After saving a Keras model, where is it saved? Is it saved locally? The below code is saving a model.
from keras.models import load_model
model.save('my_model.h5') # creates a HDF5 file 'my_model.h5
model = load_model('my_model.h5')
After saving the model can I use the load model again in another jupyter ipynb file?
When we do model.save, it will save model architecture and weight at the same time. It will save as .h5 locally and we can load it in another environment (local notebook, kaggle notebook, colab, GCP, etc). Using load_model will load fully model (arch + weight).
Please, read the official blog, you will find plenty of examples. https://www.tensorflow.org/guide/keras/save_and_serialize
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I have a pre-trained Keras model with ".h5" and ".json" file. I want to know the architecture of the model used and the names of the layers. Is there anyway I can do that?
I am new to this so I don't really know where to start
I am expecting logs that you get when you load a tensorflow model.
Sure, first load the model and then produce a summary of the model:
from keras.models import load_model
model = load_model('your_model.hdf5')
model.summary()
The summary will contain layer names and input/output shapes.
I am new to machine learning technology and I have a simple question. I have trained a network on the fashion MNIST and saved it, and then I would like to import the saved model for further processing instead of training the network each time I use the code. Can anyone help?
I have used the import function, but it always gives me an error
model.save("model_1.h5py") # save the model
model.import("model_2.h5py") # when I try to import the #model alwys gives me a valid synthax
I have trained a keras model and saved it to later make predictions. However, I loaded the saved model using:
from keras.models import load_model
#Restore saved keras model
restored_keras_model = load_model("C:/*******/saved_model.hdf5")
Now I would like to save an image of the loaded model so I can visualize it before using it before making predictions.
Is there a way of doing this in keras or is the use of another library required?
Yes, in addition to doing a restored_keras_model.summary(), you can save the model architecture as a png file using the plot_model API.
from keras.utils import plot_model
plot_model(restored_keras_model, to_file='model.png')
https://keras.io/visualization/#model-visualization
I am using google colaboratory to implement deep learning in python3. I create a model, train it, test it. Everything is fine. Finally I try to save the model on my google drive. But is says
Error: Currently 'save' requires model to be a graph network.
Upto training and testing there is no problem.
Then I mount the drive
from google.colab import drive
drive.mount('/content/gdrive')
And then try to save the model for later use as:
model.save('my_model_name.model')
But it is not saving the model. What am I missing?
the preferred way to save a model with tensorflow is to use the tf.train.Saver() module. so lets say your model is simply called Model and you want to save it in a particular directory. This is the preferred way to do it.
import tensorflow as tf
directory_to_save = '/content/drive'
with tf.Session() as sess
saver = tf.train.Saver()
#train model
saver.save(sess, directory_to_save)
Refer to this to train a GAN model for MNIST dataset, I want to save a model and restore it for further prediction. After having some understanding of Saving and Importing a Tensorflow Model I am able to save and restore some variables of inputs and outputs but for this network I am able to save the model only after some specific iterations and not able to predict some output.
Did you refer to this guide? It explains very clearly how to load and save tensorflow models in all possible formats.
If you are new to ML, I'd recommend you give Keras a try first, which is much easier to use. See https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model, pretty much you can use:
model.save('my_model.h5')
to save your model to disk.
model = load_model('my_model.h5')
to load your model and make prediction