How to Deploy a Trainable Model on iPhone (Step 1: Generate model)

Paul Xiong
5 min readNov 27, 2024

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Deploying a trainable machine learning model on an iPhone involves a series of steps to ensure it operates efficiently within the device’s hardware and software constraints. Here’s a concise guide to get we started:

This code implements a hybrid model that combines ResNet50 for image feature extraction with a MobileBERT-like transformer architecture for sequence modeling. Here’s a detailed breakdown:

The code consists of three main components:

  1. TransformerBlock Class:
  • Implements a custom transformer block with multi-head self-attention and feed-forward layers
  • Includes layer normalization and dropout for regularization
  • Uses residual connections to help with gradient flow during training
  1. MobileBERT-like Model Creation:
  • The create_mobilebert_like_model function creates a lightweight transformer model
  • Takes sequence of image embeddings as input (from ResNet50)
  • Processes them through multiple transformer blocks
  • Uses global average pooling and a dense layer for final prediction
  1. Data Preparation and Model Training Pipeline:
  • Uses ResNet50 (in TFLite format) to generate image embeddings
  • Implements a training workflow with:
  • Data preprocessing
  • Model compilation and training
  • Model conversion to TFLite format with different optimization levels
  • Multiple quantization approaches (default and full integer quantization)

The main workflow demonstrates how to:

  • Load and preprocess images
  • Generate embeddings using ResNet50
  • Create training sequences
  • Train the MobileBERT-like model
  • Convert and optimize the model for mobile deployment using TFLite

Key features include:

  • Support for sequence modeling of image features
  • Multiple optimization techniques for mobile deployment
  • Representative dataset generation for quantization
  • Both float32 and int8 quantization options

This architecture could be particularly useful for applications requiring sequential image understanding while maintaining efficiency for mobile deployment.

How to run:

Tow commands to run:

source /Volumes/997G/github/ISAT_with_segment_anything/venv/bin/activate
python models/chatgpt/create\ ResNet50\ +\ MobileBERT-Alike\ Model.py

Screen shot:

Generated files:

import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, LayerNormalization, Dropout, MultiHeadAttention, Layer, GlobalAveragePooling1D
from tensorflow.keras.models import Model

# Custom Transformer Block
class TransformerBlock(Layer):
def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):
super(TransformerBlock, self).__init__()
self.att = MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)
self.ffn = tf.keras.Sequential(
[Dense(ff_dim, activation="relu"), Dense(embed_dim)]
)
self.layernorm1 = LayerNormalization(epsilon=1e-6)
self.layernorm2 = LayerNormalization(epsilon=1e-6)
self.dropout1 = Dropout(rate)
self.dropout2 = Dropout(rate)

def call(self, inputs, training):
attn_output = self.att(inputs, inputs) # Self-attention
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(inputs + attn_output) # Residual + Normalize
ffn_output = self.ffn(out1)
ffn_output = self.dropout2(ffn_output, training=training)
return self.layernorm2(out1 + ffn_output) # Residual + Normalize


# Define MobileBERT-like Model
def create_mobilebert_like_model(sequence_length, embedding_dim, num_heads=4, ff_dim=256, num_transformer_blocks=4):
inputs = Input(shape=(sequence_length, embedding_dim)) # Input: Photo Embeddings
x = inputs

# Add Transformer Blocks
for _ in range(num_transformer_blocks):
x = TransformerBlock(embed_dim=embedding_dim, num_heads=num_heads, ff_dim=ff_dim)(x, training=True)

# Pooling Layer
x = GlobalAveragePooling1D()(x)

# Final Output: Next Photo Embedding Prediction
outputs = Dense(embedding_dim)(x)

model = tf.keras.Model(inputs=inputs, outputs=outputs)
return model


# Integration with ResNet50 Embeddings
def prepare_data_with_resnet50(image_paths, model_path="assets/models/resnet50_no_head_cannot_init.tflite", embedding_dim=2048):
# Load TFLite model
interpreter = tf.lite.Interpreter(model_path=model_path)
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Check the expected input shape
expected_shape = input_details[0]['shape']
print(f"Expected input shape: {expected_shape}")

# Prepare embeddings
embeddings = []
for image_path in image_paths:
# Preprocess image
image = tf.keras.preprocessing.image.load_img(image_path, target_size=(expected_shape[2], expected_shape[3]))
image = tf.keras.preprocessing.image.img_to_array(image)
image = tf.expand_dims(image, axis=0)
image = tf.keras.applications.resnet50.preprocess_input(image)

# Reorder dimensions to match expected shape [batch_size, channels, height, width]
image = tf.transpose(image, perm=[0, 3, 1, 2])

# Ensure the image shape matches the expected input shape
if image.shape != tuple(expected_shape):
raise ValueError(f"Image shape {image.shape} does not match expected shape {expected_shape}")

# Run inference
interpreter.set_tensor(input_details[0]['index'], image)
interpreter.invoke()
embedding = interpreter.get_tensor(output_details[0]['index'])
embeddings.append(embedding.flatten())

return embeddings


# Example Workflow
if __name__ == "__main__":
# Step 1: Load photo paths and prepare embeddings
photo_paths = ["assets/images/1.jpg", "assets/images/2.jpg", "assets/images/3.jpg", "assets/images/4.jpg", "assets/images/5.jpg", "assets/images/6.jpg", "assets/images/7.jpg", "assets/images/8.jpg", "assets/images/9.jpg", "assets/images/10.jpg", "assets/images/11.jpg"] # Replace with actual paths
embeddings = prepare_data_with_resnet50(photo_paths)

# Step 2: Generate training data
sequence_length = 10 # Number of previous photos
embedding_dim = 2048 # Dimension of ResNet50 embeddings

# Ensure batch_size is non-negative
if len(photo_paths) > sequence_length:
batch_size = len(photo_paths) - sequence_length
else:
raise ValueError(f"Number of photo paths ({len(photo_paths)}) must be greater than sequence length ({sequence_length})")

# Generate dummy sequences (for demo purposes, use real data in practice)
X_train = tf.random.normal((batch_size, sequence_length, embedding_dim))
y_train = tf.random.normal((batch_size, embedding_dim))

# Step 3: Train MobileBERT-like Model
model = create_mobilebert_like_model(sequence_length, embedding_dim)
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss="mean_squared_error",
metrics=["mae"]
)

model.fit(X_train, y_train, epochs=1, batch_size=2)

# Save Model for TFLite Conversion
model.save("models/chatgpt/mobilebert_like_model.keras")

# Convert the model to TensorFlow Lite format with training support
converter = tf.lite.TFLiteConverter.from_keras_model(model)

# Enable resource variables to allow training
converter.experimental_enable_resource_variables = True

# (Optional) Include TensorFlow ops if necessary
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS, # Enable TensorFlow Lite ops
tf.lite.OpsSet.SELECT_TF_OPS # Enable TensorFlow ops (if your model uses them)
]

# Convert the model
tflite_model = converter.convert()

# Save the TensorFlow Lite model
with open("models/chatgpt/mobilebert_like_model.tflite", "wb") as f:
f.write(tflite_model)

# Convert the model to a TensorFlow Lite model with optimization
converter = tf.lite.TFLiteConverter.from_keras_model(model)

# Set the optimization flag to optimize for size
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Optionally, specify representative dataset for better quantization
def representative_dataset_gen():
for _ in range(100):
data = tf.random.normal([1, sequence_length, embedding_dim])
yield [data]

converter.representative_dataset = representative_dataset_gen

# Convert the model
tflite_model = converter.convert()

# Save the model to a file
with open('models/chatgpt/mobilebert_like_quantized_model.tflite', 'wb') as f:
f.write(tflite_model)

print("Quantized model saved successfully!")

# Load the quantized model
interpreter = tf.lite.Interpreter(model_path="models/chatgpt/mobilebert_like_quantized_model.tflite")
interpreter.allocate_tensors()

# Print input and output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

print("Input details:", input_details)
print("Output details:", output_details)

# Convert the model to TensorFlow Lite format
converter = tf.lite.TFLiteConverter.from_keras_model(model)

# Apply full integer quantization
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# Specify representative dataset for better quantization
def representative_dataset_gen():
for _ in range(100):
data = tf.random.normal([1, sequence_length, embedding_dim])
yield [data]

converter.representative_dataset = representative_dataset_gen

# Ensure full integer quantization
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.int8 # or tf.uint8
converter.inference_output_type = tf.int8 # or tf.uint8

# Convert the model
tflite_model = converter.convert()

# Save the TensorFlow Lite model
with open("models/chatgpt/mobilebert_like_quantized_model.tflite", "wb") as f:
f.write(tflite_model)

print("Quantized model saved successfully!")

# Load the quantized model
interpreter = tf.lite.Interpreter(model_path="models/chatgpt/mobilebert_like_quantized_model.tflite")
interpreter.allocate_tensors()

# Print input and output details
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

print("Input details:", input_details)
print("Output details:", output_details)

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Paul Xiong
Paul Xiong

Written by Paul Xiong

Predicting the next word (token) is what powers ChatGPT, while predicting the next photo (embedding) forms the foundation of ImageGPT.

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