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apply tensorflow 2.3.1 version #11

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38 changes: 19 additions & 19 deletions requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,21 +1,21 @@
absl-py==0.7.1
astor==0.8.0
elasticsearch==7.0.2
gast==0.2.2
google-pasta==0.1.7
grpcio==1.22.0
h5py==2.9.0
absl-py==0.11.0
astor==0.8.1
elasticsearch==7.10.1
gast==0.3.3
google-pasta==0.2.0
grpcio==1.34.0
h5py==2.10.0
Keras-Applications==1.0.8
Keras-Preprocessing==1.1.0
Markdown==3.1.1
numpy==1.16.4
protobuf==3.9.1
six==1.12.0
tensorboard==1.14.0
tensorflow==1.14.0
tensorflow-estimator==1.14.0
tensorflow-hub==0.5.0
Keras-Preprocessing==1.1.2
Markdown==3.3.3
numpy==1.18.5
protobuf==3.13.0
six==1.15.0
tensorflow==2.3.1
tensorboard==2.4.0
tensorflow-hub==0.10.0
tensorflow-estimator==2.3.0
termcolor==1.1.0
urllib3==1.25.3
Werkzeug==0.15.5
wrapt==1.11.2
urllib3==1.25.11
Werkzeug==1.0.1
wrapt==1.12.1
26 changes: 6 additions & 20 deletions src/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,9 +4,6 @@
from elasticsearch import Elasticsearch
from elasticsearch.helpers import bulk

# Use tensorflow 1 behavior to match the Universal Sentence Encoder
# examples (https://tfhub.dev/google/universal-sentence-encoder/2).
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub

##### INDEXING #####
Expand Down Expand Up @@ -107,8 +104,8 @@ def handle_query():
##### EMBEDDING #####

def embed_text(text):
vectors = session.run(embeddings, feed_dict={text_ph: text})
return [vector.tolist() for vector in vectors]
vectors = model(text)
return [vector.numpy().tolist() for vector in vectors]

##### MAIN SCRIPT #####

Expand All @@ -124,24 +121,13 @@ def embed_text(text):
GPU_LIMIT = 0.5

print("Downloading pre-trained embeddings from tensorflow hub...")
embed = hub.Module("https://tfhub.dev/google/universal-sentence-encoder/2")
text_ph = tf.placeholder(tf.string)
embeddings = embed(text_ph)
print("Done.")

print("Creating tensorflow session...")
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = GPU_LIMIT
session = tf.Session(config=config)
session.run(tf.global_variables_initializer())
session.run(tf.tables_initializer())
print("Done.")
module_url = "https://tfhub.dev/google/universal-sentence-encoder/4"
model = hub.load(module_url)
print("module %s loaded" % module_url)

client = Elasticsearch()

index_data()
run_query_loop()

print("Closing tensorflow session...")
session.close()
print("Done.")
print("Done.")