How to predict input image using trained model in Keras?
Solution 1:
If someone is still struggling to make predictions on images, here is the optimized code to load the saved model and make predictions:
# Modify 'test1.jpg' and 'test2.jpg' to the images you want to predict on
from keras.models import load_model
from keras.preprocessing import image
import numpy as np
# dimensions of our images
img_width, img_height = 320, 240
# load the model we saved
model = load_model('model.h5')
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# predicting images
img = image.load_img('test1.jpg', target_size=(img_width, img_height))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict_classes(images, batch_size=10)
print classes
# predicting multiple images at once
img = image.load_img('test2.jpg', target_size=(img_width, img_height))
y = image.img_to_array(img)
y = np.expand_dims(y, axis=0)
# pass the list of multiple images np.vstack()
images = np.vstack([x, y])
classes = model.predict_classes(images, batch_size=10)
# print the classes, the images belong to
print classes
print classes[0]
print classes[0][0]
Solution 2:
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).
Solution 3:
keras predict_classes (docs) outputs A numpy array of class predictions. Which in your model case, the index of neuron of highest activation from your last(softmax) layer. [[0]]
means that your model predicted that your test data is class 0. (usually you will be passing multiple image, and the result will look like [[0], [1], [1], [0]]
)
You must convert your actual label (e.g. 'cancer', 'not cancer'
) into binary encoding (0
for 'cancer', 1
for 'not cancer') for binary classification. Then you will interpret your sequence output of [[0]]
as having class label 'cancer'