serket

The ✨Magical✨ JAX NN Library.

*Serket is the goddess of magic in Egyptian mythology
Installation |Description |Quick Example |Freezing/Fine tuning |Filtering

🛠️ Installation

pip install serket

Install development version

pip install git+https://github.com/ASEM000/serket

📖 Description

serket aims to be the most intuitive and easy-to-use Neural network library in JAX.
serket is built on top of pytreeclass
serket currently implements

Group	Layers
Linear	`Linear`, `Bilinear`, `FNN` (Fully connected network), `PFNN` (Parallel fully connected network)
Convolution	`Conv1D`, `Conv2D`, `Conv3D`, `Conv1DTranspose` , `Conv2DTranspose`, `Conv3DTranspose`, `DepthwiseConv1D`, `DepthwiseConv2D`, `DepthwiseConv3D`
Containers	`Sequential`, `Lambda`
Activation	`AdaptiveReLU`, `AdaptiveLeakyReLU`, `AdaptiveSigmoid`, `AdaptiveTanh`
Pooling	`MaxPool1D`, `MaxPool2D`, `MaxPool3D`, `AvgPool1D`, `AvgPool2D`, `AvgPool3D` `GlobalMaxPool1D`, `GlobalMaxPool2D`, `GlobalMaxPool3D`, `GlobalAvgPool1D`, `GlobalAvgPool2D`, `GlobalAvgPool3D`
Reshaping	`Flatten`, `Unflatten`, `FlipLeftRight2D`, `FlipUpDown2D`, `Repeat1D`, `Repeat2D`, `Repeat3D`, `Resize1D`, `Resize2D`, `Resize3D`, `Upsampling1D`, `Upsampling2D`, `Upsampling3D`, `Padding1D`, `Padding2D`, `Padding3D`
Recurrent	`RNNCell`, `LSTMCell`
Normalization	`LayerNorm`
Blurring	`AvgBlur2D`
Physics	`Laplace2D`
Blocks	`VGG16Block`, `VGG19Block`

⏩ Quick Example

Simple Fully connected neural network.

🏗️ Model definition

import serket as sk 
import jax
import jax.numpy as jnp
import jax.random as jr

@sk.treeclass
class NN:
    def __init__(
        self, 
        in_features:int, 
        out_features:int, 
        hidden_features: int, key:jr.PRNGKey = jr.PRNGKey(0)):

        k1,k2,k3 = jr.split(key, 3)

        self.l1 = sk.nn.Linear(in_features, hidden_features, key=k1)
        self.l2 = sk.nn.Linear(hidden_features, hidden_features, key=k2)
        self.l3 = sk.nn.Linear(hidden_features, out_features, key=k3)
    
    def __call__(self, x):
        x = self.l1(x)
        x = jax.nn.relu(x)
        x = self.l2(x)
        x = jax.nn.relu(x)
        x = self.l3(x)
        return x

model = NN(
    in_features=1, 
    out_features=1, 
    hidden_features=128, 
    key=jr.PRNGKey(0))

🎨 Visualize

Model representation `__repr__`

print(f"{model!r}")
# `*` represents untrainable(static) nodes.
NN(
  l1=Linear(
    weight=f32[1,128],
    bias=f32[128],
    *in_features=1,
    *out_features=128,
    *weight_init_func=init(key,shape,dtype),
    *bias_init_func=Lambda(key,shape)
  ),
  l2=Linear(
    weight=f32[128,128],
    bias=f32[128],
    *in_features=128,
    *out_features=128,
    *weight_init_func=init(key,shape,dtype),
    *bias_init_func=Lambda(key,shape)
  ),
  l3=Linear(
    weight=f32[128,1],
    bias=f32[1],
    *in_features=128,
    *out_features=1,
    *weight_init_func=init(key,shape,dtype),
    *bias_init_func=Lambda(key,shape)
  )
)

Model values `__str__`

print(f"{model!s}")
# `*` represents untrainable(static) nodes.

NN(
  l1=Linear(
    weight=
      [[-0.556661   -0.6288703   1.28644    -2.9053314  -0.9808919   0.02763719
        -1.5992663   0.3522784  -0.72343904  2.1087773  -1.184502    0.37314773
         0.13440615 -1.1792887   2.646051   -0.31855923  1.2535691  -0.350722
         0.24288356  0.8924919   1.8751624  -0.4494902  -0.6869111   2.4898252
         1.0088646   2.3707743  -1.212474   -0.19152707  0.51991814 -0.801294
         1.9568022  -0.05682194  0.7434735   0.24796781 -0.31967887 -0.6026076
         0.02562018 -2.1735084  -0.7877185   1.1945596  -0.5776542  -0.08814432
         0.01738743  0.85175467 -2.4330282   2.400132   -0.15812641 -2.2410994
         1.8925649  -1.4573553  -1.5524752   0.2746206   0.99534875 -0.52039754
        -1.6240916   0.57301414  1.2754964   0.39254263  1.5842631  -0.4408383
         0.22060809 -0.11473875  1.2702179   0.14604266 -1.1393331  -0.20517357
         2.8613555  -0.76657873 -2.7623959   1.4629859   1.7641917   1.4639573
         0.90266997 -1.4661105   1.1719718   0.6656477  -0.6834308   1.0311401
        -3.0281627   1.7895395  -1.248399   -0.13082643  2.1665883   2.8423917
         0.24363454  0.20664148  1.7082529   2.129452    0.2974662  -0.8575109
        -0.5970874   0.01702698 -0.18604587  0.7464636   0.83206064  0.6965974
         0.7219791   0.8652629   1.3164111  -2.788336   -0.06530724 -0.7846771
        -0.7344756   1.5899261   0.2623837  -0.01147135 -0.5437088   0.68380916
        -1.5405492   1.1371891  -0.67851156 -0.37528485 -0.0336573  -2.0287845
         0.3067764  -1.3464272  -0.6037441  -1.6209227  -2.3215613  -3.062661
         0.5440992  -0.8735671   0.9094481   2.3398476   0.5821143   1.9373481
        -0.36942863  2.5151203 ]],
    bias=
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1.],
    *in_features=1,
    *out_features=128,
    *weight_init_func=init(key,shape,dtype),
    *bias_init_func=Lambda(key,shape)
  ),
  l2=Linear(
    weight=
      [[ 0.01565691 -0.02781865  0.15829083 ...  0.00930642  0.03536453
         0.01890953]
       [-0.01510135  0.1975845   0.2470963  ... -0.13168702  0.01404842
        -0.21973991]
       [-0.07814246 -0.18890998 -0.26707044 ... -0.15391685 -0.16248046
        -0.11042175]
       ...
       [-0.01806537  0.01311939  0.00696071 ... -0.18970545  0.07411639
        -0.04393121]
       [ 0.07426595  0.19547018 -0.26033685 ... -0.01357261 -0.00193011
        -0.00152987]
       [-0.00897581 -0.0115421   0.08062097 ... -0.098473    0.1083767
         0.12410464]],
    bias=
      [1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
       1. 1. 1. 1. 1. 1. 1. 1.],
    *in_features=128,
    *out_features=128,
    *weight_init_func=init(key,shape,dtype),
    *bias_init_func=Lambda(key,shape)
  ),
  l3=Linear(
    weight=
      [[-0.13613197]
       [ 0.14116174]
       [-0.06744987]
       [-0.08091136]
       [-0.27361065]
       [-0.06548355]
       [ 0.01022272]
       [ 0.0252317 ]
       [ 0.0237782 ]
       [ 0.00614042]
       [ 0.1812661 ]
       [-0.06621032]
       [ 0.16613998]
       [-0.05014007]
       [-0.21103479]
       [-0.11941364]
       [ 0.00036292]
       [ 0.00039283]
       [ 0.08278123]
       [ 0.10028461]
       [ 0.07373375]
       [ 0.04089416]
       [-0.00426106]
       [-0.0247845 ]
       [ 0.2804994 ]
       [-0.11494187]
       [ 0.26255226]
       [-0.05349432]
       [-0.16621305]
       [ 0.0187737 ]
       [ 0.11997257]
       [ 0.24926668]
       [ 0.12966438]
       [ 0.02550141]
       [ 0.18541676]
       [-0.09129915]
       [-0.22716352]
       [-0.18755099]
       [ 0.1665244 ]
       [-0.10028487]
       [ 0.09164064]
       [-0.02597431]
       [-0.15029983]
       [-0.02553205]
       [ 0.16129787]
       [-0.07182706]
       [-0.07004812]
       [-0.03763127]
       [-0.06973497]
       [-0.0998554 ]
       [ 0.00957549]
       [ 0.0948947 ]
       [-0.11812133]
       [ 0.00408699]
       [ 0.18451509]
       [-0.2392044 ]
       [ 0.1889591 ]
       [ 0.20876819]
       [ 0.16006592]
       [ 0.11820399]
       [ 0.13270618]
       [-0.02642066]
       [-0.03972287]
       [ 0.0130475 ]
       [ 0.12387222]
       [-0.07360736]
       [-0.07168346]
       [ 0.26462224]
       [-0.24544406]
       [ 0.02614611]
       [ 0.17016351]
       [-0.10638441]
       [-0.01891194]
       [ 0.02476142]
       [ 0.00474042]
       [ 0.06326718]
       [-0.10003307]
       [ 0.03704525]
       [-0.17377096]
       [ 0.02369826]
       [-0.09041592]
       [ 0.06363823]
       [-0.00131075]
       [-0.19338304]
       [ 0.2741859 ]
       [-0.03178171]
       [-0.0061704 ]
       [ 0.01059608]
       [ 0.17419283]
       [ 0.08168265]
       [ 0.08119942]
       [ 0.07225287]
       [-0.02761899]
       [ 0.11468761]
       [ 0.0180395 ]
       [-0.04214213]
       [-0.10949433]
       [-0.03126818]
       [ 0.14708327]
       [-0.25051817]
       [ 0.0431254 ]
       [ 0.10890955]
       [-0.00171187]
       [-0.07619253]
       [ 0.16909993]
       [-0.11504915]
       [ 0.02266672]
       [ 0.22796142]
       [ 0.05010169]
       [-0.26961675]
       [-0.02833704]
       [-0.21504459]
       [ 0.00469143]
       [ 0.23426442]
       [ 0.04301503]
       [-0.13504943]
       [-0.1914389 ]
       [-0.1553146 ]
       [ 0.00082878]
       [-0.05092873]
       [-0.13719554]
       [-0.24856809]
       [-0.05966872]
       [-0.04416765]
       [ 0.12827884]
       [-0.06721988]
       [ 0.05502734]
       [ 0.03519182]],
    bias=[1.],
    *in_features=128,
    *out_features=1,
    *weight_init_func=init(key,shape,dtype),
    *bias_init_func=Lambda(key,shape)
  )
)

Tree diagram

# `*` represents untrainable(static) nodes.
print(model.tree_diagram())
NN
    ├── l1=Linear
    │   ├── weight=f32[1,128]
    │   ├── bias=f32[128]
    │   ├*─ in_features=1
    │   ├*─ out_features=128
    │   ├*─ weight_init_func=init(key,shape,dtype)
    │   └*─ bias_init_func=Lambda(key,shape)    
    ├── l2=Linear
    │   ├── weight=f32[128,128]
    │   ├── bias=f32[128]
    │   ├*─ in_features=128
    │   ├*─ out_features=128
    │   ├*─ weight_init_func=init(key,shape,dtype)
    │   └*─ bias_init_func=Lambda(key,shape)    
    └── l3=Linear
        ├── weight=f32[128,1]
        ├── bias=f32[1]
        ├*─ in_features=128
        ├*─ out_features=1
        ├*─ weight_init_func=init(key,shape,dtype)
        └*─ bias_init_func=Lambda(key,shape)

Tree summary

>>> print(model.summary())
┌────┬──────┬─────────┬───────┬───────────────────┐
│Name│Type  │Param #  │Size   │Config             │
├────┼──────┼─────────┼───────┼───────────────────┤
│l1  │Linear│256(0)   │1.00KB │weight=f32[1,128]  │
│    │      │         │(0.00B)│bias=f32[128]      │
├────┼──────┼─────────┼───────┼───────────────────┤
│l2  │Linear│16,512(0)│64.50KB│weight=f32[128,128]│
│    │      │         │(0.00B)│bias=f32[128]      │
├────┼──────┼─────────┼───────┼───────────────────┤
│l3  │Linear│129(0)   │516.00B│weight=f32[128,1]  │
│    │      │         │(0.00B)│bias=f32[1]        │
└────┴──────┴─────────┴───────┴───────────────────┘
Total count :	16,897(0)
Dynamic count :	16,897(0)
Frozen count :	0(0)
---------------------------------------------------
Total size :	66.00KB(0.00B)
Dynamic size :	66.00KB(0.00B)
Frozen size :	0.00B(0.00B)
===================================================

Tree summary with shape inference

**Using `model.summary(array=input_array)` `serket` can evaluate the shape propagation without evaluating the model , by using `jax` no-flop shape inference operations.**

print(model.summary(array=x))

┌────┬──────┬─────────┬───────┬───────────────────┬────────────┐
│Name│Type  │Param #  │Size   │Config             │Input/Output│
├────┼──────┼─────────┼───────┼───────────────────┼────────────┤
│l1  │Linear│256(0)   │1.00KB │weight=f32[1,128]  │f32[100,1]  │
│    │      │         │(0.00B)│bias=f32[128]      │f32[100,128]│
├────┼──────┼─────────┼───────┼───────────────────┼────────────┤
│l2  │Linear│16,512(0)│64.50KB│weight=f32[128,128]│f32[100,128]│
│    │      │         │(0.00B)│bias=f32[128]      │f32[100,128]│
├────┼──────┼─────────┼───────┼───────────────────┼────────────┤
│l3  │Linear│129(0)   │516.00B│weight=f32[128,1]  │f32[100,128]│
│    │      │         │(0.00B)│bias=f32[1]        │f32[100,1]  │
└────┴──────┴─────────┴───────┴───────────────────┴────────────┘
Total count :	16,897(0)
Dynamic count :	16,897(0)
Frozen count :	0(0)
----------------------------------------------------------------
Total size :	66.00KB(0.00B)
Dynamic size :	66.00KB(0.00B)
Frozen size :	0.00B(0.00B)
================================================================

‍🔧 Train

import matplotlib.pyplot as plt

x = jnp.linspace(0,1,100)[:,None]
y = x**3 + jax.random.uniform(jax.random.PRNGKey(0),(100,1))*0.01


@jax.value_and_grad
def loss_func(model,x,y):
    return jnp.mean((model(x)-y)**2)

@jax.jit
def update(model,x,y):
    value,grad = loss_func(model,x,y)
    return value , model - 1e-3*grad

plt.plot(x,y,'-k',label='True')
plt.plot(x,model(x),'-r',label='Prediction')
plt.title("Before training")
plt.legend()
plt.show()

for _ in range(20_000):
    value,model = update(model,x,y)

plt.plot(x,y,'-k',label='True')
plt.plot(x,model(x),'-r',label='Prediction')
plt.title("After training")
plt.legend()
plt.show()

🥶 Freezing parameters /Fine tuning

🔘 Filtering by masking

Filter by value

# get model negative values
negative_model = model.at[model<0].get()

# Set negative values to 0
zeroed_model = model.at[model<0].set(0)

# Apply `jnp.cos` to negative values
cosined_model = model.at[model<0].apply(jnp.cos)

Filter by field name

# get model layer named `l1`
l1_model = model.at[model == "l1" ].get()

# Set `l1` values to 0
zeroed_model = model.at[model == "l1" ].set(0)

# Apply `jnp.cos` to `l1` 
cosined_model = model.at[model == "l1" ].apply(jnp.cos)

Filter by field type

# get all model `Linear` layers
l1_model = model.at[model == sk.nn.Linear ].get()

# Set `Linear` layers to 0
zeroed_model = model.at[model == sk.nn.Linear ].set(0)

# Apply `jnp.cos` to all `Linear` layers 
cosined_model = model.at[model == sk.nn.Linear ].apply(jnp.cos)

Filter by mixed masks

# Set all `Linear` bias to 0
mask = (model == sk.nn.Linear) & (model == "bias" )
zero_bias_model = model.at[mask].set(0.)

✨See here for more about filterning ✨

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Release history Release notifications | RSS feed

0.2.0b2 pre-release

Apr 20, 2023

0.1.0

Jul 27, 2023

0.0.11

Oct 30, 2022

0.0.10

Oct 27, 2022

0.0.9

Oct 26, 2022

0.0.8

Oct 24, 2022

0.0.7

Oct 19, 2022

0.0.6

Oct 11, 2022

This release

0.0.5

Oct 2, 2022

0.0.4

Sep 29, 2022

0.0.3

Sep 19, 2022

0.0.2

Aug 24, 2022

0.0.1

Aug 22, 2022

0.0.0rc0 pre-release

Aug 21, 2022

serket 0.0.5

The ✨Magical✨ JAX NN Library.

*Serket is the goddess of magic in Egyptian mythology
Installation |Description |Quick Example |Freezing/Fine tuning |Filtering

🛠️ Installation

📖 Description

⏩ Quick Example

🏗️ Model definition

🎨 Visualize

‍🔧 Train

🥶 Freezing parameters /Fine tuning

🔘 Filtering by masking

Filter by value

Filter by field name

Filter by field type

Filter by mixed masks

Project links

Key dates

1 maintainer

Credits

License

Requires

Tags

Classifiers

Download files

Source Distribution

Built Distribution

File details

File metadata

File hashes

File details

File metadata

File hashes

Release history Release notifications | RSS feed

serket 0.0.5

The ✨Magical✨ JAX NN Library.

*Serket is the goddess of magic in Egyptian mythology Installation |Description |Quick Example |Freezing/Fine tuning |Filtering

🛠️ Installation

📖 Description

⏩ Quick Example

🏗️ Model definition

🎨 Visualize

‍🔧 Train

🥶 Freezing parameters /Fine tuning

🔘 Filtering by masking

Filter by value

Filter by field name

Filter by field type

Filter by mixed masks

Project links

Key dates

1 maintainer

Credits

License

Requires

Tags

Classifiers

Download files

Source Distribution

Built Distribution

File details

File metadata

File hashes

File details

File metadata

File hashes

Release history Release notifications | RSS feed

*Serket is the goddess of magic in Egyptian mythology
Installation |Description |Quick Example |Freezing/Fine tuning |Filtering