endolith · October 11, 2024 07:04 · endolith · Aug 18, 2018 · endolith · Aug 19, 2018
diff --git a/readme.md b/readme.md
diff --git a/DFT_ANN.py b/DFT_ANN.py
 """
 Train a neural network to implement the discrete Fourier transform
 """
 import matplotlib.pyplot as plt
 import numpy as np
 from tensorflow.keras.layers import Dense
 from tensorflow.keras.models import Sequential

 N = 32
 batch = 10000

 # Generate random input data and desired output data
 sig = np.random.randn(batch, N) + 1j*np.random.randn(batch, N)
 F = np.fft.fft(sig, axis=-1)

 # First half of inputs/outputs is real part, second half is imaginary part
 X = np.hstack([sig.real, sig.imag])
 Y = np.hstack([F.real, F.imag])

 # Create model with no hidden layers, same number of outputs as inputs.
 # No bias needed.  No activation function, since DFT is linear.
 model = Sequential([Dense(N*2, input_dim=N*2, use_bias=False)])
 model.compile(loss='mean_squared_error', optimizer='adam')
 model.fit(X, Y, epochs=100, batch_size=100)

 # Confirm that it works
 data = np.arange(N)


 def ANN_DFT(x):
    if len(x) != N:
        raise ValueError(f'Input must be length {N}')
    pred = model.predict(np.hstack([x.real, x.imag])[np.newaxis])[0]
    result = pred[:N] + 1j*pred[N:]
    return result


 ANN = ANN_DFT(data)
 FFT = np.fft.fft(data)
 print(f'ANN matches FFT: {np.allclose(ANN, FFT)}')

 # Heat map of neuron weights
 plt.imshow(model.get_weights()[0], vmin=-1, vmax=1, cmap='coolwarm')
	"""
	Train a neural network to implement the discrete Fourier transform
	"""
	import matplotlib.pyplot as plt
	import numpy as np
	from tensorflow.keras.layers import Dense
	from tensorflow.keras.models import Sequential

	N = 32
	batch = 10000

	# Generate random input data and desired output data
	sig = np.random.randn(batch, N) + 1j*np.random.randn(batch, N)
	F = np.fft.fft(sig, axis=-1)

	# First half of inputs/outputs is real part, second half is imaginary part
	X = np.hstack([sig.real, sig.imag])
	Y = np.hstack([F.real, F.imag])

	# Create model with no hidden layers, same number of outputs as inputs.
	# No bias needed. No activation function, since DFT is linear.
	model = Sequential([Dense(N2, input_dim=N2, use_bias=False)])
	model.compile(loss='mean_squared_error', optimizer='adam')
	model.fit(X, Y, epochs=100, batch_size=100)

	# Confirm that it works
	data = np.arange(N)


	def ANN_DFT(x):
	if len(x) != N:
	raise ValueError(f'Input must be length {N}')
	pred = model.predict(np.hstack([x.real, x.imag])[np.newaxis])[0]
	result = pred[:N] + 1j*pred[N:]
	return result


	ANN = ANN_DFT(data)
	FFT = np.fft.fft(data)
	print(f'ANN matches FFT: {np.allclose(ANN, FFT)}')

	# Heat map of neuron weights
	plt.imshow(model.get_weights()[0], vmin=-1, vmax=1, cmap='coolwarm')