I was following this tutorial, but when I wanted to convert the .h5 file into .pb I found out that I can't use checkpoints.
https://www.thepythoncode.com/article/skin-cancer-detection-using-tensorflow-in-python
Please explain this to me
Use model.save() to save the model in .pb format. For more information read Tensorflow SavedModel format.
import tensorflow as tf
#Load .h5 saved model
pre_model = tf.keras.models.load_model("final_model.h5")
#Converts .pb format
pre_model.save("saved_model/my_model")
Related
I have trained my custom object using Yolov4 and I have the following file :
yolov4-custom_best.weights
yolov4-custom.cfg
obj.names
'obj.names' has the names of the classes of the custom object.
In order to deploy my custom object detector to a web application I need the files in a tensorflow model format (eg : object_detector.h5)
Can someone help?
To get the model files in .h5 format you have to save the model. To save the model you can use
# The '.h5' extension indicates that the model should be saved to HDF5.
model.save('my_model.h5')
You can also save the model by using tf.keras.models.save_model()
tf.keras.models.save_model(model,filepath,save_format='h5')
For more details, please refer to this documentation. Thank You!
i have a saved_model.pb file and a .ckpt file for a tensorflow model.
I need to recover the summary of the model. I tried this code:
loaded = tf.saved_model.load(model_path)
loaded.summary()
But i get this error:
AttributeError: 'AutoTrackable' object has no attribute 'summary'
I have found out that the reason i get this error is that my model is a tensorflow model and not a keras model, and that I have to convert it into a keras model. However, all the solutions i found assume that i know the structure of the model, which i do not know.
Is there a way to convert the saved_model.pb or the .ckpt file directly in a keras model, without building it again?
I am pretty new to Deep Learning, I have a custom dataset which is quite large, how do I convert the .h5 model to a .tflite model and also how do I generate all the labels without doing it manually?
From Tensorflow documentation
Convert the model
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir) # path to the SavedModel directory
tflite_model = converter.convert()
Is there any way to convert data-00000-of-00001 to Tensorflow Lite model?
The file structure is like this
|-semantic_model.data-00000-of-00001
|-semantic_model.index
|-semantic_model.meta
Using TensorFlow Version: 1.15
The following 2 steps will convert it to a .tflite model.
1. Generate a TensorFlow Model for Inference (a frozen graph .pb file) using the answer posted here
What you currently have is model checkpoint (a TensorFlow 1 model saved in 3 files: .data..., .meta and .index. This model can be further trained if needed). You need to convert this to a frozen graph (a TensorFlow 1 model saved in a single .pb file. This model cannot be trained further and is optimized for inference/prediction).
2. Generate a TensorFlow lite model ( .tflite file)
A. Initialize the TFLiteConverter: The .from_frozen_graph API can be defined this way and the attributes which can be added are here. To find the names of these arrays, visualize the .pb file in Netron
converter = tf.compat.v1.lite.TFLiteConverter.from_frozen_graph(
graph_def_file='....path/to/frozen_graph.pb',
input_arrays=...,
output_arrays=....,
input_shapes={'...' : [_, _,....]}
)
B. Optional: Perform the simplest optimization known as post-training dynamic range quantization. You can refer to the same document for other types of optimizations/quantization methods.
converter.optimizations = [tf.lite.Optimize.DEFAULT]
C. Convert it to a .tflite file and save it
tflite_model = converter.convert()
tflite_model_size = open('model.tflite', 'wb').write(tflite_model)
print('TFLite Model is %d bytes' % tflite_model_size)
I am newbie of Tensorflow and trying to run one tutorial code located in https://github.com/Hvass-Labs/TensorFlow-Tutorials/blob/master/02_Convolutional_Neural_Network.ipynb
Based on this code, I would like to try to save the model in .pb format using simple_save and restore it for testing but I have no idea how to modify this piece of code. I have browsed some web pages but still didn't get the idea. Can anyone help me change this piece of code so that I can save the trained model and then load it for inference? Thank you!
For saving model, you need two things - input and output tensor names. In your case, input tensor is called x and output tensor is y_pred and y_pred_cls (mentioned in In [29] in notebook). Here's simple example to save your model:
simple_save(session,
export_dir,
inputs={"x": x,},
outputs={"y_pred": y_pred,
"y_pred_cls": y_pred_class})
EDIT:
Restoring-
restoring_graph = tf.Graph()
with restoring_graph.as_default():
with tf.Session(graph=restoring_graph) as sess:
# Restore saved values
tf.saved_model.loader.load(
sess,
[tag_constants.TRAINING],
export_dir # Path to SavedModel
)
# Pass inputs to model and do predictions below