From 8faa352af9734a401e90dbf7316054898c3299e3 Mon Sep 17 00:00:00 2001 From: Skudalen Date: Fri, 9 Jul 2021 14:53:47 +0200 Subject: [PATCH] doc: add comments to NN file --- Neural_Network_Analysis.py | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/Neural_Network_Analysis.py b/Neural_Network_Analysis.py index 13b42ca..c429f5a 100644 --- a/Neural_Network_Analysis.py +++ b/Neural_Network_Analysis.py @@ -8,9 +8,12 @@ from pathlib import Path import pandas as pd import matplotlib.pyplot as plt -# path to json file that stores MFCCs and subject labels for each processed sample +# Path to json file that stores MFCCs and subject labels for each processed sample DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data.json" +# Loads data from the json file and reshapes X_data(samples, 1, 208) and y_data(samples, 1) +# Input: JSON path +# Ouput: X(mfcc data), y(labels) def load_data_from_json(data_path): with open(data_path, "r") as fp: @@ -30,7 +33,10 @@ def load_data_from_json(data_path): return X, y - +# Plots the training history with two subplots. First training and test accuracy, and then +# loss with respect to epochs +# Input: History(from model.fit(...)) +# Ouput: None -> plot def plot_history(history): """Plots accuracy/loss for training/validation set as a function of the epochs :param history: Training history of model @@ -56,8 +62,10 @@ def plot_history(history): plt.show() - -def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_size=0.25,): +# Takes in data and labels, and splits it into train, validation and test sets +# Input: Data, labels, whether to shuffle, % validatiion, % test +# Ouput: X_train, X_validation, X_test, y_train, y_validation, y_test +def prepare_datasets_percentsplit(X, y, shuffle_vars:bool, validation_size=0.2, test_size=0.25,): # create train, validation and test split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, shuffle=shuffle_vars) @@ -65,7 +73,9 @@ def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_ return X_train, X_validation, X_test, y_train, y_validation, y_test - +# Creates a RNN_LSTM neural network model +# Input: input shape, classes of classification +# Ouput: model:Keras.model def RNN_LSTM(input_shape, nr_classes=5): """Generates RNN-LSTM model :param input_shape (tuple): Shape of input set @@ -88,6 +98,9 @@ def RNN_LSTM(input_shape, nr_classes=5): return model +# Trains the model +# Input: Keras.model, batch_size, nr epochs, training, and validation data +# Ouput: History def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validation): optimiser = keras.optimizers.Adam(learning_rate=0.0001) @@ -102,6 +115,7 @@ def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validatio epochs=epochs) return history + if __name__ == "__main__": # Load data @@ -112,7 +126,7 @@ if __name__ == "__main__": validation_size=0.2, test_size=0.25, shuffle_vars=True) - #print(X_train.shape[1], X_train.shape[2]) + print(X_train.shape) # Make model model = RNN_LSTM(input_shape=(1, 208))