Can we use tf.spectral fourier functions in keras?

Question

Let us start with an input that is a simple time series and try to build an autoencoder that simply fourier transforms then untransforms our data in keras.

If we try to

Answer 1

Just to add more to what's going on above for anyone who gets here from search engines. The following, contributed in this google group discussion, will run rfft then ifft with convolutions and other layers in between:

inputs = Input(shape=(10, 8), name='main_input')
x = Lambda(lambda v: tf.to_float(tf.spectral.rfft(v)))(inputs)
x = Conv1D(filters=5, kernel_size=3, activation='relu', padding='same')(x)
x = Lambda(lambda v: tf.to_float(tf.spectral.irfft(tf.cast(v, dtype=tf.complex64))))(x)
x = Flatten()(x)
output = Dense(1)(x)
model = Model(inputs, output)
model.summary()

It uses the same concepts as Allen's answer but the slight differences allow compatibility with intermediate convolutions.