def create_dataset():
print('criando dataset')
data = ImageClassifierDataLoader.from_folder('./images_dataset')
train_data, rest_data = data.split(0.8)
validation_data, test_data = rest_data.split(0.5)
model = image_classifier.create(train_data,
validation_data=validation_data,
epochs=10)
model.summary()
model.export(export_dir='model/', export_format=ExportFormat.LABEL)
model.export(export_dir='model/')
Tensorflow Lite Model Maker ile Uygulama için Hazır Edilmesi (Mobil Kullanım için)
pip install tflite-model-maker
import tensorflow as tf
assert tf.__version__.startswith('2')
from tflite_model_maker import configs
from tflite_model_maker import ExportFormat
from tflite_model_maker import image_classifier
from tflite_model_maker import ImageClassifierDataLoader
from tflite_model_maker import model_spec
import matplotlib.pyplot as plt
# Eğitim, Değerlendirme ve Test Verilerinin Yüklenmesi
training_data = ImageClassifierDataLoader.from_folder('/content/drive/MyDrive/onayli_onaysiz_512x512/training/')
validation_data = ImageClassifierDataLoader.from_folder('/content/drive/MyDrive/onayli_onaysiz_512x512/validation/')
test_data = ImageClassifierDataLoader.from_folder('/content/drive/MyDrive/onayli_onaysiz_512x512/test/')
print(len(validation_data))
print(len(test_data))
# Modelin Eğitilmesi
model = image_classifier.create(training_data, validation_data=validation_data, epochs=10)
# Modelin Test Verileriye Onaylanması
loss, accuracy = model.evaluate(test_data)
# Tensorflow Lite Modelin Yüklenmesi ve bu Dosya Android Studio tarafından kullanılabilecektir
model.export(export_dir='.')
seed=12,
image_size=(imgHeight, imgWidth),
batch_size=batchSize)
# Save labels
classNames = trainDataset.class_names
print("Classes names:%s" % classNames)
np.savetxt(labelsPath, classNames, '%s', delimiter='\n')
print("Control class names:")
for s in classNames:
print(s, end=', ')
print()
print("Control labels save to path: %s" % labelsPath)
# Load data
data = ImageClassifierDataLoader.from_folder(path)
trainData, testData = data.split(0.62)
trainData = data
# Create model
model = image_classifier.create(trainDataset)
model.summary()
# Evaluate the model
loss, acc = model.evaluate(testData)
print("Model accuracy: %.4f" % acc)
print("Model loss: %.4f" % loss)
# Export model
model.export(modelPath)
# Tensorflow initialization
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
# Initialize dataset
folder_name = r"trash_dataset_resized"
data = ImageClassifierDataLoader.from_folder(folder_name)
# Create train, validation, test splits
train_data, rest_data = data.split(0.7)
validation_data, test_data = rest_data.split(0.5)
# Plot 25 elements from dataset
plt.figure(figsize=(10, 10))
for i, (image, label) in enumerate(data.gen_dataset().unbatch().take(25)):
plt.subplot(5, 5, i + 1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
plt.imshow(image.numpy(), cmap=plt.cm.gray)
plt.xlabel(data.index_to_label[label.numpy()])
plt.savefig("dataset_sample.png")