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View lstm-trial-2.json
{"format": "layers-model", "generatedBy": "keras v2.2.4", "convertedBy": "TensorFlow.js Converter v1.2.6", "modelTopology": {"keras_version": "2.2.4", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential_18", "layers": [{"class_name": "LSTM", "config": {"name": "lstm_17", "trainable": true, "batch_input_shape": [null, 32, 1], "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 128, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regulari
View lstm-trial-1.json
{"class_name": "Sequential", "config": {"name": "sequential_2", "layers": [{"class_name": "LSTM", "config": {"name": "lstm_2", "trainable": true, "batch_input_shape": [null, 32, 1], "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "units": 128, "activation": "tanh", "recurrent_activation": "hard_sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "VarianceScaling", "config": {"scale": 1.0, "mode": "fan_avg", "distribution": "uniform", "seed": null}}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 1}}, {"class_name": "Dropout", "config":
@tangert
tangert / gensim_word2vec_procrustes_align.py
Last active Jun 7, 2019 — forked from quadrismegistus/gensim_word2vec_procrustes_align.py
Function to align any number of word2vec models using Procrustes matrix alignment.
View gensim_word2vec_procrustes_align.py
# Code originally ported from HistWords <https://github.com/williamleif/histwords> by William Hamilton <wleif@stanford.edu>.
def align_gensim_models(models, words=None):
"""
Returns the aligned/intersected models from a list of gensim word2vec models.
Generalized from original two-way intersection as seen above.
Also updated to work with the most recent version of gensim
Requires reduce from functools
@tangert
tangert / data.json
Last active Nov 13, 2018
MMLA Transcript Analysis
View data.json
{
"name": "Transcript data",
"children": [
{
"name": "Cluster 1",
"children": [
{"name": "tilted", "size": 0.767 },
{"name": "towards", "size": 0.199 },
{"name": "away", "size": 0.186 },
{"name": "north", "size": 0.098 },
@tangert
tangert / .block
Last active Apr 24, 2018
FREEEESH
View .block
license: mit
@tangert
tangert / .block
Last active Apr 30, 2018
Clustering Last.fm Users
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license: mit
View .block
license: mit
@tangert
tangert / .block
Last active Feb 21, 2018
SVGs and bar charts
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license: mit
@tangert
tangert / .block
Last active Feb 21, 2018
SVGs and circles
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license: mit
@tangert
tangert / .block
Last active Feb 21, 2018
Data Binding learning
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license: mit
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