如何在单个输入上应用张量流 model 并获得实际预测以及如何在单独的脚本中实现 model

Question

Recently I have been learning Tensor Flow, and I have written a few machine learning programs, however, I am wondering in what way can I test the model on a single input and receive the prediction, and not just evaluate the accuracy of the model on a lot of data as you would do using the model.fit() function. I am also wondering how can I then implement the model in a script, that for example gathers data and feeds it into the model automatically to obtain the predictions and then for example plots the results on a graph.

Thanks in advance.

Answer 1

To use your trained model for a single input lets call it y, you must process y to have the same data format your model was trained on. For example lets assume that you trained on model on images of cats and dog. If you model trained properly you should be able to submit a picture of a cat or a dog to it and have it tell you which it is. Now if images were the input used to train the model they had a certain image shape (height,width) and a certain channel format for example RGB or Grayscale etc. So for the image y you want to predict you must ensure its size is the same height and width the model was trained on. If the model was trained on rgb images then y must be an rgb image. one more thing. When using model.predict say for predicting the single image y you will have to account for the fact that model.predict requires that you have the first dimension of y to be the batch_size. For the case of a single image the batch size is 1. So you need to expand the dimensions of y to include the batch size. For an immage the shape of y is (height, width,channels). It doesn't have a batch dimension so you need to add it. You can do that with the y=np.expand_dims(y,axis=0) which will now give y the shape (1, height,width,channels). For example lets assume you trained you model on images of shape (224,224,3) in rgb format. You have an image y you want to classify and say it is a directory my_pics. The code below shows how to handle doing a prediction on image y. Somewhere in your training code you need to have an ordered list called classes. For the dog example the index code for cat might be 0 and the index code for dog then will be 1. So classes would be classes=['cat', 'dog']

model=tf.keras.models.load_model(path where model is stored) # load the trained model
image_path=r'my_pics' # path to image y
y=cv2.imread(image_path) #Note cv2 reads in images as bgr
y=cv2.resize(y, (224,224) # gives y the same shape as the training images
y=cv2.cvtColor(y, cv2.COLOR_BGR2RGB)# convert from bgr to rgb
y=np.expand_dims(y, axis=0) # y has shape (1,224,224,3)
prediction = model.predict(y) # make a prediction on y
print (prediction) # is a list with a probability value for each class 
class_index=np.argmax(prediction # gives index of entry in prediction with highest probability
klass=classes[class_index} #selects the class name from the ordered list of classes
print (class)