- echoAI # main package directory
| - Activation # sub-package containing activation functions implementation
| |- Torch  # sub-package containing implementation for PyTorch
| | | - functional.py # script which contains implementation of activation functions
| | | - weightedTanh.py # activation functions wrapper class for PyTorch
| | | - ... # PyTorch activation functions wrappers
| |- Keras  # sub-package containing implementation for Keras
| | | - custom_activations.py # script which contains implementation of activation functions
| |- TF_Keras  # sub-package containing implementation for Tensorflow-Keras
| | | - custom_activation.py # script which contains implementation of activation functions
| - __init__.py

- Observations # Folder containing other assets

- docs # Sphinx documentation folder

- LICENSE # license file
- README.md
- setup.py # package setup file
- Smoke_tests # folder, which contains scripts with demonstration of activation functions usage
- Unit_tests # folder, which contains unit test scripts

# import PyTorch
import torch

# import activation function from echoAI
from echoAI.Activation.Torch.mish import Mish

# apply activation function
mish = Mish()
t = torch.tensor(0.1)
t_mish = mish(t)

# import Keras
from keras import layers
from keras.models import Model
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D

# import activation function from echoAI
from echoAI.Activation.Keras.custom_activations import Mish

def CNNModel(input_shape):
    X_input = Input(input_shape)
    X = ZeroPadding2D((3, 3))(X_input)X
    X = Conv2D(32, (3, 3), strides = (1, 1), name = 'conv0')(X)
    X = BatchNormalization(axis = 3, name = 'bn0')(X)

    # apply the activation function
    X = Mish()(X)

    # MAXPOOL
    X = MaxPooling2D((2, 2), name='max_pool')(X)

    # FLATTEN X (means convert it to a vector) + FULLYCONNECTED
    X = Flatten()(X)
    X = Dense(10, activation='softmax', name='fc')(X)

    # Create model
    model = Model(inputs = X_input, outputs = X, name='CNNModel')

    return model

#import tensorflow
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense, Flatten

# import activation function from echoAI
from echoAI.Activation.TF_Keras.custom_activation import Mish

model = tf.keras.Sequential([
    layers.Flatten(),
    layers.Dense(128, input_shape=(784,)),
    Mish(), # use the activation function
    layers.Dense(64, activation='relu'),
    layers.Dense(10, activation='softmax')])

# Compile the model
model.compile(optimizer = "adam", loss = "mean_squared_error", metrics = ["accuracy"])

# Fit the model
model.fit(x = X_train, y = y_train, epochs = 3, batch_size = 128)