mirror of https://github.com/tracel-ai/burn.git
83 lines
3.0 KiB
Python
83 lines
3.0 KiB
Python
# Originally copied and modified from:
|
|
# https://github.com/tensorflow/tensorflow/blob/e0b19f6ef223af40e2e6d1d21b8464c1b2ebee8f/tensorflow/lite/micro/examples/hello_world/train/train_hello_world_model.ipynb
|
|
# under the following license: Apache License 2.0
|
|
import os
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from tensorflow import keras
|
|
import tf2onnx
|
|
import onnx
|
|
import math
|
|
from pathlib import Path
|
|
|
|
|
|
def main():
|
|
# Define paths to model files
|
|
MODELS_DIR = '../src/model/'
|
|
os.makedirs(MODELS_DIR, exist_ok=True)
|
|
MODEL_ONNX = MODELS_DIR + 'sine.onnx'
|
|
|
|
np.random.seed(1)
|
|
|
|
# Number of sample datapoints
|
|
SAMPLES = 1000
|
|
|
|
# Generate a uniformly distributed set of random numbers in the range from
|
|
# 0 to 2π, which covers a complete sine wave oscillation
|
|
x_values = np.random.uniform(
|
|
low=0, high=2*math.pi, size=SAMPLES).astype(np.float32)
|
|
|
|
# Shuffle the values to guarantee they're not in order
|
|
np.random.shuffle(x_values)
|
|
|
|
# Calculate the corresponding sine values
|
|
y_values = np.sin(x_values).astype(np.float32)
|
|
|
|
# Add a small random number to each y value to mimic real world data
|
|
y_values += 0.1 * np.random.randn(*y_values.shape)
|
|
|
|
# We'll use 60% of our data for training and 20% for testing. The remaining
|
|
# 20% will be used for validation. Calculate the indices of each section.
|
|
TRAIN_SPLIT = int(0.6 * SAMPLES)
|
|
TEST_SPLIT = int(0.2 * SAMPLES + TRAIN_SPLIT)
|
|
|
|
# Use np.split to chop our data into three parts.
|
|
# The second argument to np.split is an array of indices where the data
|
|
# will be split. We provide two indices, so the data will be divided into
|
|
# three chunks.
|
|
x_train, x_test, x_validate = np.split(x_values, [TRAIN_SPLIT, TEST_SPLIT])
|
|
y_train, y_test, y_validate = np.split(y_values, [TRAIN_SPLIT, TEST_SPLIT])
|
|
|
|
# Double check that our splits add up correctly
|
|
assert (x_train.size + x_validate.size + x_test.size) == SAMPLES
|
|
|
|
model = tf.keras.Sequential()
|
|
|
|
# First layer takes a scalar input and feeds it through 16 "neurons". The
|
|
# neurons decide whether to activate based on the 'relu' activation
|
|
# function.
|
|
model.add(keras.layers.Dense(16, activation='relu', input_shape=(1,)))
|
|
|
|
# The new second layer may help the network learn more complex
|
|
# representations
|
|
model.add(keras.layers.Dense(16, activation='relu'))
|
|
|
|
# Final layer is a single neuron, since we want to output a single value
|
|
model.add(keras.layers.Dense(1))
|
|
|
|
# Compile the model using a standard optimizer and loss function for
|
|
# regression
|
|
model.compile(optimizer='adam', loss='mse', metrics=['mae'])
|
|
|
|
history = model.fit(x_train, y_train, epochs=500, batch_size=64,
|
|
validation_data=(x_validate, y_validate))
|
|
|
|
# Use from_function for tf functions
|
|
onnx_model, _ = tf2onnx.convert.from_keras(model, opset=16)
|
|
onnx.save(onnx_model, MODEL_ONNX)
|
|
print("Onnx model generated at", Path(MODEL_ONNX).absolute())
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|