![]() ![]() This requires that a project is created first, and the Python script with a. Using an Integrated Development Environment (IDE)Īnother option is to run the Python script from an IDE. Print('Prediction:', decode_predictions(pred_vgg, top=int(top_guesses))) # Generate a prediction for the test image # Display the image to check that it has been correctly resized ![]() Img_resized = image.load_img(image_path, target_size=(224, 224)) # Load the image, resized according to the model target size # Read the arguments passed to the interpreter when invoking the script Vgg16_model = vgg16.VGG16(weights='imagenet') # Load the VGG16 model pre-trained on the ImageNet dataset Try typing the following code into a cell in Jupyter Notebook:įrom import vgg16įrom 16 import preprocess_input, decode_predictionsįrom import image But this means your code stays inside the Jupyter notebook and cannot be accessed elsewhere, such as using the command line as above. One way of running a Python script through the Jupyter Notebook interface is to simply add the code to a “cell” in the notebook. The Jupyter Notebook offers an interactive computing environment that can help us achieve this. ![]() We might be checking the correctness of any pre-processing applied to the input image before feeding it into a neural network or visualizing the result that the neural network produces. However, when we are working with images, it is often desirable to generate a visual output too. Running a Python script from the command-line interface is a straightforward option if your code generates a string output and not much else. We will have another post about the use of the debugger and profilers. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |