Is there a way to convert support vector classifier with rbf kernel to tf model?
I am aware of converting support vector classifier with linear kernel because there exists coef_ where we can find parameters and assign to tf model. Got this idea from how to convert saved model from sklearn into tensorflow/lite.
However _coef wont be there for rbf , so i am not sure on how to convert this model to an tf model.
Related
What would be the easiest way to convert a .pb or a .h5 model to a .meta file.
I have 2 programs, one generating a model a keras and one loading the same model in tensorflow 1.0. Can anyone help me with converting the keras model to a metagraphdef and get all trainable variables with weights,
I have an optimized TensorFlow Lite model which I load as
interpreter = tf.lite.Interpreter(model_path=converted_model_path)
interpreter.allocate_tensors()
How do I get number of model parameters similar to keras's model.count_params()?
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 am trying to convert a frozen mobilenet_v1 graph to tflite. I'm interested in the conversion process and not just the quantized tflite model, so I'm comparing my converted model to one already made by tensorflow. The difference between them is that my conv2d nodes have a relu6 activation function while the correct model's conv2d nodes don't have any activation function. How should I go about fixing this problem?
I should also mention that I'm converting to a fully quantized tflite model by using tensorflow's quantization-aware training and activating the quantization flags upon conversion to tflite.
Suppose I have a pre-trained model stored in a Tensorflow checkpoint. I'd like to convert it into a Keras model. I can load the checkpoint into a TF session alright but that's where I get stuck.
I think it's impossible to create a Keras model using TF checkpoint, but you can copy it's weights to the already created Keras model.
Checkout this. https://github.com/yuyang-huang/keras-inception-resnet-v2
The extract_weights.py is to save the TF weights to numpy array, while load_weights.py is for load the npy file to the Keras model.
For more reference, this is how I implement it https://github.com/DableUTeeF/keras-efficientnet/tree/master/keras_efficientnet.