# 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
```python
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
```python
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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"description": "# Tensorflow Model Playground\r\n\r\n- Different tensorflow Deep Learning model & Helper Function.\r\n- Currently Included Generative Adversarial Networks , some helper function and Transformer.\r\n\r\n## Usage Example\r\n### Generative Adversarial Networks\r\n* Simple CycleGAN\r\n\r\n```python\r\nfrom modelpg.GAN import build_generator , build_descriminator , composite_model,train_model\r\ngenerator_1 = build_generator(image_shape=(256,256))\r\ngenerator_2 = build_generator(image_shape=(256,256))\r\n\r\ndescriminator_1 = build_descriminator(image_shape=(256,256))\r\ndescriminator_2 = build_descriminator(image_shape=(256,256))\r\n\r\ncomposite_1 = composite_model(generator_1,descriminator_1,generator_2,image_shape=(256,256))\r\ncomposite_2 = composite_model(generator_2,descriminator_2,generator_1,image_shape=(256,256))\r\n\r\ntrain_model(descriminator_1,descriminator_2,generator_1,generator_2,composite_1,composite_2,dataset,epochs=100)\r\n```\r\n\r\n- After training use each generator to generate images.\r\n\r\n\r\n### Transformer\r\n```python\r\nfrom modelpg.Transformer import Transformer\r\nnum_layers = 4\r\nd_model = 512\r\ndff = 4\r\nnum_heads = 8\r\ndropout_rate = 0.5\r\ntf = Transformer(num_layers=num_layers,\r\n num_heads=num_heads,\r\n d_model = d_model,\r\n forward_expansion=dff,\r\n inpt_vocab_size=2000,\r\n tar_vocab_size=2000,\r\n dropout=dropout_rate)\r\n```\r\nTrain this transformer using custom training loop or by `.fit()` method.\r\n**Note : `.fit` would take ((query , key),value) as parameter here X = (query,key) & Y = (value).**\r\n",
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