Mohit Cheppudira 0xfe
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| [[language]] | |
| name = "go" | |
| auto-format = true | |
| formatter = { command = "goimports" } | |
| [[language]] | |
| name = "rust" | |
| auto-format = true | |
| language-servers = ["rust-analyzer"] |
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| [[language]] | |
| name = "go" | |
| auto-format = true | |
| formatter = { command = "goimports" } | |
| [[language]] | |
| name = "rust" | |
| auto-format = true | |
| language-servers = ["rust-analyzer"] |
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| <!DOCTYPE html> | |
| <!-- | |
| Try this out on: https://mo.town/chordonaut | |
| Prompt: | |
| Create a guitar chord generateor single-page-webapp, in a HTML file called chords.html. |
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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) |
0xfe
/ peek_nn_spectrogram_weights_2.py
Created
March 2, 2020 16:48
Peek into Spectrogram ANN weights (1D)
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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) |
0xfe
/ compare_scipy_vs_nn_log_scale.py
Created
March 2, 2020 16:38
Compare SciPy vs ANN: log-scaled spectrograms
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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() |
0xfe
/ spectrogram_scipy_vs_nn.py
Created
March 2, 2020 16:37
Compare SciPy vs ANN in generating spectrograms
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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() |
0xfe
/ spectrogram_keras_model.py
Created
March 2, 2020 16:31
Keras model for training spectrogram NN
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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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