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validate_model.py
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validate_model.py
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import numpy as np
import cv2
from dataset_loader import DatasetLoader
from constants import *
from keras.models import load_model
from quiver_engine.server import launch
# Load dataset
# Comment/uncomment to select dataset to use
dataset = DatasetLoader()
# JAFFE
# dataset.jaffe_load_from_save()
# classes = JAFFE_EMOTIONS
# num_classes = len(classes)
# CK Extended
# dataset.ck_extended_load_from_save()
# classes = CK_EXTENDED_EMOTIONS
# num_classes = len(classes)
# FERPlus (select one)
# dataset.fer_2013_small_load_from_save() # SMALL
# dataset.fer_2013_medium_load_from_save() # MEDIUM
dataset.fer_2013_load_from_save() # FULL
classes = FER_2013_EMOTIONS
num_classes = len(classes)
x_train, x_test = dataset.images, dataset.images_test
y_train, y_test = dataset.labels, dataset.labels_test
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# Load model
model = load_model(MODELS_PATH + 'model_fer_2013_full_vgg_16_1.h5')
print ('[+] Model loaded')
count = 0
result = model.predict(x_test)
for i in range(len(result)):
if (classes[np.argmax(result[i])] == classes[np.argmax(y_test[i])]):
count += 1
print ('[+] Accuracy: ', count / len(result))