Is there a simple way in tensorflow to make any model run in parallel without reprogramming the model? Like in PyTorch using the DataParallel method
I am new to tensorflow so any reference or code example is very much appreciated.
maybe you can modify the config proto of tensorflow and use:
config = tf.ConfigProto(device_count={'GPU': GPU},
inter_op_parallelism_threads=NBR_THREADS,
intra_op_parallelism_threads=NBR_THREADS)
with tf.Session(config=config) as sess:
<run_your_code>
Related
I've beeen trying out the Tensorflow 2 alpha and I have been trying to freeze and export a model to a .pb graphdef file.
In Tensorflow 1 I could do something like this:
# Freeze the graph.
frozen_graph_def = tf.graph_util.convert_variables_to_constants(
sess,
sess.graph_def,
output_node_names)
# Save the frozen graph to .pb file.
with open('model.pb', 'wb') as f:
f.write(frozen_graph_def.SerializeToString())
However this doesn't seem possible anymore as convert_variables_to_constants is removed and use of sessions is discouraged.
I looked and found there is the freeze graph util
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py that works with SavedModel exports.
Is there some way to do it within Python still or I am meant to switch and use this tool now?
I have also faced this same problem while migrating from tensorflow1.x to tensoflow2.0 beta.
This problem can be solved by 2 methods:
1st is to go to the tensflow2.0 docs search for the methods you have used and change the syntax for each line &
To use google's tf_ugrade_v2 script
tf_upgrade_v2 --infile your_tf1_script_file --outfile converted_tf2_file
You try above command to change your tensorflow1.x script to tensorflow2.0, it will solve all your problem.
Also, you can rename the method (Manual step by refering documentation)
Rename 'tf.graph_util.convert_variables_to_constants' to 'tf.compat.v1.graph_util.convert_variables_to_constants'
The measure problem is that in tensorflow2.0 is that many syntax and function has changed try referring the tensoflow2.0 docs or use the google's tf_upgrade_v2 script
Not sure if you've seen this Tensorflow 2.0 issue, but this response seems to be a work-around:
https://github.com/tensorflow/tensorflow/issues/29253#issuecomment-530782763
Note: this hasn't worked for my nlp model but maybe it will work for you. The suggested work-around is to use model.save_weights('weights.h5') while in TF 2.0 environment. Then create new environment with TF 1.14 and do all following steps in TF 1.14 env. Build your model model = create_model() and use model.load_weights('weights.h5') to load weights back into your model. Then save entire model with model.save('final_model.h5'). If you manage to have success with the above steps, then follow the rest of the steps in the link to use freeze_graph.
i got a model converted from caffe by using MMDNN tool, it converted the caffe model into a saved_model tensorflow style. it's a resnet18 model, and i just strip out several layers in the last, i wish i could load this architecture in the model_fn in a tf.estimator, and manually add some extra layers to do my job.
As the tutorial recommended that I could use loader.load method to load the saved_model. But i just want to use it in a estimator, and i need to define the architecture in the model_fn function. I searched out the SO and github but there isn't a very specific workflow to do that thing, somebody could help me out?
Here is one way of fine tuning using tf.Estimator:
Define your model using the SAME variable names/scopes as in your saved model
Use tf.estimator's warm start functions to initialize your new model with the saved weights. Here is a code snippet :
if fine_tuning:
ws = tf.estimator.WarmStartSettings(ckpt_to_initialize_from=path_saved_model,
vars_to_warm_start='.*')
else:
ws = None
estimator = tf.estimator.Estimator(model_fn=model_function,
warm_start_from=ws,
...
)
This will initialize any variable that share names between your currently defined graph and the saved model.
I just finished training a categorizer model exactly the way described in https://github.com/GoogleCloudPlatform/MiniCat but I am not sure how to use the model to make predictions.
Trained model in the direction Train
Data in the directory Data
I'm really new to this and I don't know where to start. I read something about deploying model in https://cloud.google.com/ml-engine/docs/tensorflow/deploying-models but how do I even create a SavedModel.
Any answers will be appreciated.
So in the folder where you got the trained model, you just need to load that model in your session. First create a saver (you can also use it for laoding)
train_saver = tf.train.Saver()
Now inside your session:
train_saver.restore(sess, 'path/to/model/doc_classifier_cnn_model.ckpt')
Then just feed the tensors with feed_dict.
Other option is to create a protobuf file (.pb) but in doing so you will have to load the model as I said.
I'm struggling running Tensorflow (v1.1) code multiple times in Jupyter Notebook.
For example, I execute this simple code snippet that creates an encoding layer for a seq2seq model:
# Construct encoder layer (LSTM)
encoder_cell = tf.contrib.rnn.LSTMCell(encoder_hidden_units)
encoder_outputs, encoder_final_state = tf.nn.dynamic_rnn(
encoder_cell, encoder_inputs_embedded,
dtype=tf.float32, time_major=False
)
First time is totally fine, my encoder is created.
However, if I rerun it (no matter the changes I've applied), I get this error:
Attempt to have a second RNNCell use the weights of a variable scope that already has weights
It's very annoying as it forces me to restart the kernel every time I want to change a layer.
Can someone explain me why this happens and how I can fix this ?
Thanks!
You are trying to build the exact same graph twice and therefore TensorFlow complains because the variables already exist in the default graph.
What you could do is to call tf.reset_default_graph() before trying to call the method a second time to ensure you create a new graph when required.
Just in case, I would also suggest using an interactive session as described here in the Start TensorFlow InteractiveSession section:
import tensorflow as tf
sess = tf.InteractiveSession()
I just started to learn tensorlayer, an excellent high-level wrapper over tensorflow framework for deep learning. Does anyone know how to do parameter gridsearch to tune DNN? Is there anyway that I can use GridSearchCV from sklearn to tune network parameters from tensorlayer? Thanks a lot.
you can do something like this.
for .... (different hyper-parameter):
with tf.Graph().as_default() as graph: # clear all variables of TF
tl.layers.clear_layers_name() # clear all layer name of TL
sess = tf.InteractiveSession()
# define and train a new model here
ref: https://github.com/tensorlayer/tensorlayer/issues/214