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Michael Becker mdbecker

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@BrikerMan
BrikerMan / w2v_visualizer-v2.py
Last active March 30, 2022 20:26
Convert gensim word2vec to tensorboard visualized model, detail: https://eliyar.biz/using-pre-trained-gensim-word2vector-in-a-keras-model-and-visualizing/
from gensim.models import KeyedVectors
# Load gensim word2vec
w2v_path = '<Gensim File Path>'
w2v = KeyedVectors.load_word2vec_format(w2v_path)
import io
# Vector file, `\t` seperated the vectors and `\n` seperate the words
"""
@lampts
lampts / gensim2projector_tf.py
Last active December 7, 2020 22:37
how to convert/port gensim word2vec to tensorflow projector board.
# required tensorflow 0.12
# required gensim 0.13.3+ for new api model.wv.index2word or just use model.index2word
from gensim.models import Word2Vec
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
# loading your gensim
model = Word2Vec.load("YOUR-MODEL")
@shashankg7
shashankg7 / ngram_cnn.py
Created September 20, 2016 15:34 — forked from joshloyal/ngram_cnn.py
Convolutional Network for Sentence Classification (Keras)
from keras.models import Graph
from keras.layers import containers
from keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution2D, MaxPooling2D
def ngram_cnn(n_vocab, max_length, embedding_size, ngram_filters=[2, 3, 4, 5], n_feature_maps=100, dropout=0.5, n_hidden=15):
"""A single-layer convolutional network using different n-gram filters.
Parameters
@joshloyal
joshloyal / ngram_cnn.py
Created March 11, 2016 15:29
Convolutional Network for Sentence Classification (Keras)
from keras.models import Graph
from keras.layers import containers
from keras.layers.core import Dense, Dropout, Activation, Reshape, Flatten
from keras.layers.embeddings import Embedding
from keras.layers.convolutional import Convolution2D, MaxPooling2D
def ngram_cnn(n_vocab, max_length, embedding_size, ngram_filters=[2, 3, 4, 5], n_feature_maps=100, dropout=0.5, n_hidden=15):
"""A single-layer convolutional network using different n-gram filters.
Parameters