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feat: make func for cross validation based on session split

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Skudalen 2021-07-13 10:59:47 +02:00
parent c009b0cdb2
commit e0e88abf41
1 changed files with 99 additions and 27 deletions
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126
Neural_Network_Analysis.py
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@ -4,9 +4,11 @@ import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
import tensorflow.keras as keras
from keras import backend as K
from pathlib import Path
import pandas as pd
import matplotlib.pyplot as plt
import statistics
# Path to json file that stores MFCCs and subject labels for each processed sample
DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data.json"
@ -14,7 +16,7 @@ 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), session_lengths
def load_data_from_json(data_path):
def load_data_from_json(data_path, nr_classes):
with open(data_path, "r") as fp:
data = json.load(fp)
@ -23,12 +25,12 @@ def load_data_from_json(data_path):
X = np.array(data['mfcc'])
#print(X.shape)
X = X.reshape(X.shape[0], 1, X.shape[1])
#print(X.shape)
print(X.shape)
y = np.array(data["labels"])
#print(y.shape)
y = y.reshape(y.shape[0], 1)
#print(y.shape)
y = keras.utils.to_categorical(y, nr_classes)
print(y.shape)
session_lengths = np.array(data['session_lengths'])
#print(session_lengths.shape)
@ -188,18 +190,26 @@ def RNN_LSTM(input_shape, nr_classes=5):
# Trains the model
# Input: Keras.model, batch_size, nr epochs, training, and validation data
# Ouput: History
def train(model, X_train, X_validation, y_train, y_validation, batch_size=64, epochs=30):
def train(model, X_train, y_train, verbose, batch_size=64, epochs=30, X_validation=None, y_validation=None):
optimiser = keras.optimizers.Adam(learning_rate=0.0001)
model.compile(optimizer=optimiser,
loss='sparse_categorical_crossentropy',
loss='categorical_crossentropy',
metrics=['accuracy'])
history = model.fit(X_train,
y_train,
validation_data=(X_validation, y_validation),
batch_size=batch_size,
epochs=epochs)
if X_validation != None:
history = model.fit(X_train,
y_train,
validation_data=(X_validation, y_validation),
batch_size=batch_size,
epochs=epochs,
verbose=verbose)
else:
history = model.fit(X_train,
y_train,
batch_size=batch_size,
epochs=epochs,
verbose=verbose)
return history
@ -223,33 +233,95 @@ def print_session_train_data(X_train, X_test, y_train, y_test, session_lengths,
print(y_test.shape)
print('Datapoints in session ' + str(session_nr) + ':', get_nr_in_session(session_lengths, session_nr))
print('Should be remaining:', 2806 - get_nr_in_session(session_lengths, session_nr))
# Reshapes training og test data into batches
# Input: training, test data (and validation), batch_size
# Ouput: training, test data (and validation)
def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes=5, X_validation=None, y_validation=None):
train_splits = X_train.shape[0] // batch_size
train_rest = X_train.shape[0] % batch_size
test_splits = X_test.shape[0] // batch_size
test_rest = X_test.shape[0] % batch_size
X_train = X_train[:-train_rest]
y_train = y_train[:-train_rest]
X_test = X_test[:-test_rest]
y_test = y_test[:-test_rest]
X_train_batch = np.reshape(X_train, (batch_size, train_splits, 208))
y_train_batch = np.reshape(y_train, (batch_size, train_splits, nr_classes))
X_test_batch = np.reshape(X_test, (batch_size, test_splits, 208))
y_test_batch = np.reshape(y_test, (batch_size, test_splits, nr_classes))
if X_validation != None:
val_splits = X_validation.shape[0] // batch_size
val_rest = X_validation.shape[0] % batch_size
X_validation = X_validation[:-val_rest]
y_validation = y_validation[:-val_rest]
X_val_batch = np.reshape(X_validation, (batch_size, val_splits, 208))
y_val_batch = np.reshape(y_validation, (batch_size, val_splits))
return X_train_batch, X_test_batch, y_train_batch, y_test_batch, X_val_batch, y_val_batch
return X_train_batch, X_test_batch, y_train_batch, y_test_batch
def session_cross_validation(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
session_training_results = []
for i in range(nr_sessions):
model = RNN_LSTM(input_shape=(1, 208))
X_train_session, X_test_session, y_train_session, y_test_session = prepare_datasets_sessions(X, y, session_lengths, i)
train(model, X_train_session, y_train_session, verbose=0, batch_size=batch_size, epochs=epochs)
test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2)
session_training_results.append(test_acc)
del model
K.clear_session()
print('Session', i, 'as test data gives accuracy:', test_acc)
average_result = statistics.mean((session_training_results))
return average_result, session_training_results
if __name__ == "__main__":
# Load data
X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC)
# ----- Load data ------
# X.shape = (2806, 1, 208)
# y.shape = (2806, 5)
# session_lengths.shape = (5, 4)
X, y, session_lengths = load_data_from_json(DATA_PATH_MFCC, nr_classes=5)
# Get prepared data: train, validation, and test
session_nr = 4
# Parameters:
NR_SUBJECTS = 5
NR_SESSIONS = 4
BATCH_SIZE = 64
EPOCHS = 30
# ----- Get prepared data: train, validation, and test ------
'''
X_train, X_test, y_train, y_test = prepare_datasets_sessions(X, y, session_lengths, session_nr)
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
#print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr)
'''
#'''
# ----- Make model ------
#model = RNN_LSTM(input_shape=(1, 208)) # (timestep, coefficients)
#model.summary()
# Make model
model = RNN_LSTM(input_shape=(1, 208))
model.summary()
# Train network
history = train(model, X_train, X_validation, y_train, y_validation, batch_size=64, epochs=30)
# ----- Train network ------
#history = train(model, X_train, y_train, batch_size=batch_size, epochs=30)
average = session_cross_validation(X, y, session_lengths, NR_SESSIONS, BATCH_SIZE, EPOCHS)
print('\nCrossvalidated:', average)
# plot accuracy/error for training and validation
plot_history(history)
#plot_history(history)
# evaluate model on test set
test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)
print('\nTest accuracy:', test_acc)
# ----- Evaluate model on test set ------
#test_loss, test_acc = model.evaluate(X_test, y_test, verbose=1)
#print('\nTest accuracy:', test_acc)
#'''