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https://github.com/abhilash1910/deepgenerator
Sentence Sequence Transduction Library (Seq to Seq) for text generation using sequential generative Vanilla RNN using numpy
https://github.com/abhilash1910/deepgenerator
adagrad generativemodeling nlp numpy optimizer rnn seq2seq-model transduction vanilla-rnn
Last synced: about 1 month ago
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Sentence Sequence Transduction Library (Seq to Seq) for text generation using sequential generative Vanilla RNN using numpy
- Host: GitHub
- URL: https://github.com/abhilash1910/deepgenerator
- Owner: abhilash1910
- License: mit
- Created: 2020-04-10T15:10:09.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-08-24T19:42:55.000Z (about 4 years ago)
- Last Synced: 2024-08-23T10:51:10.208Z (3 months ago)
- Topics: adagrad, generativemodeling, nlp, numpy, optimizer, rnn, seq2seq-model, transduction, vanilla-rnn
- Language: Jupyter Notebook
- Homepage:
- Size: 69.3 KB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# DeepGenerator :pushpin:
Sentence Sequence Transduction Library (Seq to Seq) for text generation using sequential generative Vanilla RNN using numpy
The library is a generative network built purely in numpy and matplotlib. The library is a sequence to sequence transduction
library for generating n sequence texts from given corpus. Metrics for accuracy-BLEU has provided a considerable accuracy metric
for epochs greater than 20000.The library is sequential and includes intermediate tanh activation in the intermediate stages with
softmax cross entropy loss ,and generalised Adagrad optimizer.
# Installation:
```python
pip install DeepGenerator
```
# library facts:
initialization:
```python
import DeepGenerator.DeepGenerator as dg
```
Object Creation:
```python
deepgen=dg.DeepGenerator()
```
# Functions:
1.attributes for users- learning rate,epochs,local path of data storage(text format),number of hidden layers,kernel size,sequence/step size,count of next words
2.data_abstract function- Takes arguements (self,path,choice) -
path= local path of text file
choice= 'character_generator' for character generation network
'word_generator' for word generator network
Returns data
Usage- ouput_data=deepgen.data_preprocess(DeepGenerator.path,DeepGenerator.choice)
3.data_preprocess function- Takes arguements (self,path,choice)-
path= local path of text file
choice= 'character_generator' for character generation network
'word_generator' for word generator network
Returns data,data_size,vocab_size,char_to_idx,idx_to_char
Usage- data,data_size,vocab_size,char_to_idx,idx_to_char=deepgen.data_preprocess(DeepGenerator.path,DeepGenerator.choice)
4.hyperparameters function-Takes arguements (self,hidden_layers_size,no_hidden_layers,learning_rate,step_size,vocab_size)-
hidden_layers-kernel size-recommended under 2048
no_hidden_layers- sequential intermediate layers
learning_rate- learning_rate (range of 1e-3)
step_size- sequence length(should be <= vocab_size)
vocab_size
Returns hidden_layers,learning_rate,step_size,hid_layer,Wxh,Whh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by
Usage- hidden_layers,learning_rate,step_size,hid_layer,Wxh,Whh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by=deepgen.hyperparamteres(dg.hidden_layers_size,dg.no_hidden_layers,dg.learning_rate,dg.step_size,dg.vocab_size)
5. loss_evaluation function- Takes arguements (self,inp,target,h_previous,hidden_layers,hid_layer,Wxh,Wh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by) -
inp= character to indices encoded dictionary of input text
target=character to indices encoded dictionary of generated text
h_previous-value of hidden layer for previous state
hidden_layers-kernel size
hid_layer-sequential hidden layers
---------- sequential layers---------
-----weight tensors------
Wxh- weight tensor of input to first hidden layer
Wh1- weight tensor of first hidden layer to first layer of sequential network
Whh_vector-weight tensors of intermediate sequential network
Whh- weight tensor of last sequential to last hidden layer
Why-weight tensor of last hidden layer to output layer
-----bias tensors-------
bh1-bias of first hidden layer
bh_vector-bias of intermediate sequential layers
bhh-bias of end hidden layer
by-bias at output
Returns loss,dWxh,dWhh1,dWhh_vector,dWhh,dWhy,dbh1,dbh_vector,dbh,dby,h_state[len(inp)-1],Whh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by
Usage loss,dWxh,dWhh1,dWhh_vector,dWhh,dWhy,dbh1,dbh_vector,dbh,dby,h_state[len(inp)-1],Whh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by=deepgen.loss_evaluation(dg.inp,dg.target,dg.h_previous,dg.hidden_layers,dg.hid_layer,dg.Wxh,dg.Wh1,dg.Whh_vector,dg.Whh,dg.Why,dg.bh1,dg.bh_vector,dg.bh,dg.by)
6.start_predict function-Takes arguements (self,count,epochs,Wh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by,hid_layer,char_to_idx,idx_to_char,vocab_size,learning_rate,step_size,data,hidden_layers)
counts-count of sequences to generate
epochs-epochs
Whi -weight tensors
bhi-bias tensors
hid_layer-no of sequential layers
char_to_idx-character to index encoder
idx_to_char-index to character decoder
vocab_size-vocab_size
learning_rate-learning_rate
step_size-sequence length
hidden_layers-kernel size
Returns epochs,gradient losses vector -Beta release(out_txt_vector -vector of putput generated sentences - *() implies not yet shipped to pypi)
Usage-epochs,gradient_loss,*(out_txt_vector)=deepgen.start_predict(dg.count,dg.epochs,dg.Whh1,dg.Whh_vector,dg.Whh,dg.Why,dg.bh1,dg.bh_vector,dg.bh,dg.by,dg.hid_layer,dg.char_to_idx,dg.idx_to_char,dg.vocab_size,dg.learning_rate,dg.step_size,dg.data,dg.hidden_layers)
7.output_sample function- Takes arguements (self,h1,seed_ix,n,vocab_size,Wh1,Whh_vector,Whh,Why,bh1,bh_vector,bh,by,hid_layer)-
h1-hidden layer previous state
seed_ix-starting point for generation
n-count of text to generate
Whi-weight tensor
bhi-bias tensor
hid_layer-no of sequential layers
Returns ixs- integer vector of maximum probability characters/words
Usage-ixs=deepgen.output_sample(dg.h1,dg.seed_ix,dg.n,dg.vocab_size,dg.Wh1,dg.Whh_vector,dg.Whh,dg.Why,dg.bh1,dg.bh_vector,dg.bh,dg.by,dg.hid_layer)
8.plot_loss function -Takes arguements(self,epochs,gradient_loss)-
epochs-epoch vector
gradient_loss- gradient loss vector
Returns void
Usage-deepgen.plot_loss(dg.epoch,dg.gradient_loss)
# Usage-
The file sample.py contains the usage specification and syntax for generating text
Jupyter notebook -Deepgen.ipynb is also present as a sample with different text files.
# Library link:
https://pypi.org/project/DeepGenerator/0.1/