
import torch
import torch.nn as nn
import torch.optim as optim

import numpy as np
import tensorflow as tf
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, BertTokenizer, BertModel, ViTFeatureExtractor, ViTModel

# ========================= HYPERPARAMETERS =========================

NUM_EPOCHS = 10
BATCH_SIZE = 32
NUM_CLASSES = 10
NUM_FRAMES = 100
NUM_MFCC_FEATURES = 13
NUM_TEXT_FEATURES = 1000
IMAGE_HEIGHT = 128
IMAGE_WIDTH = 128
NUM_CHANNELS = 3

VOICE_INPUT_SHAPE = (NUM_FRAMES, NUM_MFCC_FEATURES)
TEXT_INPUT_SHAPE = (NUM_TEXT_FEATURES,)
SIGHT_INPUT_SHAPE = (IMAGE_HEIGHT, IMAGE_WIDTH, NUM_CHANNELS)

# ========================= TRANSFORMERS =========================

def transform_voice(normalized_audio):
    processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
    input_values = processor(normalized_audio, return_tensors="pt").input_values
    with torch.no_grad():
        logits = model(input_values).logits
    return logits

def transform_text(tokenized_text):
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertModel.from_pretrained('bert-base-uncased')
    inputs = tokenizer(tokenized_text, return_tensors="pt")
    outputs = model(**inputs)
    return outputs.last_hidden_state

def transform_sight(loaded_image):
    feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
    model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
    inputs = feature_extractor(loaded_image, return_tensors="pt")
    outputs = model(**inputs)
    return outputs.last_hidden_state

# ========================= FUSION MECHANISMS =========================

def early_fusion(voice_features, text_features, sight_features):
    return tf.concat([voice_features, text_features, sight_features], axis=-1)

def late_fusion(voice_output, text_output, sight_output):
    return 0.3 * voice_output + 0.3 * text_output + 0.4 * sight_output

# ========================= CUSTOM LOSS FUNCTION =========================

def custom_loss(y_true, y_pred):
    voice_loss = tf.keras.losses.MeanSquaredError()(y_true[0], y_pred[0])
    text_loss = tf.keras.losses.MeanSquaredError()(y_true[1], y_pred[1])
    sight_loss = tf.keras.losses.MeanSquaredError()(y_true[2], y_pred[2])
    fusion_loss = voice_loss + text_loss + sight_loss
    return fusion_loss + 0.1 * fusion_loss

# ========================= MULTIMODAL MODEL =========================

def build_multimodal_model(input_shape):
    input_layer = tf.keras.layers.Input(shape=input_shape)
    x = tf.keras.layers.Dense(128, activation='relu')(input_layer)
    x = tf.keras.layers.Dense(64, activation='relu')(x)
    output_layer = tf.keras.layers.Dense(NUM_CLASSES, activation='softmax')(x)
    model = tf.keras.Model(inputs=input_layer, outputs=output_layer)
    return model

# Learning Rate Schedule
initial_lr = 0.001
decay_steps = 10000
decay_rate = 0.9
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(initial_lr, decay_steps, decay_rate, staircase=True)

# Optimizer
optimizer = tf.keras.optimizers.Adam(learning_rate=lr_schedule)

# Compile Model
model = build_multimodal_model(early_fusion_output.shape[1])
model.compile(optimizer=optimizer, loss=custom_loss, metrics=['accuracy'])

# Model Summary
model.summary()

BUILD_ONE.py
rjslvn/AI-Frameworks-Insights
# Import Required Libraries
import numpy as np
import tensorflow as tf
import torch
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, BertTokenizer, BertModel, ViTFeatureExtractor, ViTModel
from gensim.models import Word2Vec
from sklearn.feature_extraction.text import TfidfVectorizer
import librosa
from tensorflow.keras.layers import Input, Dense, Concatenate
from tensorflow.keras.models import Model
from sklearn.metrics import f1_score
from tensorflow.keras.callbacks import Callback
import logging

# Initialize Logging
logging.basicConfig(level=logging.INFO)

# Hyperparameters
num_epochs = 10
batch_size = 32
num_classes = 10
num_frames = 100
num_mfcc_features = 13
num_text_features = 1000
image_height = 128
image_width = 128
num_channels = 3

# Input Shapes
voice_input_shape = (num_frames, num_mfcc_features)
text_input_shape = (num_text_features,)
sight_input_shape = (image_height, image_width, num_channels)

# Download Models and Dependencies
def download_models_and_deps():
    Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
    Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
    BertTokenizer.from_pretrained('bert-base-uncased')
    BertModel.from_pretrained('bert-base-uncased')
    ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
    ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')

# Custom Loss Function
def custom_loss(y_true, y_pred):
    return tf.keras.losses.MeanSquaredError()(y_true, y_pred)

# Model Architecture
def build_multimodal_model(input_shape):
    input_layer = Input(shape=input_shape)
    x = Dense(128, activation='relu')(input_layer)
    x = Dense(64, activation='relu')(x)
    output_layer = Dense(1, activation='sigmoid')(x)
    model = Model(inputs=input_layer, outputs=output_layer)
    return model

# Transform Functions
def transform_voice(normalized_audio):
    # Implementation here
    pass

def transform_text(tokenized_text):
    # Implementation here
    pass

def transform_sight(loaded_image):
    # Implementation here
    pass

# Callbacks
class F1ScoreCallback(Callback):
    # Implementation here
    pass

# Continue Training
def continue_training():
    # Implementation here
    pass

# Main Execution
if __name__ == "__main__":
    download_models_and_deps()
    continue_training()
    # Further code for testing and deployment
# Transform Functions

# Transform voice data using Wav2Vec2
def transform_voice(normalized_audio):
    processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
    model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")
    input_values = processor(normalized_audio, return_tensors="pt").input_values
    with torch.no_grad():
        logits = model(input_values).logits
    return logits

# Transform text data using BERT
def transform_text(tokenized_text):
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertModel.from_pretrained('bert-base-uncased')
    inputs = tokenizer(tokenized_text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state

# Transform sight data using ViT
def transform_sight(loaded_image):
    feature_extractor = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
    model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
    inputs = feature_extractor(loaded_image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    return outputs.last_hidden_state

multimodal.py
rjslvn/AI-Frameworks-Insights