Projet_innovant_IA/solutions/test_kaggle.py
Clémentine Bonneau a81f142495 Ajout solutions
2021-12-21 12:54:05 +01:00

29 lines
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991 B
Python

data_dir_test = data_dir+'test/'
N_test = len(os.listdir(data_dir_test+"/test"))
test_datagen = kpi.ImageDataGenerator(rescale=1. / 255)
test_generator = test_datagen.flow_from_directory(
data_dir_test,
#data_dir_sub+"/train/",
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode=None,
shuffle=False)
test_prediction = model_VGG_LastConv_fcm.predict_generator(test_generator, N_test // batch_size)
images_test = [data_dir_test+"/test/"+k for k in os.listdir(data_dir_test+"/test")][:9]
x_test = [kpi.img_to_array(kpi.load_img(image_test))/255 for image_test in images_test] # this is a PIL image
fig = plt.figure(figsize=(10,10))
for k in range(9):
ax = fig.add_subplot(3,3,k+1)
ax.imshow(x_test[k], interpolation='nearest')
pred = test_prediction[k]
if pred >0.5:
title = "Probabiliy for dog : %.1f" %(pred*100)
else:
title = "Probabiliy for cat : %.1f" %((1-pred)*100)
ax.set_title(title)
plt.show()