I want to load a model trained in ML.NET in Python Keras to try and see the result. The model file is zip formatted and I don't have train data. How can I do that?
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I am a novice in using tensorflow and I built a CNN which is trained and tested both with 80-85% accuracy. I tried to save my trained model using model.save('example.h5'), and download the file using files.download('example.h5')
Afterwards I tried to load it to my flask back-end using model = tf.keras.models.load_model('example.h5').
When I tried using it with random images, it's like the model has never been trained before. Any solutions? thank you
A common reason to this might be that you are normalizing your data on training but not before predicting. Make sure you normalize the input data before you run predictions, if you have trained on normalized data.
I am fairly new to keras and I am trying transfer learning here:
https://www.tensorflow.org/tutorials/images/transfer_learning
My dataset however is not a binary and I have tfrecord file. I can read the file in tensorflow. I do not want to feed the images as an input to the network as the input comes from the pre-trained model. How can I pass the images and labels in the ImageDataGenerator class in Keras.
For anyone that may have this issue in the future. If the pre-train process is all correct. You can use the tf.data API to read and prepare the images for the training and the (image, label) set, can be fed to to the (.fit) method of your model.
look at this great Post to get familiar how to read the tfrecord file:
https://medium.com/#moritzkrger/speeding-up-keras-with-tfrecord-datasets-5464f9836c36
I built a CNN model for image classification using the Keras library. However training takes many hours. Once I trained my model, how can I use it without training once more? I mean after I trained my model, I want to use it many times.
Because I will use my model in android studio.
Any help is appreciated
Thank YOU...
EDIT
When I wrote this question, I did not know the save model and load.model, in the answers you see the appropriate usage of them.
You can easily save your model after the training process by using:
model.save('my_model.h5')
you can later load that model by using:
model = load_model('my_model.h5')
for more details have a look at the documentation: https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model
I am training a keras model and saving the weights into a JSON file like so:
with open('weigths.json', 'w') as f:
json.dump(model.get_weigths())
Now I want to load the weights and rebuild the keras model so that I can do testing and prediction
How can I do that?
To save a model, you should use the dedicated function keras.model.save(filepath) and load it again with keras.models.load_model(filepath) as explained here.
I have trained a model using CNTK, lets call simple.dnn
now for the phase of testing I do not want to install CNTK on windows,but use trained model with python. How can I use trained model (weights,...) for testing using python?
You can use the load_model function, see https://www.cntk.ai/pythondocs/cntk.html?highlight=load_model#cntk.persist.load_model. The basic flow should look like this:
from cntk import load_model
loaded_model = load_model("yourModel.model", 'float')
output = model.eval(arguments)