This repository contains the Machine Learning Model training code as well as the trained model deployment to Android app code. The model deployment is done using TF-Mobile and TF-Lite.
To load and test the TFLite model locally, use the following code:
import numpy as np
import tensorflow as tf
# Load TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test model on random input data.
input_shape = input_details[0]['shape']
input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)
interpreter.set_tensor(input_details[0]['index'], input_data)
interpreter.invoke()
# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
print(output_data)
Source: https://www.tensorflow.org/lite/guide/inference#load_and_run_a_model_in_python
1. Tensorflow 1.13
2. Jupyter Notebook / PyCharm CE
3. Android Studio
4. Python 3+
| Name | Aim | Status |
|---|---|---|
| Kotlin Basics | Basics of Kotlin language in Android Studio. | Completed |
| BasicUI | Just playing around app to get familiar with UI design in Android. | Completed |
| TensorFlow Basics | Basics of TensorFlow in Python. | Completed |
| TensorFlow Estimator API | Basics of TensorFlow Estimator API and creating a custom Estimator API. | Completed |
| Linear-Regression | Linear Regression model in TensorFlow with Android app code. | Completed |
| Handwritten_Digit_Recognition | Linear Regression Model in TensorFlow for MNIST Image classification on Android. | Completed |
| Artistic-Style-Transfer | Artistic Style Transfer on Image on Android. | Completed |
| Weather-Prediction | Android app with TensorFlow code for making weather predictions. | Completed |