Various Deep Learning Models (tensorflow)
Project description
Tensorflow Model Playground
- Different tensorflow Deep Learning model & Helper Function.
- Currently Included Generative Adversarial Networks , some helper function and Transformer.
Usage Example
Generative Adversarial Networks
- Simple CycleGAN
from modelpg.GAN import build_generator , build_descriminator , composite_model,train_model
generator_1 = build_generator(image_shape=(256,256))
generator_2 = build_generator(image_shape=(256,256))
descriminator_1 = build_descriminator(image_shape=(256,256))
descriminator_2 = build_descriminator(image_shape=(256,256))
composite_1 = composite_model(generator_1,descriminator_1,generator_2,image_shape=(256,256))
composite_2 = composite_model(generator_2,descriminator_2,generator_1,image_shape=(256,256))
train_model(descriminator_1,descriminator_2,generator_1,generator_2,composite_1,composite_2,dataset,epochs=100)
- After training use each generator to generate images.
Transformer
from modelpg.Transformer import Transformer
num_layers = 4
d_model = 512
dff = 4
num_heads = 8
dropout_rate = 0.5
tf = Transformer(num_layers=num_layers,
num_heads=num_heads,
d_model = d_model,
forward_expansion=dff,
inpt_vocab_size=2000,
tar_vocab_size=2000,
dropout=dropout_rate)
Train this transformer using custom training loop or by .fit()
method.
Note : .fit
would take ((query , key),value) as parameter here X = (query,key) & Y = (value).
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