#!pip install -q textgenrnn
import os
import pandas as pd
from textgenrnn import textgenrnn
Using TensorFlow backend.
BASE_DIR = os.getcwd()
DATA_DIR = os.path.join(BASE_DIR, '..', 'datasets')
# from: https://github.com/amauboussin/arxiv-twitterbot
arxiv_df = pd.read_csv(os.path.join(DATA_DIR, 'brundage_bot.csv'))
print('Total number of papers:', len(arxiv_df))
arxiv_df.head()
Total number of papers: 27188
link | time | favorites | rts | authors | category | published | summary | title | tweeted | |
---|---|---|---|---|---|---|---|---|---|---|
0 | arxiv.org/abs/1611.10003 | NaN | NaN | NaN | [Tom A. F. Anderson, C. -H. Ruan] | q-bio.NC | 2016-11-30 05:17:11 | In summary of the research findings presented ... | Vocabulary and the Brain: Evidence from Neuroi... | 0 |
1 | arxiv.org/abs/1611.10007 | NaN | NaN | NaN | [M. Amin Rahimian, Amir G. Aghdam] | cs.SY | 2016-11-30 05:37:11 | In this paper, structural controllability of a... | Structural Controllability of Multi-Agent Netw... | 0 |
2 | arxiv.org/abs/1611.10010 | NaN | NaN | NaN | [Debidatta Dwibedi, Tomasz Malisiewicz, Vijay ... | cs.CV | 2016-11-30 06:00:47 | We present a Deep Cuboid Detector which takes ... | Deep Cuboid Detection: Beyond 2D Bounding Boxes | 0 |
3 | arxiv.org/abs/1611.10012 | 2016-12-01 01:46:12 | 11.0 | 2.0 | [Jonathan Huang, Vivek Rathod, Chen Sun, Mengl... | cs.CV | 2016-11-30 06:06:15 | In this paper, we study the trade-off between ... | Speed/accuracy trade-offs for modern convoluti... | 1 |
4 | arxiv.org/abs/1611.10014 | NaN | NaN | NaN | [Yoones Hashemi, Amir H. Banihashemi] | cs.IT | 2016-11-30 06:12:45 | In this paper, we propose a characterization o... | Characterization and Efficient Exhaustive Sear... | 0 |
# cs.AI Artificial Intelligence -- cs.CL Computation and Language -- cs.CV Computer Vision and Pattern Recognition
# cs.LG Learning -- cs.NE Neural and Evolutionary Computing -- stat.ML Machine Learning
arxiv_df = arxiv_df.loc[(arxiv_df.category == 'cs.AI') | (arxiv_df.category == 'cs.CL') |
(arxiv_df.category == 'cs.CV') | (arxiv_df.category == 'cs.LG') |
(arxiv_df.category == 'cs.NE') | (arxiv_df.category == 'stat.ML')]
print('Number of deep learning papers:', len(arxiv_df))
arxiv_df.head()
Number of deep learning papers: 10003
link | time | favorites | rts | authors | category | published | summary | title | tweeted | |
---|---|---|---|---|---|---|---|---|---|---|
2 | arxiv.org/abs/1611.10010 | NaN | NaN | NaN | [Debidatta Dwibedi, Tomasz Malisiewicz, Vijay ... | cs.CV | 2016-11-30 06:00:47 | We present a Deep Cuboid Detector which takes ... | Deep Cuboid Detection: Beyond 2D Bounding Boxes | 0 |
3 | arxiv.org/abs/1611.10012 | 2016-12-01 01:46:12 | 11.0 | 2.0 | [Jonathan Huang, Vivek Rathod, Chen Sun, Mengl... | cs.CV | 2016-11-30 06:06:15 | In this paper, we study the trade-off between ... | Speed/accuracy trade-offs for modern convoluti... | 1 |
5 | arxiv.org/abs/1611.10017 | NaN | NaN | NaN | [Gou Koutaki, Keiichiro Shirai, Mitsuru Ambai] | cs.CV | 2016-11-30 06:35:39 | In this paper, we propose a learning-based sup... | Fast Supervised Discrete Hashing and its Analysis | 0 |
10 | arxiv.org/abs/1611.10031 | NaN | NaN | NaN | [Peng Liu, Hui Zhang, Kie B. Eom] | cs.LG | 2016-11-30 07:34:46 | Active deep learning classification of hypersp... | Active Deep Learning for Classification of Hyp... | 0 |
11 | arxiv.org/abs/1611.10038 | NaN | NaN | NaN | [Si Li, Nianwen Xue] | cs.CL | 2016-11-30 07:53:34 | A patent is a property right for an invention ... | Towards Accurate Word Segmentation for Chinese... | 0 |
arxiv_df.loc[32, 'title']
'Fusion of EEG and Musical Features in Continuous Music-emotion\n Recognition'
# remove newlines in arxiv titles
arxiv_df.title = arxiv_df.title.apply(lambda x: x.replace('\n ', ''))
arxiv_df.loc[32, 'title']
'Fusion of EEG and Musical Features in Continuous Music-emotion Recognition'
paper_len = (arxiv_df.title.str.len()).mean()
print(f'mean paper title length: {paper_len:.2f} chars')
mean paper title length: 69.33 chars
songs_df = pd.read_json(os.path.join(DATA_DIR, 'song_titles_5yrs.json'))
songs_df.columns = ['title']
print('Total number of songs:', len(songs_df))
songs_df.head()
Total number of songs: 9850
title | |
---|---|
0 | Silver Lining |
1 | Four Winds |
2 | Half Love |
3 | Sowa (Alex Garett & Greg Herma Edit) |
4 | Killing My Time |
song_len = (songs_df.title.str.len()).mean()
print(f'mean song title length: {song_len:.2f} chars')
mean song title length: 22.43 chars
arxiv_titles = '\n'.join(arxiv_df.title)
song_titles = '\n'.join(songs_df.title)
print('arxiv_titles:\n', arxiv_titles[:100])
print('\nsong_titles:\n', song_titles[:50])
arxiv_titles: Deep Cuboid Detection: Beyond 2D Bounding Boxes Speed/accuracy trade-offs for modern convolutional o song_titles: Silver Lining Four Winds Half Love Sowa (Alex Gare
print('arxiv_titles vocab_len:', len(set(arxiv_titles)))
print('song_titles vocab_len:', len(set(song_titles)))
bs = min(len(set(arxiv_titles)), len(set(song_titles)))
print('min vocab_len (and max batch_size):', bs)
arxiv_titles vocab_len: 111 song_titles vocab_len: 142 min vocab_len (and max batch_size): 111
with open(os.path.join(DATA_DIR, 'arxiv_titles.txt'), 'w', encoding='utf-8') as f:
f.write(arxiv_titles)
with open(os.path.join(DATA_DIR, 'song_titles.txt'), 'w', encoding='utf-8') as f:
f.write(song_titles)
model_cfg = {
'rnn_size': 128,
'rnn_layers': 4,
'rnn_bidirectional': False, #True,
'max_length': int(paper_len),
#'max_words': 10000,
'dim_embeddings': 100,
'word_level': False,
}
train_cfg = {
'num_epochs': 10,
'gen_epochs': 2,
'batch_size': bs, # 1024,
'train_size': 0.8,
'dropout': 0.0,
'max_gen_length': int(paper_len*2), #300,
'validation': True, # False,
'is_csv': False
}
paper_model_name = 'deep_paper_titles'
textgen = textgenrnn(name=paper_model_name)
textgen.train_from_file(
file_path=os.path.join(DATA_DIR, 'arxiv_titles.txt'),
new_model=True,
num_epochs=train_cfg['num_epochs'],
gen_epochs=train_cfg['gen_epochs'],
batch_size=train_cfg['batch_size'],
train_size=train_cfg['train_size'],
dropout=train_cfg['dropout'],
max_gen_length=train_cfg['max_gen_length'],
validation=train_cfg['validation'],
is_csv=train_cfg['is_csv'],
rnn_layers=model_cfg['rnn_layers'],
rnn_size=model_cfg['rnn_size'],
rnn_bidirectional=model_cfg['rnn_bidirectional'],
max_length=model_cfg['max_length'],
dim_embeddings=model_cfg['dim_embeddings'],
word_level=model_cfg['word_level']
)
10,002 texts collected. Training new model w/ 4-layer, 128-cell LSTMs Training on 562,837 character sequences. Epoch 1/10 8794/8794 [==============================] - 406s 46ms/step - loss: 2.0727 - val_loss: 1.3352 Epoch 2/10 8794/8794 [==============================] - 398s 45ms/step - loss: 1.2272 - val_loss: 1.1812 #################### Temperature: 0.2 #################### Deep Learning of Computer Neural Networks with Deep Convolutional Neural Networks State Recognition of Control with Deep Learning of Convolutional Neural Networks State of Deep Learning of Convolutional Neural Networks for Structured Matrix Faction #################### Temperature: 0.5 #################### Structural Sparse Shallow Analysis of Humanoratic Models for Prediction of Concerving Reconstruction and Typerspectral Regression A Deep Learning Learning of Image Sparse Recognition with Automatic Deep Learning Learning the Encoder Transformation in the Encoder in Stochastic Driven Selection of Consistent and Textures #################### Temperature: 1.0 #################### A Droma of Treatforms: Can Learning to evide neural network for Two Imaging Structures Grutacurs Assolonithms with semantic Graphical RCV Deep Sentiment of Character ag Deval Improve Imaging intervention simulations Epoch 3/10 8794/8794 [==============================] - 489s 56ms/step - loss: 1.1083 - val_loss: 1.1110 Epoch 4/10 8794/8794 [==============================] - 398s 45ms/step - loss: 1.0401 - val_loss: 1.0838 #################### Temperature: 0.2 #################### A Statistical Representation for Behavioral Subspace Classification Semantic Segmentation in Semantic Segmentation Semi-supervised Learning of Semantic Segmentation #################### Temperature: 0.5 #################### Interpretation of Solvers from Saliency of Semantic Constrained Based on State Selection Statistical Layor Matching Algorithms Based on Propagation Network programming for the convolutional neural networks #################### Temperature: 1.0 #################### Focus trafform Generation in space from a Building in widding and syntactc, and mlo a InduV and Response Margin in Incremental Pindired Datasets for High Document Detection Mislocationzant Detection Using Deep-Grammatical Images Epoch 5/10 8794/8794 [==============================] - 397s 45ms/step - loss: 0.9978 - val_loss: 1.0565 Epoch 6/10 8794/8794 [==============================] - 398s 45ms/step - loss: 0.9664 - val_loss: 1.0459 #################### Temperature: 0.2 #################### A New Semantic Parsing with Sparse Representations Product Networks for Predictive Adversarial Networks Learning to Predictive Adversarial Networks for Structured Sparsity Alignment and Transfer Learning #################### Temperature: 0.5 #################### Synthesizing Random Forest Control for Context-Supervision Applications A New Head Network Function for Presence of Semantic Segmentation Coupled Data of Convolutional Neural Network Through Structured Detection in Supervision Make Recognition #################### Temperature: 1.0 #################### Deep Frame based Correction in Reasoning, Algorithms Madiods error closures Ehher-classifiers: Madgmability through Transfer Learning Epoch 7/10 8794/8794 [==============================] - 398s 45ms/step - loss: 0.9214 - val_loss: 1.0258 Epoch 8/10 8794/8794 [==============================] - 398s 45ms/step - loss: 0.8805 - val_loss: 1.0247 #################### Temperature: 0.2 #################### A Deep Learning Approach to Deep Learning Models A New Method for Deep Learning Approaches for Deep Learning Semi-supervised learning for spectrum interaction and classification #################### Temperature: 0.5 #################### A Deep Network Model for Spectrum Reconstruction and Selective Learning A Deep Learning Approach to Deep Neural Networks for Real-time Transfer Learning GPU-based Event Detection using Tree Simple Dependencies and Linear Density Estimation and Semi-supervised Learning of Medical Image Ret #################### Temperature: 1.0 #################### Event-Shallow Using Faster Curriculum Learning Lifte Predictability ildexming Semi-supervised dependent selection to drive modeling in the surget videos Long Short Memory Arlience of CNNs evolving Techniques Epoch 9/10 8794/8794 [==============================] - 398s 45ms/step - loss: 0.8375 - val_loss: 1.0179 Epoch 10/10 8794/8794 [==============================] - 398s 45ms/step - loss: 0.7921 - val_loss: 1.0204 #################### Temperature: 0.2 #################### A Survey on Deep Neural Network for Sequence Labeling and Sequence Machine Translation A Comparison of Neural Network Architectures for Semantic Segmentation A Survey on Deep Learning Approach for Semantic Segmentation #################### Temperature: 0.5 #################### A Multi-View Structure of Stream Selection in Continuous Functions and Minimization Semi-Supervised Learning for Learning a Multi-task Matching Automatic Structure of Semantic Parsing with Convolutional Neural Networks #################### Temperature: 1.0 #################### IDAN: Play-Mo: Reading Hand synaphrous Conversations in Linear Regression with Optimal CT Dictionary Learning Penal-related to nissating harvetryctive multi-view system Scale-based Graph-Machine Learning Localization using Swarmprint Anatomety in First-Person Content
"textgen.train_new_model(\n arxiv_titles,\n num_epochs=train_cfg['num_epochs'],\n gen_epochs=train_cfg['gen_epochs'],\n batch_size=train_cfg['batch_size'],\n train_size=train_cfg['train_size'],\n dropout=train_cfg['dropout'],\n max_gen_length=train_cfg['max_gen_length'],\n validation=train_cfg['validation'],\n is_csv=train_cfg['is_csv'],\n rnn_layers=model_cfg['rnn_layers'],\n rnn_size=model_cfg['rnn_size'],\n rnn_bidirectional=model_cfg['rnn_bidirectional'],\n max_length=model_cfg['max_length'],\n dim_embeddings=model_cfg['dim_embeddings'],\n word_level=model_cfg['word_level']\n)"
textgen_song = textgenrnn(weights_path=os.path.join(DATA_DIR, 'models', f'{paper_model_name}_weights.hdf5'),
vocab_path=os.path.join(DATA_DIR, 'models', f'{paper_model_name}_vocab.json'),
config_path=os.path.join(DATA_DIR, 'models', f'{paper_model_name}_config.json'))
textgen_song.train_from_file(
file_path=os.path.join(DATA_DIR, 'song_titles.txt'),
num_epochs=1,
gen_epochs=1,
batch_size=train_cfg['batch_size'],
train_size=train_cfg['train_size'],
dropout=0.5,
max_gen_length=train_cfg['max_gen_length'],
validation=train_cfg['validation'],
is_csv=train_cfg['is_csv'],
rnn_layers=model_cfg['rnn_layers'],
rnn_size=model_cfg['rnn_size'],
rnn_bidirectional=model_cfg['rnn_bidirectional'],
max_length=model_cfg['max_length'],
dim_embeddings=model_cfg['dim_embeddings'],
word_level=model_cfg['word_level']
)
9,849 texts collected. Training on 184,005 character sequences. Epoch 1/1 2875/2875 [==============================] - 131s 46ms/step - loss: 1.9834 - val_loss: 1.8403 #################### Temperature: 0.2 #################### Way (Roothine Remix) Will In The We (Kays Remix) Way To Feel (Bootleg) #################### Temperature: 0.5 #################### Home (feat. K................5 ...........5.........................5...........5..............5................5....5.................. Without Keep (Flum Remix) Inter Love #################### Temperature: 1.0 #################### Reson feat. Jeryni Map (Rockon Text Remix) Psners (GFURS Reqish) Lex The Wire In Get Oiv Like French
textgen_song.generate_samples(max_gen_length=100, n=5)
#################### Temperature: 0.2 #################### In The Wend (RAC Mix) Wond In The We (Roothing Remix) Stay (Robother Remix) Stay (Prod. by Know) Bellon The We Downt (feat. All In The Wook Remix) #################### Temperature: 0.5 #################### Forest (Jaman Chainsmokers Remix) Way The Changer (Now Remix) When In Me (Kill Remix) Commer Green My Hand (Boul Club Remix) #################### Temperature: 1.0 #################### About Un Glassica Ku Georgetting City (The Main Remax) Goodly Wwin Vice (Koelien Moh Remix)
model_cfg = {
'rnn_size': 128,
'rnn_layers': 4,
'rnn_bidirectional': False, #True,
'max_length': int(song_len),
#'max_words': 10000,
'dim_embeddings': 100,
'word_level': False,
}
train_cfg = {
'num_epochs': 10,
'gen_epochs': 2,
'batch_size': bs, # 1024,
'train_size': 0.8,
'dropout': 0.0,
'max_gen_length': int(song_len*2), #300,
'validation': True, # False,
'is_csv': False
}
song_model_name = 'deep_song_titles'
textgen = textgenrnn(name=song_model_name)
textgen.train_from_file(
file_path=os.path.join(DATA_DIR, 'song_titles.txt'),
new_model=True,
num_epochs=train_cfg['num_epochs'],
gen_epochs=train_cfg['gen_epochs'],
batch_size=train_cfg['batch_size'],
train_size=train_cfg['train_size'],
dropout=train_cfg['dropout'],
max_gen_length=train_cfg['max_gen_length'],
validation=train_cfg['validation'],
is_csv=train_cfg['is_csv'],
rnn_layers=model_cfg['rnn_layers'],
rnn_size=model_cfg['rnn_size'],
rnn_bidirectional=model_cfg['rnn_bidirectional'],
max_length=model_cfg['max_length'],
dim_embeddings=model_cfg['dim_embeddings'],
word_level=model_cfg['word_level']
)
9,849 texts collected. Training new model w/ 4-layer, 128-cell LSTMs Training on 184,530 character sequences. Epoch 1/10 1662/1662 [==============================] - 37s 22ms/step - loss: 3.3772 - val_loss: 2.3540 Epoch 2/10 1662/1662 [==============================] - 36s 22ms/step - loss: 2.0871 - val_loss: 1.9189 #################### Temperature: 0.2 #################### Stronge (feat. Sean Brand) Searter (feat. Ander Brand) Wanter (feat. Aling Land) #################### Temperature: 0.5 #################### Whe Somether No Love (Life Strac Remix) Comethan (Strive Remix) #################### Temperature: 1.0 #################### Wanny Night (Donk Coce Edit) The Pennega Epoch 3/10 1662/1662 [==============================] - 36s 21ms/step - loss: 1.8194 - val_loss: 1.7822 Epoch 4/10 1662/1662 [==============================] - 37s 22ms/step - loss: 1.6628 - val_loss: 1.6967 #################### Temperature: 0.2 #################### Sunnast (Prod. by Big Shain) The Wild You (Solidis Sambi Remix) Better Way (feat. Sam Suntant) #################### Temperature: 0.5 #################### Better With You (feat. Miesy) Say My Madion (Disco Bootleg) The Wild (feat. Kelex Boys) #################### Temperature: 1.0 #################### Nathing You Lover (Im Heud In My. Wanting Under Pratch (Benger Kelix Edit) Somk Live You Feat. K.E.A. & Motthra Have) Epoch 5/10 1662/1662 [==============================] - 37s 22ms/step - loss: 1.5423 - val_loss: 1.6513 Epoch 6/10 1662/1662 [==============================] - 36s 22ms/step - loss: 1.4369 - val_loss: 1.6237 #################### Temperature: 0.2 #################### Stay (Sam Selektah Remix) The Starding (feat. Brasstree) Stay (feat. Lil Yachty) (Cassion Remix) #################### Temperature: 0.5 #################### Feel In The Cold Come And Me (feat. Kele & Rae Mars) Drive Me Look (Lash Remix) #################### Temperature: 1.0 #################### Eugh feat. Blacks (produced by give Camman BelieveSpate Better Tois Epoch 7/10 1662/1662 [==============================] - 37s 22ms/step - loss: 1.3404 - val_loss: 1.6078 Epoch 8/10 1662/1662 [==============================] - 36s 21ms/step - loss: 1.2503 - val_loss: 1.6263 #################### Temperature: 0.2 #################### Stay With Me Something Bout You (Remix) Sunshine #################### Temperature: 0.5 #################### Hello (feat. Bright The Deep) Can't Hear Me There (feat. Arternan Ellie Hold On We're Going Home (feat. Antis Bloo #################### Temperature: 1.0 #################### Love Like Thus in Stills Moving Closer Cold Bob (Basswris Remix) Epoch 9/10 1662/1662 [==============================] - 36s 21ms/step - loss: 1.1629 - val_loss: 1.6341 Epoch 10/10 1662/1662 [==============================] - 36s 21ms/step - loss: 1.0816 - val_loss: 1.6477 #################### Temperature: 0.2 #################### The One (Feat. Mathe Dayt) Superfriends (feat. Kendrick Lamar) Hold On We're Going Home (Dave Edwards Rem #################### Temperature: 0.5 #################### The Startion feat. Zies Boy, Phonix Chorto Bend On My Mind (Marce Remix) Can't Help In Last (Like Sings Remix) #################### Temperature: 1.0 #################### Gone Probes feat. Embreesty (Laou Kniss & Black Bubble) Head Up (Loud Luxuse Remix)
textgen_paper = textgenrnn(weights_path=os.path.join(DATA_DIR, 'models', f'{song_model_name}_weights.hdf5'),
vocab_path=os.path.join(DATA_DIR, 'models', f'{song_model_name}_vocab.json'),
config_path=os.path.join(DATA_DIR, 'models', f'{song_model_name}_config.json'))
textgen_paper.train_from_file(
file_path=os.path.join(DATA_DIR, 'arxiv_titles.txt'),
num_epochs=1,
gen_epochs=1,
batch_size=train_cfg['batch_size'],
train_size=train_cfg['train_size'],
dropout=0.9,
max_gen_length=train_cfg['max_gen_length'],
validation=train_cfg['validation'],
is_csv=train_cfg['is_csv'],
rnn_layers=model_cfg['rnn_layers'],
rnn_size=model_cfg['rnn_size'],
rnn_bidirectional=model_cfg['rnn_bidirectional'],
max_length=model_cfg['max_length'],
dim_embeddings=model_cfg['dim_embeddings'],
word_level=model_cfg['word_level']
)
10,002 texts collected. Training on 562,523 character sequences. Epoch 1/1 5067/5067 [==============================] - 106s 21ms/step - loss: 1.3301 - val_loss: 1.1896 #################### Temperature: 0.2 #################### A Simultaneous Search for Structure Learni A Neural Neural Networks for Multi-label D A Statistical Search for Structure Models #################### Temperature: 0.5 #################### A Neural Networks for Real-time Cluster fo Bein Low-Reduced Prediction in Multi-modal Domain Function Using Deep Learning with B #################### Temperature: 1.0 #################### Multi-Agents independence propagator seep Typeoring-Level Mundems Intervesconronquent Variatorits for RalisN
textgen_paper.generate_samples(max_gen_length=100, temperatures=[0.5, 0.7, 1.0], n=5)
#################### Temperature: 0.5 #################### A Linear Learning Framework for Person Re-identification of Stochastic Sensors Using Model Predict A Normalized Constrained End-to-End Linear Gradient Face Regression with Deep Learning and Sensor A Discovery of Finiter Grade for Learning for Resolution Models A Deep Neural Networks for Fully Context Placking Multi-labeling Large Sensing for Multi-view Constrained Residual Learning for Betweed Programs of #################### Temperature: 0.7 #################### Neural Networks and Function Processing Exploining Deep Learning for Face extraction of Structure Detection Dlatchion Detection for Learning Data for Unsupervised Learning in Visual Recognizing A tro Constrained Neighbor Models Trick Prediction with Minimum Band Features Model for Neural Language Modeling and Hate Tracking D #################### Temperature: 1.0 #################### Multimor: The CLAM-IO Ranch Scoculus Tracking Motion Large Veor Multillal Recognization Framework for Recognizing Large-scale Information for Fa Neural Factorizits Prices Groundded Robust Waras Non-Seats and Pening Based Netwark Reduced Low-Rank Tabdling
with open(os.path.join(DATA_DIR, 'mixed_titles.txt'), 'w', encoding='utf-8') as f:
f.write(arxiv_titles + '\n' + song_titles)
model_cfg = {
'rnn_size': 128,
'rnn_layers': 4,
'rnn_bidirectional': False, #True,
'max_length': 5, #40,
'max_words': 10000,
'dim_embeddings': 100,
'word_level': False,
}
train_cfg = {
'num_epochs': 2, # 10,
'gen_epochs': 1,
'batch_size': 64, # 1024,
'train_size': 0.8,
'dropout': 0.5, # 0.0,
'max_gen_length': 50, #300,
'validation': True, # False,
'is_csv': False
}
mixed_model_name = 'mixed'
textgen = textgenrnn(name=mixed_model_name)
textgen.train_from_file(
file_path=os.path.join(DATA_DIR, 'mixed_titles.txt'),
new_model=True,
num_epochs=train_cfg['num_epochs'],
gen_epochs=train_cfg['gen_epochs'],
batch_size=train_cfg['batch_size'],
train_size=train_cfg['train_size'],
dropout=train_cfg['dropout'],
max_gen_length=train_cfg['max_gen_length'],
validation=train_cfg['validation'],
is_csv=train_cfg['is_csv'],
rnn_layers=model_cfg['rnn_layers'],
rnn_size=model_cfg['rnn_size'],
rnn_bidirectional=model_cfg['rnn_bidirectional'],
max_length=model_cfg['max_length'],
dim_embeddings=model_cfg['dim_embeddings'],
word_level=model_cfg['word_level']
)
19,852 texts collected. Training new model w/ 4-layer, 128-cell LSTMs Training on 747,719 character sequences. Epoch 1/2 11683/11683 [==============================] - 147s 13ms/step - loss: 1.7612 - val_loss: 1.5157 #################### Temperature: 0.2 #################### Based Recognition in Deep Neural Network for Spe A State Convolutional Network for State Convolut Deep Neural Networks #################### Temperature: 0.5 #################### A Shode Linear Sequential Streaming Remix) An Every of Localization Me Down (feat. A. Gradi An Estimation of a Grammodition of Evolutional N #################### Temperature: 1.0 #################### C-Neclarided Localized Dispricon Layeroa) Arn't Frame Continuous intelliative for Video Re Intacle Aggrix (Low Freed in Work Epoch 2/2 11683/11683 [==============================] - 143s 12ms/step - loss: 1.4492 - val_loss: 1.3971 #################### Temperature: 0.2 #################### Multi-Task Learning for Sparse Remix) Exploring with Recognition in Context for Neural Recognition for Multi-modal Recognition for Spar #################### Temperature: 0.5 #################### Improved Translations Structural Networks Multimodal Segmentation of End-to-End Cardand (T #################### Temperature: 1.0 #################### Whard and Me Line Cell (Prediction Networks for Supervised-Pe UltTrims for Visual Loses Remix)
textgen.generate_samples(max_gen_length=100, n=5, temperatures=[0.5, 0.7, 1.0])
#################### Temperature: 0.5 #################### Experiment Learning Embedding for Prediction for Prediction with Media Segmentation Metric Interpretable Generation DeepMe (feat. Remix) On Me (feat. Andreak (feat. Change Weight (Feat. The Minimal Recognition for Deep Neural networks Good (Feat. The Love (feat. Bundit Detection #################### Temperature: 0.7 #################### Framework for Subspace (Dance of Resolutional Networks Shot Caption Bie Detection with Generation On (Feat Nonless Free Box Story Method for Parallel recurrent Neural Everything Survey #################### Temperature: 1.0 #################### Gradients Lesion Restarding with a Wild Recognitional similarity Identification Networks Face Recognizing Label 1017Id2 ME Romix Remix)
Default textgenrnn settings (2 epochs):
Rebalancing songs and papers + max_length of 5 + not bi-directional + dropout of 0.5 + short max_gen_length (2 epochs):
Same as last, but max_length of 10 + dropout of 0.1:
Same as last, but max_length of 5:
Lost track of these:
Same as 'space camera' one, but dropout of 0.5:
deep_paper_titles only: (not mixed with song titles at all... but they still seem musical)