You can use model.predict()
to predict the class of a single image as follows [doc]:
# load_model_sample.py
from keras.models import load_model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
import os
def load_image(img_path, show=False):
img = image.load_img(img_path, target_size=(150, 150))
img_tensor = image.img_to_array(img) # (height, width, channels)
img_tensor = np.expand_dims(img_tensor, axis=0) # (1, height, width, channels), add a dimension because the model expects this shape: (batch_size, height, width, channels)
img_tensor /= 255. # imshow expects values in the range [0, 1]
if show:
plt.imshow(img_tensor[0])
plt.axis('off')
plt.show()
return img_tensor
if __name__ == "__main__":
# load model
model = load_model("model_aug.h5")
# image path
img_path = '/media/data/dogscats/test1/3867.jpg' # dog
#img_path = '/media/data/dogscats/test1/19.jpg' # cat
# load a single image
new_image = load_image(img_path)
# check prediction
pred = model.predict(new_image)
In this example, a image is loaded as a numpy
array with shape (1, height, width, channels)
. Then, we load it into the model and predict its class, returned as a real value in the range [0, 1] (binary classification in this example).