We need to remove the last classification layer to get the (2048, ) dimensional feature vector from InceptionV3 model. We can directly import this model from Keras.applications module. Here I’m using the InceptionV3 model which has been trained on Imagenet dataset that had 1000 different classes to classify. For this task, I’m using transfer learning i.e, we use a pre-trained model that has been already trained on large datasets and extract the features from these models and use them for our work. So we need to convert the image into an encoding so that the machine can understand the patterns in it.
![imagegif sourcemaker ndt7edl imagegif sourcemaker ndt7edl](http://dmannies.org/images/projects/target/DataFileGenerator0.gif)
Now we will give an image as an input to our model but unlike humans, machines cannot understand the image by seeing them. Step 6: Extract the feature vector from all images 'startseq little girl in pink dress going into wooden cabin endseq']
![imagegif sourcemaker ndt7edl imagegif sourcemaker ndt7edl](https://media.giphy.com/media/RxtIoH1phfuOQ/giphy.gif)
'startseq little girl climbing the stairs to her playhouse endseq', 'startseq little girl climbing into wooden playhouse endseq', 'startseq girl going into wooden building endseq', ['startseq child in pink dress is climbing up set of stairs in an entry way endseq', The path or an open stream resource (which is automatically closed after this function returns) to save the file to.
![imagegif sourcemaker ndt7edl imagegif sourcemaker ndt7edl](https://66.media.tumblr.com/d237f81d6901129605a76804bfea2e9c/b8a62617bcf7d65b-08/s250x400/e60a15a0f463b35703633550f204ae0a13c1d65f.gif)
Removing stop words with NLTK in Python.ISRO CS Syllabus for Scientist/Engineer Exam.ISRO CS Original Papers and Official Keys.GATE CS Original Papers and Official Keys.