Machine Learning for Industrial Applications

16Machine Learning for Industrial Applications

16.1 Python Code for Generating Music Using Neural Networks

import numpy as np

import pandas as pd

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import LSTM, Dense, Dropout

from tensorflow.keras.callbacks import ModelCheckpoint

from tensorflow.keras.utils import to_categorical

# Load and preprocess data

data = pd.read_csv(‘music_data.csv’) # Your music dataset

notes = sorted(set(data[‘note’]))

note_to_int = dict((note, number) for number, note in enumerate(notes))

sequence_length = 100 # Adjust according to your data and model

architecture

network_input = []

network_output = []

for i in range(0, len(data) - sequence_length, 1):

  sequence_in = data[‘note’][i:i + sequence_length]

  sequence_out = data[‘note’][i + sequence_length]

  network_input.append([note_to_int[char] for char in sequence_in])

  network_output.append(note_to_int[sequence_out])

n_patterns = len(network_input)

network_input = np.reshape(network_input, (n_patterns, sequence_length, 1))

network_input = network_input / float(len(notes))

network_output = to_categorical(network_output)

# Define LSTM model

model = Sequential()

model.add(LSTM(

  256,

  input_shape=(network_input.shape[1], network_input.shape[2]),

  return_sequences=True

))

model.add(Dropout(0.3))

model.add(LSTM(512, return_sequences=True))

model.add(Dropout(0.3))

model.add(LSTM(256))

model.add(Dense(256))

model.add(Dropout(0.3))

model.add(Dense(len(notes), activation=’softmax’))) ...