Skip to main content

Calculate similarity with embedding

Project description

Keras Embedding Similarity

Version License

[中文|English]

Compute the similarity between the outputs and the embeddings.

Install

pip install keras-embed-sim

Usage

from tensorflow import keras
from keras_embed_sim import EmbeddingRet, EmbeddingSim

input_layer = keras.layers.Input(shape=(None,), name='Input')

embed, embed_weights = EmbeddingRet(
    input_dim=20,
    output_dim=100,
    mask_zero=True,
)(input_layer)

output_layer = EmbeddingSim()([embed, embed_weights])

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

keras-embed-sim-0.10.0.tar.gz (3.6 kB view details)

Uploaded Source

File details

Details for the file keras-embed-sim-0.10.0.tar.gz.

File metadata

  • Download URL: keras-embed-sim-0.10.0.tar.gz
  • Upload date:
  • Size: 3.6 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.0.1 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.7.4

File hashes

Hashes for keras-embed-sim-0.10.0.tar.gz
Algorithm Hash digest
SHA256 164a591168c757d18212a1bd4983ed015a9bc2d148e1878bcd01c2ba712c375b
MD5 6a42b6ece775f46f595889209dbf0bf8
BLAKE2b-256 d9ac641978394aaa28d90a81ae3e999ed8206c4c3223b7a668a5aa9ed6034db0

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page