135 lines
4.1 KiB
Python
135 lines
4.1 KiB
Python
import json
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from python_speech_features.python_speech_features.base import mfcc
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import numpy as np
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from sklearn.model_selection import train_test_split
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import tensorflow as tf
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import tensorflow.keras as keras
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from pathlib import Path
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import pandas as pd
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import matplotlib.pyplot as plt
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# path to json file that stores MFCCs and subject labels for each processed sample
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DATA_PATH = str(Path.cwd()) + "/mfcc_data.json"
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def load_data(data_path):
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with open(data_path, "r") as fp:
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data = json.load(fp)
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# convert lists to numpy arraysls
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X = np.array(data['mfcc'])
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X = X.reshape(X.shape[0], 1, X.shape[1])
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#print(X.shape)
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y = np.array(data["labels"])
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y = y.reshape(y.shape[0], 1)
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#print(y.shape)
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print("Data succesfully loaded!")
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return X, y
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def plot_history(history):
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"""Plots accuracy/loss for training/validation set as a function of the epochs
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:param history: Training history of model
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:return:
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"""
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fig, axs = plt.subplots(2)
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# create accuracy sublpot
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axs[0].plot(history.history["accuracy"], label="train accuracy")
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axs[0].plot(history.history["val_accuracy"], label="test accuracy")
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axs[0].set_ylabel("Accuracy")
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axs[0].legend(loc="lower right")
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axs[0].set_title("Accuracy eval")
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# create error sublpot
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axs[1].plot(history.history["loss"], label="train error")
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axs[1].plot(history.history["val_loss"], label="test error")
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axs[1].set_ylabel("Error")
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axs[1].set_xlabel("Epoch")
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axs[1].legend(loc="upper right")
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axs[1].set_title("Error eval")
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plt.show()
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def prepare_datasets(test_size=0.25, validation_size=0.2):
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"""Loads data and splits it into train, validation and test sets.
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:param test_size (float): Value in [0, 1] indicating percentage of data set to allocate to test split
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:param validation_size (float): Value in [0, 1] indicating percentage of train set to allocate to validation split
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:return X_train (ndarray): Input training set
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:return X_validation (ndarray): Input validation set
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:return X_test (ndarray): Input test set
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:return y_train (ndarray): Target training set
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:return y_validation (ndarray): Target validation set
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:return y_test (ndarray): Target test set
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"""
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# load data
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X, y = load_data(DATA_PATH)
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# create train, validation and test split
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size)
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X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size)
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return X_train, X_validation, X_test, y_train, y_validation, y_test
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def build_model(input_shape, nr_classes=5):
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"""Generates RNN-LSTM model
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:param input_shape (tuple): Shape of input set
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:return model: RNN-LSTM model
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"""
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# build network topology
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model = keras.Sequential()
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# 2 LSTM layers
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model.add(keras.layers.LSTM(64, input_shape=input_shape, return_sequences=True))
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model.add(keras.layers.LSTM(64))
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# dense layer
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model.add(keras.layers.Dense(64, activation='relu'))
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model.add(keras.layers.Dropout(0.3))
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# output layer
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model.add(keras.layers.Dense(nr_classes, activation='softmax'))
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return model
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if __name__ == "__main__":
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# get train, validation, test splits
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X_train, X_validation, X_test, y_train, y_validation, y_test = prepare_datasets(0.25, 0.2)
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print(X_train.shape[1], X_train.shape[2])
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# create network
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input_shape = (X_train.shape[1], X_train.shape[2]) # (~2800), 1, 208
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model = build_model(input_shape)
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# compile model
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optimiser = keras.optimizers.Adam(learning_rate=0.0001)
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model.compile(optimizer=optimiser,
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loss='sparse_categorical_crossentropy',
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metrics=['accuracy'])
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model.summary()
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# train model
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history = model.fit(X_train, y_train, validation_data=(X_validation, y_validation), batch_size=64, epochs=30)
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# plot accuracy/error for training and validation
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plot_history(history)
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# evaluate model on test set
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test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
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print('\nTest accuracy:', test_acc)
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