conda install -c negu93 cvnn

pip install cvnn

pip install cvnn[plotter]

pip install cvnn[full]

import numpy as np
import tensorflow as tf

# Assume you already have complex data... example numpy arrays of dtype np.complex64
(train_images, train_labels), (test_images, test_labels) = get_dataset()        # to be done by each user

model = get_model()   # Get your model

# Compile as any TensorFlow model
model.compile(optimizer='adam', metrics=['accuracy'],
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True))
model.summary()

# Train and evaluate
history = model.fit(train_images, train_labels, epochs=epochs, validation_data=(test_images, test_labels))
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)

import cvnn.layers as complex_layers

def get_model():
    model = tf.keras.models.Sequential()
    model.add(complex_layers.ComplexInput(input_shape=(32, 32, 3)))                     # Always use ComplexInput at the start
    model.add(complex_layers.ComplexConv2D(32, (3, 3), activation='cart_relu'))
    model.add(complex_layers.ComplexAvgPooling2D((2, 2)))
    model.add(complex_layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
    model.add(complex_layers.ComplexMaxPooling2D((2, 2)))
    model.add(complex_layers.ComplexConv2D(64, (3, 3), activation='cart_relu'))
    model.add(complex_layers.ComplexFlatten())
    model.add(complex_layers.ComplexDense(64, activation='cart_relu'))
    model.add(complex_layers.ComplexDense(10, activation='convert_to_real_with_abs'))   
    # An activation that casts to real must be used at the last layer. 
    # The loss function cannot minimize a complex number
    return model

import cvnn.layers as complex_layers
def get_model():
    inputs = complex_layers.complex_input(shape=(128, 128, 3))
    c0 = complex_layers.ComplexConv2D(32, activation='cart_relu', kernel_size=3)(inputs)
    c1 = complex_layers.ComplexConv2D(32, activation='cart_relu', kernel_size=3)(c0)
    c2 = complex_layers.ComplexMaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid')(c1)
    t01 = complex_layers.ComplexConv2DTranspose(5, kernel_size=2, strides=(2, 2), activation='cart_relu')(c2)
    concat01 = tf.keras.layers.concatenate([t01, c1], axis=-1)

    c3 = complex_layers.ComplexConv2D(4, activation='cart_relu', kernel_size=3)(concat01)
    out = complex_layers.ComplexConv2D(4, activation='cart_relu', kernel_size=3)(c3)
    return tf.keras.Model(inputs, out)

@software{j_agustin_barrachina_2021_4452131,
  author       = {J Agustin Barrachina},
  title        = {Complex-Valued Neural Networks (CVNN)},
  month        = jan,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {v1.0.3},
  doi          = {10.5281/zenodo.4452131},
  url          = {https://doi.org/10.5281/zenodo.4452131}
}