How to predict from saved model in Keras ?

Question

I have trained an image classifier using keras and it gave a very good accuracy. I've saved the model using the save() and saved it using the h5 format. How can I make a prediction using the model?

The code is:

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

classifier = Sequential()

classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation =   'relu'))

classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))
classifier.add(Flatten())

classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = train_datagen.flow_from_directory('training_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
test_set = test_datagen.flow_from_directory('test_set',
target_size = (64, 64),
batch_size = 32,
class_mode = 'binary')
classifier.fit_generator(training_set,
steps_per_epoch = 8000,
epochs = 5,
validation_data = test_set,
validation_steps = 2000)
classifier.save('classifier.h5')

Thanks in Advance..!!

Answer 1

The first step is to import your model using load_model method.

from keras.models import load_model
model = load_model('my_model.h5')

Then you have to compile the model in order to make predictions.

model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

Now you can predict results for a new entry image.

from keras.preprocessing import image

test_image = image.load_img(imagePath, target_size = (64, 64)) 
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)

#predict the result
result = model.predict(test_image)