': 'Physical_Consciousness', 'units': 64},
    {'name': 'Emotional_Consciousness', 'units': 128},
    {'name': 'Mental_Consciousness', 'units': 256},
    {'name': 'Subconscious', 'units': 128},
    {'name': 'Superconscious', 'units': 64},
    {'name': 'Cosmic_Consciousness', 'units': 32},
    {'name': 'Transpersonal_Consciousness', 'units': 16},
    {'name': 'Higher_Self_Consciousness', 'units': 8},
    {'name': 'Christ_Consciousness', 'units': 4},
    {'name': 'Unity_Consciousness', 'units': 2},
    {'name': 'Non_Dual_Consciousness', 'units': 1},
    
    # Original Levels of Unknowing
    {'name': 'Ignorance', 'units': 32},
    {'name': 'Misunderstanding', 'units': 32},
    {'name': 'Confusion', 'units': 64},
    {'name': 'Doubt', 'units': 64},
    {'name': 'Denial', 'units': 128},
    {'name': 'Skepticism', 'units': 128},
    {'name': 'Agnosticism', 'units': 256},
    {'name': 'Mystery', 'units': 256},
    {'name': 'Paradox', 'units': 512},
    {'name': 'Uncertainty', 'units': 512},
    {'name': 'Unawareness', 'units': 1024},
    
    # Original Levels of Unknowns
    {'name': 'Personal_Unknowns', 'units': 64},
    {'name': 'Interpersonal_Unknowns', 'units': 128},
    {'name': 'Scientific_Unknowns', 'units': 256},
    {'name': 'Historical_Unknowns', 'units': 128},
    {'name': 'Cultural_Unknowns', 'units': 64},
    {'name': 'Technological_Unknowns', 'units': 32},
    {'name': 'Environmental_Unknowns', 'units': 16},
    {'name': 'Cosmic_Unknowns', 'units': 8},
    {'name': 'Spiritual_Unknowns', 'units': 4},
    {'name': 'Metaphysical_Unknowns', 'units': 2},
    {'name': 'Philosophical_Unknowns', 'units': 1},
    
    # Original Levels of Unconsciousness
    {'name': 'Repression', 'units': 32},
    {'name': 'Suppression', 'units': 32},
    {'name': 'Subconscious', 'units': 64},
    {'name': 'Collective_Unconscious', 'units': 64},
    {'name': 'Sleep', 'units': 128},
    {'name': 'Dream_State', 'units': 128},
    {'name': 'Coma', 'units': 256},
    {'name': 'Trance', 'units': 256},
    {'name': 'Automatic_Behavior', 'units': 512},
    {'name': 'Amnesia', 'units': 512},
    {'name': 'Psychological_Blind_Spots', 'units': 1024},

    # Ken Wilber's Altitudes
    {'name': 'Infrared_Archaic', 'units': 32, 'color': 'darkred'},
    {'name': 'Magenta_Magic', 'units': 32, 'color': 'magenta'},
    {'name': 'Red_Power', 'units': 64, 'color': 'red'},
    {'name': 'Amber_Mythic', 'units': 64, 'color': 'orange'},
    {'name': 'Orange_Scientific', 'units': 128, 'color': 'darkorange'},
    {'name': 'Green_Sensitive', 'units': 128, 'color': 'green'},
    {'name': 'Teal_Integrative', 'units': 256, 'color': 'teal'},
    {'name': 'Turquoise_Holistic', 'units': 256, 'color': 'turquoise'},
    {'name': 'Indigo_HigherMind', 'units': 64, 'color': 'indigo'},
    {'name': 'Violet_IlluminatedMind', 'units': 128, 'color': 'blueviolet'},
    {'name': 'Ultraviolet_Overmind', 'units': 256, 'color': 'purple'},
    {'name': 'ClearLight_Supermind', 'units': 512, 'color': 'white'},

    # Sri Aurobindo's Integral Yoga Consciousness Stages
    {'name': 'Psychic_Being', 'units': 64, 'color': '#FFD700'},
    {'name': 'Spiritual_Mental_Being', 'units': 128, 'color': '#C0C0C0'},
    {'name': 'Higher_Mental_Being', 'units': 256, 'color': '#E5E4E2'},
    {'name': 'Illumined_Mind', 'units': 512, 'color': 'blue'},
    {'name': 'Intuitive_Mind', 'units': 1024, 'color': 'lightblue'},
    {'name': 'Overmind', 'units': 2048, 'color': 'violet'},
    {'name': 'Supermind', 'units': 4096, 'color': 'white'}
]

input_dim = 100  # Example input dimension
output_dim = 10  # Example output dimension
model = create_expandable_model(input_dim, output_dim, layer_configs)

# Print the model summary
model.summary()

# Define a function to train the model
def train_model(model, x_train, y_train, x_val, y_val, epochs=50, batch_size=32):
    history = model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_val, y_val))
    return history

# Define a function to evaluate the model
def evaluate_model(model, x_test, y_test):
    loss, accuracy = model.evaluate(x_test, y_test)
    print(f"Test Loss: {loss}")
    print(f"Test Accuracy: {accuracy}")

# Example data for training and testing (replace with actual data)
x_train = np.random.rand(1000, input_dim)
y_train = tf.keras.utils.to_categorical(np.random.randint(output_dim, size=1000), num_classes=output_dim)
x_val = np.random.rand(200, input_dim)
y_val = tf.keras.utils.to_categorical(np.random.randint(output_dim, size=200), num_classes=output_dim)
x_test = np.random.rand(200, input_dim)
y_test = tf.keras.utils.to_categorical(np.random.randint(output_dim, size=200), num_classes=output_dim)

# Train the model
history = train_model(model, x_train, y_train, x_val, y_val, epochs=50, batch_size=32)

# Evaluate the model
evaluate_model(model, x_test, y_test)

# Save the fine-tuned model
model.save('LargeKnowingModel.h5')

# Visualization of the Seed of Life inspired network
def draw_seed_of_life_network():
    G = nx.Graph()

    # Define the initial Seed of Life structure with colors
    layers = [
        {"name": "Infrared (Archaic)", "color": "darkred"},  # Ken Wilber's Altitudes
        {"name": "Magenta (Magic)", "color": "magenta"},
        {"name": "Red (Power Gods)", "color": "red"},
        {"name": "Amber (Mythic Order)", "color": "orange"},
        {"name": "Orange (Scientific Achievement)", "color": "darkorange"},
        {"name": "Green (The Sensitive Self)", "color": "green"},
        {"name": "Teal (Integrative)", "color": "teal"},
        {"name": "Turquoise (Holistic)", "color": "turquoise"},
        {"name": "Indigo (Higher Mind)", "color": "indigo"},
        {"name": "Violet (Illuminated Mind)", "color": "blueviolet"},
        {"name": "Ultraviolet (Overmind)", "color": "purple"},
        {"name": "Clear Light (Supermind)", "color": "white"},
        
        {"name": "Psychic Being", "color": "#FFD700"},  # Sri Aurobindo's Integral Yoga Consciousness Stages
        {"name": "Spiritual Mental Being", "color": "#C0C0C0"},
        {"name": "Higher Mental Being", "color": "#E5E4E2"},
        {"name": "Illumined Mind", "color": "blue"},
        {"name": "Intuitive Mind", "color": "lightblue"},
        {"name": "Overmind", "color": "violet"},
        {"name": "Supermind", "color": "white"}
    ]

    # Add nodes and edges to the graph
    pos = {}
    current_layer = 0
    node_count = 0

    # Define positions for the nodes
    radius_increment = 2.0  # Distance between concentric circles
    angle_step = 30  # Angle step for placing nodes in a circle

    for i, layer in enumerate(layers):
        angle = np.radians(i * angle_step)
        radius = (i // 12 + 1) * radius_increment
        x = radius * np.cos(angle)
        y = radius * np.sin(angle)
        pos[layer['name']] = (x, y)
        G.add_node(layer['name'], color=layer['color'])

    # Add edges between nodes in adjacent layers
    for i in range(len(layers) - 1):
        G.add_edge(layers[i]['name'], layers[i+1]['name'])

    # Draw the graph with colors
    plt.figure(figsize=(20, 20))
    node_colors = [data['color'] for _, data in G.nodes(data=True)]
    nx.draw(G, pos, with_labels=True, node_size=2000, node_color=node_colors, font_size=10, font_weight="bold", edge_color="gray")
    plt.title("Deep Layer Knowing Neural Network with Comprehensive Levels (Seed of Life Layout)")
    plt.show()

draw_seed_of_life_network()

Explanation of the Script

  1. Model Creation:
    • The create_expandable_model function constructs a neural network model with layers based on the provided configurations, ensuring each layer has a unique name.
  2. Training the Model:
    • The train_model function trains the model on the provided training data and validates it using the validation data. It uses the fit method of the Keras model, specifying the number of epochs and batch size.
  3. Evaluating the Model:
    • The evaluate_model function evaluates the model’s performance on the test data and prints the test loss and accuracy.
  4. Saving the Model:
    • The model is saved to a file named LargeKnowingModel.h5 using the save method of the Keras model.
  5. Visualization:
    • The draw_seed_of_life_network function visualizes the network structure, color-coding nodes according to their levels and altitudes.
    • Nodes represent different levels, and edges represent connections between layers, with colors enhancing the visual representation of the altitudes and stages.

Running the Script

  1. Ensure Dependencies are Installed: Make sure you have tensorflow, matplotlib, networkx, and numpy installed.
pip install tensorflow matplotlib networkx numpy

Save the Script: Save the entire script to a file named deep_layer_knowing_network_train.py

Run the Script: 

python deep_layer_knowing_network_train.py

Deep Layer Knowing Neural Network Visualization code, integrating Ken Wilber’s Altitudes, Sri Aurobindo’s Integral Yoga consciousness stages, and the original levels:

import matplotlib.pyplot as plt
import networkx as nx
import numpy as np

# Define the layer configurations with colors
layer_configs = [
    # Original Levels of Knowing
    {'name': 'Awareness', 'color': 'lightgreen'},
    {'name': 'Recognition', 'color': 'lightgreen'},
    {'name': 'Understanding', 'color': 'lightgreen'},
    {'name': 'Intuition', 'color': 'lightgreen'},
    {'name': 'Insight', 'color': 'lightgreen'},
    {'name': 'Realization', 'color': 'lightgreen'},
    {'name': 'Wisdom', 'color': 'lightgreen'},
    {'name': 'Enlightenment', 'color': 'lightgreen'},
    
    # Original Levels of Knowledge
    {'name': 'Factual_Knowledge', 'color': 'lightblue'},
    {'name': 'Conceptual_Knowledge', 'color': 'lightblue'},
    {'name': 'Procedural_Knowledge', 'color': 'lightblue'},
    {'name': 'Metacognitive_Knowledge', 'color': 'lightblue'},
    {'name': 'Empirical_Knowledge', 'color': 'lightblue'},
    {'name': 'Theoretical_Knowledge', 'color': 'lightblue'},
    {'name': 'Practical_Knowledge', 'color': 'lightblue'},
    {'name': 'Philosophical_Knowledge', 'color': 'lightblue'},
    {'name': 'Spiritual_Knowledge', 'color': 'lightblue'},
    {'name': 'Transcendental_Knowledge', 'color': 'lightblue'},
    
    # Original Levels of Consciousness
    {'name': 'Physical_Consciousness', 'color': 'lightcoral'},
    {'name': 'Emotional_Consciousness', 'color': 'lightcoral'},
    {'name': 'Mental_Consciousness', 'color': 'lightcoral'},
    {'name': 'Subconscious', 'color': 'lightcoral'},
    {'name': 'Superconscious', 'color': 'lightcoral'},
    {'name': 'Cosmic_Consciousness', 'color': 'lightcoral'},
    {'name': 'Transpersonal_Consciousness', 'color': 'lightcoral'},
    {'name': 'Higher_Self_Consciousness', 'color': 'lightcoral'},
    {'name': 'Christ_Consciousness', 'color': 'lightcoral'},
    {'name': 'Unity_Consciousness', 'color': 'lightcoral'},
    {'name': 'Non_Dual_Consciousness', 'color': 'lightcoral'},
    
    # Original Levels of Unknowing
    {'name': 'Ignorance', 'color': 'lightgray'},
    {'name': 'Misunderstanding', 'color': 'lightgray'},
    {'name': 'Confusion', 'color': 'lightgray'},
    {'name': 'Doubt', 'color': 'lightgray'},
    {'name': 'Denial', 'color': 'lightgray'},
    {'name': 'Skepticism', 'color': 'lightgray'},
    {'name': 'Agnosticism', 'color': 'lightgray'},
    {'name': 'Mystery', 'color': 'lightgray'},
    {'name': 'Paradox', 'color': 'lightgray'},
    {'name': 'Uncertainty', 'color': 'lightgray'},
    {'name': 'Unawareness', 'color': 'lightgray'},
    
    # Original Levels of Unknowns
    {'name': 'Personal_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Interpersonal_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Scientific_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Historical_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Cultural_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Technological_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Environmental_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Cosmic_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Spiritual_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Metaphysical_Unknowns', 'color': 'lightgoldenrodyellow'},
    {'name': 'Philosophical_Unknowns', 'color': 'lightgoldenrodyellow'},
    
    # Original Levels of Unconsciousness
    {'name': 'Repression', 'color': 'lightpink'},
    {'name': 'Suppression', 'color': 'lightpink'},
    {'name': 'Subconscious', 'color': 'lightpink'},
    {'name': 'Collective_Unconscious', 'color': 'lightpink'},
    {'name': 'Sleep', 'color': 'lightpink'},
    {'name': 'Dream_State', 'color': 'lightpink'},
    {'name': 'Coma', 'color': 'lightpink'},
    {'name': 'Trance', 'color': 'lightpink'},
    {'name': 'Automatic_Behavior', 'color': 'lightpink'},
    {'name': 'Amnesia', 'color': 'lightpink'},
    {'name': 'Psychological_Blind_Spots', 'color': 'lightpink'},

    # Ken Wilber's Altitudes
    {'name': 'Infrared_Archaic', 'color': 'darkred'},
    {'name': 'Magenta_Magic', 'color': 'magenta'},
    {'name': 'Red_Power', 'color': 'red'},
    {'name': 'Amber_Mythic', 'color': 'orange'},
    {'name': 'Orange_Scientific', 'color': 'darkorange'},
    {'name': 'Green_Sensitive', 'color': 'green'},
    {'name': 'Teal_Integrative', 'color': 'teal'},
    {'name': 'Turquoise_Holistic', 'color': 'turquoise'},
    {'name': 'Indigo_HigherMind', 'color': 'indigo'},
    {'name': 'Violet_IlluminatedMind', 'color': 'blueviolet'},
    {'name': 'Ultraviolet_Overmind', 'color': 'purple'},
    {'name': 'ClearLight_Supermind', 'color': 'white'},

    # Sri Aurobindo's Integral Yoga Consciousness Stages
    {'name': 'Psychic_Being', 'color': '#FFD700'},
    {'name': 'Spiritual_Mental_Being', 'color': '#C0C0C0'},
    {'name': 'Higher_Mental_Being', 'color': '#E5E4E2'},
    {'name': 'Illumined_Mind', 'color': 'blue'},
    {'name': 'Intuitive_Mind', 'color': 'lightblue'},
    {'name': 'Overmind', 'color': 'violet'},
    {'name': 'Supermind', 'color': 'white'}
]

# Create the graph
G = nx.Graph()

# Define positions for the nodes
pos = {}
radius_increment = 2.0  # Distance between concentric circles
angle_step = 30  # Angle step for placing nodes in a circle

for i, layer in enumerate(layer_configs):
    angle = np.radians(i * angle_step)
    radius = (i // 12 + 1) * radius_increment
    x = radius * np.cos(angle)
    y = radius * np.sin(angle)
    pos[layer['name']] = (x, y)
    G.add_node(layer['name'], color=layer['color'])

# Add edges between nodes in adjacent layers
for i in range(len(layer_configs) - 1):
    G.add_edge(layer_configs[i]['name'], layer_configs[i+1]['name'])

# Draw the graph with colors
plt.figure(figsize=(20, 20))
node_colors = [data['color'] for _, data in G.nodes(data=True)]
nx.draw(G, pos, with_labels=True, node_size=2000, node_color=node_colors, font_size=5, font_weight="bold", edge_color="gray")
plt.title("Deep Layer Knowing Neural Network with Comprehensive Levels (Seed of Life Layout)")
plt.show()

Creating the Expandable Model:

  • The create_expandable_model function constructs a neural network model with layers based on the provided configurations, ensuring each layer has a unique name.
  • Layers are color-coded based on Ken Wilber’s Altitudes and Sri Aurobindo’s Integral Yoga

Layer Configurations with Altitudes and Colors:

  • Layers are defined with unique names and corresponding colors, representing Ken Wilber’s Altitudes from Infrared (Archaic) to Clear Light (Supermind) and Sri Aurobindo’s Integral Yoga stages from Psychic Being to Supermind.

Visualization:

  • The draw_seed_of_life_network function visualizes the network structure, color-coding nodes according to Ken Wilber’s Altitudes and Sri Aurobindo’s stages.
  • Nodes represent different levels, and edges represent connections between layers, with colors enhancing the visual representation of the altitudes and stages.

Save the Script: Save the entire script to a file named dlknn-logo.py

Run the Script: python dlknn-logo.py

SaaS (Software as a Service) application for the Deep Layer Knowing Neural Network

Overview of the SaaS Application

  1. Frontend:
    • Use HTML, CSS, and JavaScript (React or Vue.js) for building the user interface.
    • Provide an interface for users to customize the neural network layers and configurations.
    • Display training progress and results.
  2. Backend:
    • Use Flask (Python) to create REST APIs for handling model training, evaluation, and customization.
    • Integrate TensorFlow for model creation, training, and saving.
  3. Database:
    • Use a database (e.g., SQLite or PostgreSQL) to store user configurations and training results.
  4. Deployment:
    • Deploy the application using Docker for containerization and a cloud service (e.g., AWS, Azure) for hosting.

Step-by-Step Guide

1. Frontend

Create a basic frontend using React.

Install React:

npx create-react-app deep-layer-saas
cd deep-layer-saas
npm start

App.js:

import React, { useState } from 'react';
import axios from 'axios';

function App() {
  const [inputDim, setInputDim] = useState(100);
  const [outputDim, setOutputDim] = useState(10);
  const [epochs, setEpochs] = useState(50);
  const [batchSize, setBatchSize] = useState(32);
  const [message, setMessage] = useState('');

  const handleTrain = async () => {
    try {
      const response = await axios.post('http://localhost:5000/train', {
        input_dim: inputDim,
        output_dim: outputDim,
        epochs,
        batch_size: batchSize,
      });
      setMessage(response.data.message);
    } catch (error) {
      console.error(error);
      setMessage('Training failed.');
    }
  };

  return (
    <div className="App">
      <h1>Deep Layer Knowing Neural Network</h1>
      <div>
        <label>Input Dimension:</label>
        <input type="number" value={inputDim} onChange={(e) => setInputDim(e.target.value)} />
      </div>
      <div>
        <label>Output Dimension:</label>
        <input type="number" value={outputDim} onChange={(e) => setOutputDim(e.target.value)} />
      </div>
      <div>
        <label>Epochs:</label>
        <input type="number" value={epochs} onChange={(e) => setEpochs(e.target.value)} />
      </div>
      <div>
        <label>Batch Size:</label>
        <input type="number" value={batchSize} onChange={(e) => setBatchSize(e.target.value)} />
      </div>
      <button onClick={handleTrain}>Train Model</button>
      <p>{message}</p>
    </div>
  );
}

export default App;

2. Backend

Create a Flask backend to handle the model training and customization.

Install Flask and other dependencies:

pip install Flask tensorflow

app.py:

from flask import Flask, request, jsonify
from flask_cors import CORS
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Input
import numpy as np

app = Flask(__name__)
CORS(app)

def create_expandable_model(input_dim, output_dim, layer_configs):
    model = Sequential()
    model.add(Input(shape=(input_dim,)))
    
    for i, config in enumerate(layer_configs):
        layer_name = f"{config['name']}_{i}"
        model.add(Dense(units=config['units'], activation='relu', name=layer_name))
    
    model.add(Dense(units=output_dim, activation='softmax', name='Output_Layer'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

layer_configs = [
    {'name': 'Awareness', 'units': 32},
    {'name': 'Recognition', 'units': 32},
    # Add other layers...
]

@app.route('/train', methods=['POST'])
def train_model():
    data = request.json
    input_dim = data.get('input_dim', 100)
    output_dim = data.get('output_dim', 10)
    epochs = data.get('epochs', 50)
    batch_size = data.get('batch_size', 32)
    
    model = create_expandable_model(input_dim, output_dim, layer_configs)
    
    x_train = np.random.rand(1000, input_dim)
    y_train = tf.keras.utils.to_categorical(np.random.randint(output_dim, size=1000), num_classes=output_dim)
    x_val = np.random.rand(200, input_dim)
    y_val = tf.keras.utils.to_categorical(np.random.randint(output_dim, size=200), num_classes=output_dim)
    
    model.fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(x_val, y_val))
    
    model.save('LargeKnowingModel.h5')
    
    return jsonify({'message': 'Model trained and saved successfully as LargeKnowingModel.h5'})

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

3. Deploying with Docker

Dockerfile:

# Use the official Python image.
# https://hub.docker.com/_/python
FROM python:3.9-slim

# Set the working directory
WORKDIR /app

# Install Flask and other dependencies
COPY requirements.txt .
RUN pip install -r requirements.txt

# Copy the rest of the application code
COPY . .

# Make port 5000 available to the world outside this container
EXPOSE 5000

# Run the application
CMD ["python", "app.py"]

requirements.txt

Flask==2.0.1
tensorflow==2.5.0
flask-cors==3.0.10

Build and run the Docker container:

docker build -t deep-layer-saas .
docker run -p 5000:5000 deep-layer-saas

Testing and Using the Application

  1. Start the Frontend:
npm start

Start the Backend:

python app.py
  1. Access the Application:
    • Open a web browser and go to http://localhost:3000 to access the frontend.
    • Customize the neural network parameters and click the “Train Model” button to train and save the model.

Additional Enhancements

  • User Authentication: Implement user authentication using JWT or OAuth.
  • Database Integration: Use a database like PostgreSQL to store user data, training configurations, and results.
  • Real-time Updates: Use WebSockets to provide real-time updates on the training progress.
  • Advanced Customization: Allow users to customize more aspects of the neural network, such as activation functions, optimizers, and additional layers.

This setup provides a basic structure for a customizable SaaS application that allows users to interact with and customize the Deep Layer Knowing Neural Network model.

Sources: GPT 4o

Stay in the NOW with Inner I Network;

Leave a comment

Unknown's avatar

Published by Inner I Net Company/

Be Still, Know. Act once in alignment with, the Inner Intelligence. Shape Reality.

Leave a comment