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def plot_weights(index, weights=model.get_weights()[0], N=fft_size):
  plt.figure(figsize=(9, 3))
  plt.subplot(1, 2, 1)
  plt.plot(weights[index][:N])
  plt.plot(weights[index][N:], 'g-')
  plt.subplot(1, 2, 2)
  plt.plot(weights[fft_size+index][:N])
  plt.plot(weights[fft_size+index][N:], 'g-')
  plt.show()

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import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

batch_size = 15000
fft_size = 128

xs = np.random.randn(batch_size, fft_size) + np.random.randn(batch_size, fft_size) * 1j
ys = np.fft.fft(xs, axis=1)

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def plot_weights(index, weights=weights_sxx):
  plt.figure(figsize=(15, 2))
  plt.subplot(1, 3, 1)
  plt.plot(weights[index][0])
  plt.subplot(1, 3, 2)
  plt.plot(weights[index][5])
  plt.subplot(1, 3, 3)
  plt.plot(weights[index][10])
  plt.show()

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weights_sxx = model.get_weights()[0]
weights_sxx = np.reshape(weights_sxx, (size, rows * 2, cols))
weights_sxx = np.moveaxis(weights_sxx, (0,1,2), (2,1,0))

plt.figure(figsize=(12, 7))
plt.subplot(2, 2, 1)
plt.pcolormesh(weights_sxx[0], cmap="Accent")
plt.subplot(2, 2, 2)
plt.pcolormesh(weights_sxx[10], cmap="Accent")
plt.subplot(2, 2, 3)

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plt.figure(figsize=(12, 3))
plt.subplot(1, 2, 1)
plt.pcolormesh(t, f, np.log(wave_sxx_graph))
plt.subplot(1, 2, 2)
plt.pcolormesh(t, f, np.log(nn_sxx_graph))
plt.show()

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def make_wave():
  # Create four evenly-spaced tones
  waves = []
  hz = 0
  for i in range(0, size, 512):
    hz = hz + 50
    waves.append(np.sin(np.linspace(0, 2 * np.pi * hz, 512)))
  return np.hstack(waves)

wave = make_wave()

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import tensorflow as tf

size = 2048
N = 15000

(xs, ys, rows, cols) = build_training_data(N, size)

model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(rows * cols * 2, input_dim=size, use_bias=False)
])

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def build_training_data(batch_size, sample_size):
  xs = np.random.randn(batch_size, sample_size)
  f, t, ys = spectrogram(xs)

  (num_rows, num_cols) = (ys.shape[1], ys.shape[2])
  print(ys.shape)

  ys = ys.reshape(batch_size, num_rows * num_cols)
  Ys = np.hstack([ys.real, ys.imag])
  return (xs, Ys, num_rows, num_cols)

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import numpy as np
import matplotlib.pyplot as plt
from scipy import signal

def spectrogram(xs):
  return signal.spectrogram(xs, 256, window=('hann'), nperseg=256, nfft=256, noverlap=200, mode='complex')

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x = np.linspace(0, 2 * np.pi * hz, points * hz)
y = np.sin(x)
plt.figure(figsize=(10, 3))
plt.bar(np.arange(75), y[:75 * 10:10]) # sample every 10th element for bar graph
plt.show()

fft_y = fft.ifft(y.astype("complex64")) * 2
plt.figure(figsize=(10, 3))
plt.bar(np.arange(100), np.absolute(fft_y[:100]))
plt.show()