# -*- coding:utf-8 -*-
import keras
from keras.preprocessing.image import ImageDataGenerator,array_to_img
from keras.datasets import cifar10
import numpy as np
import os
import pickle
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from keras.optimizers import Adam
from data_input.data_input import getDataGenerator
from model.DenseNet import createDenseNet
from cifar10_train import check_point_file
from cifar10_train import nb_classes
from cifar10_train import img_dim
from cifar10_train import densenet_depth
from cifar10_train import densenet_growth_rate
batch_size = 128
def eval_model():
model = createDenseNet(nb_classes=nb_classes,img_dim=img_dim,depth=densenet_depth,
growth_rate = densenet_growth_rate)
model.load_weights(check_point_file)
optimizer = Adam()
model.compile(loss='categorical_crossentropy',optimizer=optimizer,metrics=['accuracy'])
label_list_path = 'datasets/cifar-10-batches-py/batches.meta'
keras_dir = os.path.expanduser(os.path.join('~', '.keras'))
datadir_base = os.path.expanduser(keras_dir)
if not os.access(datadir_base, os.W_OK):
datadir_base = os.path.join('/tmp', '.keras')
label_list_path = os.path.join(datadir_base, label_list_path)
with open(label_list_path, mode='rb') as f:
labels = pickle.load(f)
(x_train,y_train),(x_test,y_test) = cifar10.load_data()
x_test = x_test.astype('float32')
x_test /= 255
y_test= keras.utils.to_categorical(y_test, nb_classes)
test_datagen = getDataGenerator(train_phase=False)
test_datagen = test_datagen.flow(x_test,y_test,batch_size = batch_size,shuffle=False)
# Evaluate model with test data set and share sample prediction results
evaluation = model.evaluate_generator(test_datagen,
steps=x_test.shape[0] // batch_size,
workers=4)
print('Model Accuracy = %.2f' % (evaluation[1]))
counter = 0
figure = plt.figure()
plt.subplots_adjust(left=0.1,bottom=0.1, right=0.9, top=0.9,hspace=0.5, wspace=0.3)
for x_batch,y_batch in test_datagen:
predict_res = model.predict_on_batch(x_batch)
for i in range(batch_size):
actual_label = labels['label_names'][np.argmax(y_batch[i])]
predicted_label = labels['label_names'][np.argmax(predict_res[i])]
if actual_label != predicted_label:
counter += 1
pics_raw = x_batch[i]
pics_raw *= 255
pics = array_to_img(pics_raw)
ax = plt.subplot(25//5, 5, counter)
ax.axis('off')
ax.set_title(predicted_label)
plt.imshow(pics)
if counter >= 25:
plt.savefig("./wrong_predicted.jpg")
break
if counter >= 25:
break
print("Everything seems OK...")
if __name__ =='__main__':
eval_model()
