Phase learning https://stackoverflow.com/questions/56098924

phaselearning.py

from keras.models import Sequential
from keras.layers import Dense
import numpy as np

def normalize_angles(phases):
    phases = phases + np.pi
    phases /= (2 * np.pi)
    return phases

def build_fourier_mnist():
    mnist = np.load("train_features.npy") #MNIST as is.
    fourier_mnist = np.zeros(mnist.shape, dtype=np.complex)
    for i in range(mnist.shape[0]):
        current_image = np.reshape(mnist[i, :], (28, 28)) #Turn to matrix so we can perform 2d fft
        fourier_current_image = np.fft.fft2(current_image) #perform 2d fft
        fourier_mnist[i, :] = np.reshape(fourier_current_image,(1, 784)) #flatten and save to new matrix
    return fourier_mnist

fourier_mnist = build_fourier_mnist()
amplitudes = np.abs(fourier_mnist)
phases = normalize_angles(np.angle(fourier_mnist))

model = Sequential()
model.add(Dense(784, input_dim=amplitudes.shape[1], activation='sigmoid'))
model.add(Dense(784, activation='sigmoid'))
model.add(Dense(784, activation='sigmoid'))
model.add(Dense(784, activation='sigmoid'))
model.add(Dense(phases.shape[1], activation='sigmoid'))
#Compile model
model.compile(loss='mean_squared_error', optimizer='adam')
#Fit the model
model.fit(amplitudes, phases, epochs=400, batch_size=100)
model.save("phase_retriever2.h5")