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drumset.py
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drumset.py
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"""
Contains the `DrumSetSequence` class.
Uses TensorFlow, Keras and numpy
"""
from tensorflow import keras
import numpy as np
import utils
import os
class DrumSetSequence(keras.utils.Sequence):
"""
This class extends `keras.utils.Sequence` to wrap the drumset pre-processed data in an easy-to-access (for the neural network) way.
"""
def __init__(self, inputs, batch_size, buffer_size, token_count, dictionary):
self.batch_size = batch_size
self.inputs = inputs
self.buffer_size = buffer_size
self.token_count = token_count
self.dictionary = dictionary
def __getitem__(self, index):
"""
Used by TensorFlow when training to fetch inputs and target outputs.
Returns two values, the inputs `Xs` (previous timesteps and intensities) and the output `ys` (next timestep, no intensity)
`index` is not used as the fetched window is random.
"""
Xs = np.zeros((self.buffer_size - 1, self.token_count, self.batch_size))
ys = np.zeros((self.token_count, self.batch_size))
for x in range(self.batch_size):
cut = utils.get_random_cut(self.inputs, self.buffer_size)
new_input = utils.categorical_encoder(cut, self.token_count, self.dictionary)
output_x = new_input[:self.buffer_size - 1]
output_y = new_input[self.buffer_size - 1:]
output_x = output_x.astype("float32")
output_y = output_y.astype("float32")
Xs[:,:,x] = output_x
ys[:,x] = output_y
# delete final column as intensity not needed as prediction.
ys = np.delete(ys, [self.token_count - 1], 0)
Xs = Xs.transpose((2, 0, 1))
ys = ys.transpose((1, 0))
return Xs, ys
def __len__(self):
"""
Not really applicable for this style of dataset.
Returns an appropriate value given the batch size.
"""
return int(len(self.inputs) / self.batch_size)
def on_epoch_end(self):
pass