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How to train pre trained model (MNIST) tensorflow

I have a project with Fashion MNIST, which predicts clothes from uploaded images, and I want to make some improvements with it. Is it possible to modify my project that it will train automatically after each uploaded and prediction?

You can train your model manually by using the transfer learning technique(Transfer learning is a method of reusing an already trained model for another task).
Instantiate a base model and load pre-trained weights into it.
Freeze all layers in the base model by setting trainable = False.
Create a new model on top of the output of one (or several) layers
from the base model. Train your new model on your new dataset.
Please refer to this gist for working code example. Thank You.

Tensorflow Object Detection API Untrained Faster-RCNN Model

I am currently trying to build an Object Detector using the the Tensorflow Object Detection API with python. I have managed to retrain the faster-rcnn model by following the instructions posted here and here
However, training time is considerably long as I understand that I am. I understand that I am using transfer learning as opposed to training a faster-rcnn model from scratch. I am wondering if there is anyway to download an untrained faster-rcnn model and train it from scratch (end-to-end) instead of having to recourse to transfer-learning.
I am familiar with the advantages of transfer learning, however, my object detector is aimed at being quickly trainable, narrow in scope, and trained on letters as opposed to objects, so I do not think transfer learning is the best route.
I beleive solving this will have something to do with the pipeline.config file, particulary in this part:
fine_tune_checkpoint: "PATH/TO/PRETRAINED/model.ckpt"
from_detection_checkpoint: true
num_steps: 200000
But I am not sure how to specify that there is no fine_tune_checkpoint

To train your own model from scratch do the following:
Comment out the following lines
# fine_tune_checkpoint: <YOUR PATH>
# from_detection_checkpoint: true
Remove your downloaded pretrained model or rename its path in case you followed the tutorial.
You don't have to download an "empty" model. Instead you can specify your own weight initialization in the config file, e.g., as done here: How to initialize weight for convolution layers in Tensorflow Object Detection API?

Is it possible to add another dataset on my trained model in tensorflow?

I already trained a custom person detector which doesn't work well on detecting perons on aerial footage which is because my dataset lacks on aerial images of a person. Can I continue the training using the latest checkpoint and add another dataset (different tfrecord file) for my model or do I have to restart the training using the updated dataset?
I retrained the Inception model to detect persons only since there no other way to remove the other 89 objects from the pretrained model.

You can definitely start from a checkpoint using another dataset. However, it might not be a good idea to train only on a subset of your data due to the tendency of neural nets to forget what they'e already learned (a problem known as catastrophic forgetting). It's probably a better idea to create a new dataset that includes both your old and new data, and either pick up from the checkpoint using those data (similar to how you fine-tuned the Inception model), or start the training process over.

Image Retrain Inception only check the own specific category not tensor dataset

I have retrained the inception model from my data set of traffic sign.Its working fine but when I am trying to check other image e.g panda it's resulting with the name of traffic sign with some probabilities.I don't understand why its doing it.I need both tensor-flow data-set and my own category too.
My steps:
I have installed the python 3.5.2 in windows 7
I installed tensor-flow with
pip --install tensorflow
I download these two files retrain.py to train my data and label_image.py to check image.
Files downloaded from:
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/image_retraining

You have misunderstood the fundamentals of transfer learning wrt this image retraining program.
In the image retraining program you are referencing, you are taking the inception CNN model that is already pretraining on the imageNET dataset. You are then retraining the final classification layers on your NEW classes and data.
The transfer learning occurs because you are retaining all the learnt feature extraction filters etc. in the early layers and you are just reclassifying the activations of those layers to new classes based on your new dataset. This means you are replacing the classification part with a new one. AFAIK there is no way to simply add classes to a CNN model via transfer learning because you have already trained a softmax layer (for example) with the classification distribution for each class.
To achieve what you are suggesting will require you to retrain the final layers of inception with the original dataset PLUS your additional data. This will take a long time due to the size of imageNET.
I would re-evaluate whether you actually need to be able to utilise all these classes in your application or whether it is sufficient to just have your traffic signs etc.
You can learn more about the program at the tensorflow tutorial here.

retrain a saved model in scikit-learn

I am a newbie in machine learning and currently working with scikit-learn python library.I want to know if there is a way to add more training data to a already trained neural network.For example,I am loading a trained neural net instance from a pickle file.Now If I get a wrong prediction,I want to add it to my training data.is that possible?