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doc: add comments to NN file

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Skudalen 2021-07-09 14:53:47 +02:00
parent 81d4a53335
commit 8faa352af9
1 changed files with 20 additions and 6 deletions
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Neural_Network_Analysis.py
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@ -8,9 +8,12 @@ from pathlib import Path
import pandas as pd import pandas as pd
import matplotlib.pyplot as plt 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" 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): def load_data_from_json(data_path):
with open(data_path, "r") as fp: with open(data_path, "r") as fp:
@ -30,7 +33,10 @@ def load_data_from_json(data_path):
return X, y 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): def plot_history(history):
"""Plots accuracy/loss for training/validation set as a function of the epochs """Plots accuracy/loss for training/validation set as a function of the epochs
:param history: Training history of model :param history: Training history of model
@ -56,8 +62,10 @@ def plot_history(history):
plt.show() plt.show()
# Takes in data and labels, and splits it into train, validation and test sets
def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_size=0.25,): # 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 # 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) 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 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): def RNN_LSTM(input_shape, nr_classes=5):
"""Generates RNN-LSTM model """Generates RNN-LSTM model
:param input_shape (tuple): Shape of input set :param input_shape (tuple): Shape of input set
@ -88,6 +98,9 @@ def RNN_LSTM(input_shape, nr_classes=5):
return model 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): def train(model, batch_size, epochs, X_train, X_validation, y_train, y_validation):
optimiser = keras.optimizers.Adam(learning_rate=0.0001) 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) epochs=epochs)
return history return history
if __name__ == "__main__": if __name__ == "__main__":
# Load data # Load data
@ -112,7 +126,7 @@ if __name__ == "__main__":
validation_size=0.2, validation_size=0.2,
test_size=0.25, test_size=0.25,
shuffle_vars=True) shuffle_vars=True)
#print(X_train.shape[1], X_train.shape[2]) print(X_train.shape)
# Make model # Make model
model = RNN_LSTM(input_shape=(1, 208)) model = RNN_LSTM(input_shape=(1, 208))