load saved model
# compile the model in order to make predictions
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])load saved model
# compile the model in order to make predictions
model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])load saved model
from keras.models import load_model
model = load_model('my_model.h5')load saved model
#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)Copyright © 2021 Codeinu
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