If I want to use pretrained VGG19 network, I can simply do
from keras.applications.vgg19 import VGG19
VGG19(weights='imagenet')
Is there a similar implementation for AlexNet in keras or any other library?
In case anyone comes here for a solution,
I found a pretrained alex net from PyTorch here
import torchvision.models as models
alexnet_model = models.alexnet(pretrained=True)
You can find pretrained AlexNet model for keras here.
Related
I have h5 weights from a Keras model.
I want to rewrite the Keras model into a tf.keras model (using TF2.x).
I know that only the high level API changed, but do you know if I still can use the h5 weights?
Most likely they can be loaded, but is the structure different between Keras and tf.keras weights?
Thanks
It seems that they are the same
cudos to Mohsin hasan answer
In the past, when I had to convert tf.keras model to keras model, I
did following:
Train model in tf.keras
Save only the weights tf_model.save_weights("tf_model.hdf5")
Make Keras model architecture using all layers in keras (same as the tf keras one)
load weights by layer names in keras: keras_model.load_weights(by_name=True)
This seemed to work for me. Since, I was using out of box architecture
(DenseNet169), I had to very less work to replicate tf.keras network
to keras.
And the answer from Alex Cohn
tf.keras HDF5 model and Keras HDF5 models are not different things,
except for inevitable software version update synchronicity. This is
what the official docs say:
tf.keras is TensorFlow's implementation of the Keras API specification. This is a high-level API to build and train models that
includes first-class support for TensorFlow-specific functionality
If the convertor can convert a keras model to tf.lite, it will deliver
same results. But tf.lite functionality is more limited than tf.keras.
If this feature set is not enough for you, you can still work with
tensorflow, and enjoy its other advantages.
I am interested in training a model in tf.keras and then loading it with keras. I know this is not highly-advised, but I am interested in using tf.keras to train the model because
tf.keras is easier to build input pipelines
I want to take advantage of the tf.dataset API
and I am interested in loading it with keras because
I want to use coreml to deploy the model to ios.
I want to use coremltools to convert my model to ios, and coreml tools only works with keras, not tf.keras.
I have run into a few road-blocks, because not all of the tf.keras layers can be loaded as keras layers. For instance, I've had no trouble with a simple DNN, since all of the Dense layer parameters are the same between tf.keras and keras. However, I have had trouble with RNN layers, because tf.keras has an argument time_major that keras does not have. My RNN layers have time_major=False, which is the same behavior as keras, but keras sequential layers do not have this argument.
My solution right now is to save the tf.keras model in a json file (for the model structure) and delete the parts of the layers that keras does not support, and also save an h5 file (for the weights), like so:
model = # model trained with tf.keras
# save json
model_json = model.to_json()
with open('path_to_model_json.json', 'w') as json_file:
json_ = json.loads(model_json)
layers = json_['config']['layers']
for layer in layers:
if layer['class_name'] == 'SimpleRNN':
del layer['config']['time_major']
json.dump(json_, json_file)
# save weights
model.save_weights('path_to_my_weights.h5')
Then, I use the coremlconverter tool to convert from keras to coreml, like so:
with CustomObjectScope({'GlorotUniform': glorot_uniform()}):
coreml_model = coremltools.converters.keras.convert(
model=('path_to_model_json','path_to_my_weights.h5'),
input_names=#inputs,
output_names=#outputs,
class_labels = #labels,
custom_conversion_functions = { "GlorotUniform": tf.keras.initializers.glorot_uniform
}
)
coreml_model.save('my_core_ml_model.mlmodel')
My solution appears to be working, but I am wondering if there is a better approach? Or, is there imminent danger in this approach? For instance, is there a better way to convert tf.keras models to coreml? Or is there a better way to convert tf.keras models to keras? Or is there a better approach that I haven't thought of?
Any advice on the matter would be greatly appreciated :)
Your approach seems good to me!
In the past, when I had to convert tf.keras model to keras model, I did following:
Train model in tf.keras
Save only the weights tf_model.save_weights("tf_model.hdf5")
Make Keras model architecture using all layers in keras (same as the tf keras one)
load weights by layer names in keras: keras_model.load_weights(by_name=True)
This seemed to work for me. Since, I was using out of box architecture (DenseNet169), I had to very less work to replicate tf.keras network to keras.
I trained an h5 model using Keras and i don't know how to use it on other programs for other images. And also this question is not duplicate or i couldn't find any solution for that. Thanks...
Use models from keras library as below.
from keras open models
model = models.load_model("model.h5")
I don't really understand how to combine sklearn_crfsuite and Keras.
I have to made a classic LSTM and insteed of the last Activation, I use sklearn_crfsuite?
Someone have an example?
Thx,
You might want to look into the keras-contrib package, which has an implementation of CRF as a Keras layer.
I am trying to implement Gaussian Mixture Model using keras with tensorflow backend. Is there any guide or example on how to implement it?
Are you sure that it is what you want? you want to integrate a GMM into a neural network?
Tensorflow and Keras are libraries to create, train and use neural networks models. The Gaussian Mixture Model is not a neural network.