How do I convert a .h5 weight file to .pb file ? I trained keras pre-trained model and saved the file as something.h5 . How do i convert it to .pb file for using it for tensorflow serving ?
P.S: Don't degrade the question, I couldn't find any solution online. Do mention the reason why you degraded the question. Otherwise, help with solving the question.
import tensorflow as tf
model = tf.keras.models.load_model(keras_model_path)
tf.saved_model.save(model, saved_model_path)
Bingo.
Related
I'm exporting model to .h5 format using PyTorch like this.
torch.save(model.state_dict(), 'model.h5')
But when I load this model.h5 in TensorFlow, I got this error.
File "h5py/h5f.pyx", line 106, in h5py.h5f.open
OSError: Unable to open file (file signature not found)
I try both ways of saving models.
# 1.
torch.save(model.state_dict(), 'model.h5')
# 2.
torch.save(model, 'model.h5')
In both ways, I'm getting the same error.
You cant do that!
according to the official TF documentation, TensorFlow uses the HDF5 file format. PyTorch doesnt. Pytorch uses Python’s pickle to store the weights.
Just passing a random file extension doesn't magically convert it to that file format! you have to explicitly save your weights as hdf5.
I get this error when trying to convert .h5 model to .tflite
OSError: SavedModel file does not exist at: /home/xyz/Desktop/facial-expression-recognition-using-cnn-master/model/{saved_model.pbtxt|saved_model.pb}
The model is saved as .h5 but it does not save a .pb or .pbtext file. Can I have a solution to convert the .h5 file to .tflite please?
Thank you
Convert keras h5 model to tflite
tflite_convert \
--keras_model_file=/tmp/keras_model.h5
--output_file=/tmp/keras_model.tflite
after some searching and asked a question here i found a way to save the entire model of keras inside one file which is example.h5 for later use i found an answer here
https://machinelearningmastery.com/save-load-keras-deep-learning-models/
my previous question is
keras save the model weights to one file
and now i have to change to the mode.h5 convert it to tflite for tensorflow lite then i can used inside the mobile app
I need to train from scratch a CNN over a COCO dataset with a specific configuration: https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/embedded_ssd_mobilenet_v1_coco.config
Thus, I installed TF Object Detection API and I downloaded the COCO dataset. However the dataset is in .h5 extension.
Is it possible to run the training with this kind of file or do I need to convert it in images in someway? If that is possible, what would the command be?
PS: I was not able to find a pre-trained model with that config, this is why I need to train a cnn from scratch.
My suggestion would be to convert the .hdf5 file to a .tfrecord file, you can find examples of how to do this here.
I have downloaded a pre-trained model on ImageNet of Inception v3 from http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz (found this link while following one of the tutorials on codelabs).
This zip file contains .pb file that I can directly import as GraphDef in TensorFlow.
I would like to know if there are similar .pb files for other architectures as well, such as ResNet, VGG16, MobileNet, etc. If yes, could you provide the link of those?
Thanks in advance.
Kind Regards,
Ajay
You can find many pretrained models here: https://github.com/tensorflow/models/tree/master/research/slim#pre-trained-models
And the corresponding codes are here:
https://github.com/tensorflow/models/tree/master/research/slim/nets
Here are addition sources to download pretrained models:
TensorFlow Hub: https://tfhub.dev/
ModelZoo: https://modelzoo.co/
Though slightly different, Keras provides an interface to access pretrained models:
https://keras.io/api/applications/