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create_model_try_scale.py
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create_model_try_scale.py
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import logging
logging.getLogger("tensorflow").setLevel(logging.DEBUG)
import tensorflow as tf
from tensorflow import keras
import numpy as np
import pathlib
import os
print(tf.__version__)
print("WARNING: This script is only tested on tf==2.2.0.")
def representative_dataset():
for _ in range(100):
data = np.random.rand(1, 28, 28)
yield [data.astype(np.float32)]
# Load MNIST dataset
mnist = keras.datasets.mnist
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
#train_images = train_images[:1,:,:]
#train_labels = train_labels[1]
#test_images = test_images[:1,:,:]
#test_labels = test_labels[1]
# Normalize the input image so that each pixel value is between 0 to 1.
train_images = train_images / 255.0
test_images = test_images / 255.0
# Define the model architecture
model = keras.Sequential([
keras.layers.InputLayer(input_shape=(28, 28)),
keras.layers.Reshape(target_shape=(28, 28, 1)),
keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation=tf.nn.relu),
keras.layers.MaxPooling2D(pool_size=(2, 2)),
keras.layers.Flatten(),
keras.layers.Dense(1)
])
# Train the digit classification model
model.compile(optimizer='adam',
loss=keras.losses.CategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(
train_images[:1,:,:],
train_labels[:1],
epochs=1
#validation_data=(test_images, test_labels)
)
# ===== Convert to a TensorFlow Lite model =====
converter = tf.lite.TFLiteConverter.from_keras_model(model)
tflite_model = converter.convert()
tflite_models_dir = pathlib.Path("/tmp/mnist_tflite_models/")
tflite_models_dir.mkdir(exist_ok=True, parents=True)
tflite_model_file = tflite_models_dir/"mnist_model.tflite"
tflite_model_file.write_bytes(tflite_model)
converter.experimental_new_quantizer = False
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_dataset
converter.inference_input_type = tf.uint8 # or tf.uint8
converter.inference_output_type = tf.uint8 # or tf.uint8
tflite_model = converter.convert()
print("convert done.")
tflite_model_file = tflite_models_dir/"mnist_model_quant.tflite"
tflite_model_file.write_bytes(tflite_model)
# ===== Run the Tensorflow Lite models =====
interpreter = tf.lite.Interpreter(model_path=str(tflite_model_file))
interpreter.allocate_tensors()
test_image = np.expand_dims(test_images[0], axis=0).astype(np.float32)
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
interpreter.set_tensor(input_index, test_image)
interpreter.invoke()
predictions = interpreter.get_tensor(output_index)
# helper function to evaluate the TF Lite model using "test" dataset.
def evaluate_model(interpreter):
input_index = interpreter.get_input_details()[0]["index"]
output_index = interpreter.get_output_details()[0]["index"]
# Run predictions on every image in the "test" dataset.
prediction_digits = []
for test_image in test_images:
# Pre-processing: add batch dimension and convert to float32 to match with
# the model's input data format.
test_image = np.expand_dims(test_image, axis=0).astype(np.float32)
interpreter.set_tensor(input_index, test_image)
# Run inference.
interpreter.invoke()
# Post-processing: remove batch dimension and find the digit with highest
# probability.
output = interpreter.tensor(output_index)
digit = np.argmax(output()[0])
prediction_digits.append(digit)
# Compare prediction results with ground truth labels to calculate accuracy.
accurate_count = 0
for index in range(len(prediction_digits)):
if prediction_digits[index] == test_labels[index]:
accurate_count += 1
accuracy = accurate_count * 1.0 / len(prediction_digits)
return accuracy
#print(evaluate_model(interpreter))
# NOTE: This quantization mode is an experimental post-training mode,
# it does not have any optimized kernels implementations or
# specialized machine learning hardware accelerators. Therefore,
# it could be slower than the float interpreter.
#print(evaluate_model(interpreter))
os.system("edgetpu_compiler "+str(tflite_model_file)+" -o ./ -s")