I'm working on facial expression recognition using CNN. I'm using Keras and Tensorflow as backend. My model is saved to h5 format.
I want to retrain my network, and fine-tune my model with the VGG model.
How can I do that with keras ?
Save your models architecture and weights:
json_string = model.to_json()
model.save_weights('model_weights.h5')
Load model architecture and weights:
from keras.models import model_from_json
model = model_from_json(json_string)
model.load_weights('model_weights.h5')
Start training again from here for finetuning. I hope this helps.
You can use the Keras model.save(filepath) function.
Details for the various Keras saving and loading techniques are discussed with examples in this YouTube video: Save and load a Keras model
model.save(filepath)saves:
The architecture of the model, allowing to re-create the model.
The weights of the model.
The training configuration (loss, optimizer).
The state of the optimizer, allowing to resume training exactly where you left off.
To load this saved model, you would use the following:
from keras.models import load_model
new_model = load_model(filepath)
If you used model.to_json(), you would only be saving the architecture of the model. Additionally, if you used model.save_weights(), you would only be saving the weights of the model. With both of these alternative saving techniques, you would not be saving the training configuration (loss, optimizer), nor would you be saving the state of the optimizer.
Related
When I trained a Keras model using QAT (Quantization aware training),
There are some non-compatible problems like not support BatchNormalization, or UpSampling2D, etc.
How to prevent it directly without apply each layer with tfmot.quantization.keras.quantize_annotate_layer on each layer? (especially when building model with tensorflow keras functional API (instead of tf.keras.Sequential))
Supported layers for QAT module can be found here
Then, to quantize some layers instead of whole Model, just followed the official tutorial, then add what layer you wanna use to quantize.
#added layers here
supported_layers = [tf.keras.layers.Conv2D, tf.keras.layers.Dense, tf.keras.layers.ReLU]
def apply_quantization_to_dense(layer):
for supported_layer in supported_layers:
if isinstance(layer, tf.keras.layers.Dense):
return tfmot.quantization.keras.quantize_annotate_layer(layer)
return layer
If I have the saved description and weights of the model in json and .h5 files respectively, how can i continue/transfer training the SSD model in Keras on additional data? Which Keras SSD implementation would be simple to do this? https://github.com/pierluigiferrari/ssd_keras?
from keras.models import model_from_json
with open("path_to_json_file.json") as json_file:
model = model_from_json(json_file.read())
model.load_weights("path_to_weights_file.h5")
Do we have an option to save a trained Gensim Word2Vec model as a saved model using tf 2.0 tf.saved_model.save? In other words, how can I save a trained embedding vector as a saved model signature to work with tensorflow 2.0. The following steps are not correct normally:
model = gensim.models.Word2Vec(...)
model.init_sims(..)
model.train(..)
model.save(..)
module = gensim.models.KeyedVectors.load_word2vec(...)
tf.saved_model.save(
module,
export_dir
)
EDIT:
This example helped me about how to do it : https://keras.io/examples/nlp/pretrained_word_embeddings/
Gensim does not use TensorFlow and it has its own methods for loading and saving models.
You would need to convert Gensim embeddings into a TensorFlow a model which only makes sense if you further plan to use your embeddings within TensorFlow and possibly fine-tune them for your task.
Gensim Word2Vec are two steps in TensorFlow:
Vocabulary lookup: a table that assigns indices to tokens.
Embedding lookup layer that picks up the actual embeddings for the indices.
Then, you can save it as any other TensorFlow model.
I have keras pretrained model(model.h5). And I want to prune that model with tensorflow Magnitude-based weight pruning with Keras. One curious things is that my pretrained model is built with original keras model > I mean that is not from tensorflow.keras. Inside tensorflow Magnitude-based weight pruning with Keras example, they show how to do with tensorflow.keras model. I want to ask is that can I use their tool to prune my original keras pretrained model?
inside their weight pruning toolkit ,there is two way. one is pruned the model layer by layer while training and second is pruned the whole model. I tried the second way to prune the whole pretrained model. below is my code.
inside their weight pruning toolkit ,there is two way. one is pruned the model layer by layer while training and second is pruned the whole model. I tried the second way to prune the whole pretrained model. below is my code.
For my original pretrained model, I load the weight from model.h5 and can call model.summary() after I apply prune_low_magnitude() none of the method from model cannot call including model.summary() method. And show the error like AttributeError: 'NoneType' object has no attribute 'summary'
model = get_training_model(weight_decay)
model.load_weights('model/keras/model.h5')
model.summary()
epochs = 1
end_step = np.ceil(1.0 * 100 / 2).astype(np.int32) * epochs
print(end_step)
new_pruning_params = {
'pruning_schedule': tfm.sparsity.keras.PolynomialDecay(initial_sparsity=0.1,
final_sparsity=0.90,
begin_step=40,
end_step=end_step,
frequency=30)
}
new_pruned_model = tfm.sparsity.keras.prune_low_magnitude(model, **new_pruning_params)
print(new_pruned_model.summary())
Inside their weight pruning toolkit
enter link description here ,there is two way. one is pruned the model layer by layer while training and second is pruned the whole model. I tried the second way to prune the whole pretrained model. below is my code.
For my original pretrained model, I load the weight from model.h5 and can call model.summary() after I apply prune_low_magnitude() none of the method from model cannot call including model.summary() method. And show the error like
AttributeError: 'NoneType' object has no attribute 'summary'
I hope this answer still helps, but I recently had the same issue that prune_low_magnitude() returns an object of type 'None'. Also new_pruned_model.compile() would not work.
The model I had been using was a pretrained model that could be imported from tensorflow.python.keras.applications.
For me this worked:
(0) Import the libraries:
from tensorflow_model_optimization.python.core.api.sparsity import keras as sparsity
from tensorflow.python.keras.applications.<network_type> import <network_type>
(1) Define the pretrained model architecture
# define model architecture
loaded_model = <model_type>()
loaded_model.summary()
(2) Compile the model architecture and load the pretrained weights
# compile model
opt = SGD(lr=learn_rate, momentum=momentum)
loaded_model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
loaded_model.load_weights('weight_file.h5')
(3) set pruning parameters and assign pruning schedule
# set pruning parameters
pruning_params = {
'pruning_schedule': sparsity.PolynomialDecay(...)
}
# assign pruning schedule
model_pruned = sparsity.prune_low_magnitude(loaded_model, **pruning_params)
(4) compile model and show summary
# compile model
model_pruned.compile(
loss=tf.keras.losses.categorical_crossentropy,
optimizer='SGD',
metrics=['accuracy'])
model_pruned.summary()
It was important to import the libraries specifically from tensorflow.python.keras and use this keras model from the TensorFlow library.
Also, it was important to use the TensorFlow Beta Release (pip install tensorflow==2.0.0b1), otherwise still an object with type 'None' would be returned by prune_low_magnitude.
I am using PyCharm 2019.1.3 (x64) as IDE. Here is the link that led me to this solution: https://github.com/tensorflow/model-optimization/issues/12#issuecomment-526338458
I have repurposed an Inception V3 network using the transfer learning method, following this article.
For that, I removed the final network layer, and fed hundreds of images of my face into the network.
A new model was then sucessfully generated: inceptionv3-ft.model
Now I would like to load this model and use its fixed weights to apply my face as a 'theme' on a input image, like google-dream.
For that I am using a keras program, which loads models like so:
from keras.applications import inception_v3
# Build the InceptionV3 network with our placeholder.
# The model will be loaded with pre-trained ImageNet weights.
model = inception_v3.InceptionV3(weights='imagenet',
include_top=False)
dream = model.input
Full code here: https://github.com/keras-team/keras/blob/master/examples/deep_dream.py
So, how do I load and pass not a pre-trained but rather my RE-trained model weights?
simply:
from keras.models import load_model
model = load_model('inceptionv3-ft.model')
dream = model.input