saver.restore(sess, load_model_path)
validation_images, validation_labels = dataset.images, dataset.labels
validation_images = changed_shape(validation_images, [
len(validation_images), sub_Config.IMAGE_W, sub_Config.IMAGE_W, 1
])
validation_accuracy, logits = sess.run([accuracy_tensor, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
_, _, _, error_indexs, error_record = calculate_acc_error(
logits=np.argmax(logits, 1), label=validation_labels, show=True)
print 'accuracy is %g' % \
(validation_accuracy)
return error_indexs, error_record
if __name__ == '__main__':
dataset = ValDataSet(
data_path=
'/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val',
phase='ART',
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
shuffle=False)
error_indexs, error_record = val(
dataset,
load_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/LeNet/model_finetuing/model_art/',
save_model_path=None)
dataset.show_error_name(error_indexs, error_record)
len(validation_images), sub_Config.IMAGE_W, sub_Config.IMAGE_W, 1
])
validation_accuracy, validation_loss, logits = sess.run(
[accuracy_tensor, loss_, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
_, _, _, error_indexs, error_record = calculate_acc_error(
logits=np.argmax(logits, 1), label=validation_labels, show=True)
print 'validation loss value is %g, accuracy is %g' % \
(validation_loss, validation_accuracy)
return error_indexs, error_record
if __name__ == '__main__':
phase_name = 'ART'
state = ''
val_dataset = ValDataSet(
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path=
'/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val')
error_indexs, error_record = val(
val_dataset,
load_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/ResNet/models/fine_tuning/2/'
)
val_dataset.show_error_name(error_indexs, error_record, copy=False)