1189 lines
54 KiB
Python
1189 lines
54 KiB
Python
import json
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from keras import callbacks
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from pandas.core.frame import DataFrame
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from psf_lib.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 keras import backend as K
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from keras.regularizers import l2
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from keras.callbacks import Callback, CSVLogger, ModelCheckpoint
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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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#from matplotlib.legend import _get_legend_handles_
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import statistics
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import csv
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# Path to json file that stores MFCCs and subject labels for each processed sample
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SOFT_DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data_soft.json"
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HARD_DATA_PATH_MFCC = str(Path.cwd()) + "/mfcc_data_hard.json"
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# Loads data from the json file and reshapes X_data(samples, 1, 208) and y_data(samples, 1)
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# Input: JSON path
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# Ouput: X(mfcc data), y(labels), session_lengths
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def load_data_from_json(data_path, nr_classes):
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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 arrays and reshapes them
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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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y = np.array(data["labels"])
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y = keras.utils.to_categorical(y, nr_classes)
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session_lengths = np.array(data['session_lengths'])
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print("Data succesfully loaded!")
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return X, y, session_lengths
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# ----- DATA HANDLING ------
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# Takes in data and labels, and splits it into train, validation and test sets by percentage
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# Input: Data, labels, whether to shuffle, % validatiion, % test
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# Ouput: X_train, X_validation, X_test, y_train, y_validation, y_test
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def prepare_datasets_percentsplit(X, y, shuffle_vars, validation_size=0.2, test_size=0.25,):
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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, shuffle=shuffle_vars)
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X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=validation_size, shuffle=shuffle_vars)
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return X_train, X_validation, X_test, y_train, y_validation, y_test
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# Takes in data and labels, and splits it into train and test sets by session
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# Input: Data, labels, session_lengths and test_session_index
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# Ouput: X_train, X_validation, X_test, y_train, y_validation, y_test
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def prepare_datasets_sessions(X, y, session_lengths, test_session_index=4, nr_subjects=5):
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session_lengths = list(session_lengths)
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subject_starting_index = 0
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start_test_index = subject_starting_index + sum(session_lengths[0][:test_session_index-1])
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end_test_index = start_test_index + session_lengths[0][test_session_index-1]
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end_subject_index = subject_starting_index + sum(session_lengths[0])
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# Testing to check correctly slicing
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'''
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print(session_lengths[0], 'Sum:', sum(session_lengths[0]))
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print('Subject start:', subject_starting_index)
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print('Test start:', start_test_index)
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print('Test end:', end_test_index)
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print('Subject end:', end_subject_index, '\n -------')
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'''
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if start_test_index == subject_starting_index:
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X_test = X[start_test_index:end_test_index]
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y_test = y[start_test_index:end_test_index]
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X_train = X[end_test_index:end_subject_index]
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y_train = y[end_test_index:end_subject_index]
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elif end_test_index == end_subject_index:
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#print(X[subject_starting_index:start_test_index].shape)
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X_train = X[subject_starting_index:start_test_index]
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y_train = y[subject_starting_index:start_test_index]
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X_test = X[start_test_index:end_test_index]
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#print(X[start_test_index:end_test_index].shape, '\n ---')
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y_test = y[start_test_index:end_test_index]
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else:
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X_train = X[subject_starting_index:start_test_index]
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y_train = y[subject_starting_index:start_test_index]
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X_test = X[start_test_index:end_test_index]
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y_test = y[start_test_index:end_test_index]
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X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
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y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
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#print(X_train.shape, '\n -------')
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subject_starting_index = max(end_subject_index, end_test_index)
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for i in range(1, nr_subjects):
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start_test_index = subject_starting_index + sum(session_lengths[i][:test_session_index-1])
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end_test_index = start_test_index + session_lengths[i][test_session_index-1]
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end_subject_index = subject_starting_index + sum(session_lengths[i])
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# Testing to check correctly slicing
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'''
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print(session_lengths[i], 'Sum:', sum(session_lengths[i]))
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print('Subject start:', subject_starting_index)
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print('Test start:', start_test_index)
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print('Test end:', end_test_index)
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print('Subject end:', end_subject_index, '\n -------')
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'''
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if start_test_index == subject_starting_index:
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X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
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y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
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X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
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y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
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elif end_test_index == end_subject_index:
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#print(X[subject_starting_index:start_test_index].shape)
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X_train = np.concatenate((X_train, X[subject_starting_index:start_test_index]))
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y_train = np.concatenate((y_train, y[subject_starting_index:start_test_index]))
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#print(X[start_test_index:end_test_index].shape, '\n ---')
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X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
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y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
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else:
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X_train = np.concatenate((X_train, X[subject_starting_index:start_test_index]))
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y_train = np.concatenate((y_train, y[subject_starting_index:start_test_index]))
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X_test = np.concatenate((X_test, X[start_test_index:end_test_index]))
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y_test = np.concatenate((y_test, y[start_test_index:end_test_index]))
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X_train = np.concatenate((X_train, X[end_test_index:end_subject_index]))
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y_train = np.concatenate((y_train, y[end_test_index:end_subject_index]))
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#print(X_train.shape, '\n -------')
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subject_starting_index = max(end_subject_index, end_test_index)
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return X_train, X_test, y_train, y_test
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# NOT FUNCTIONAL, HAVE NOT LOCATED ERROR
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# Should be like func above, but with extended flexibility
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def prepare_datasets_new(test_session_indexes, X, y, session_lengths, nr_subjects=5, nr_sessions=4):
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X_list = []
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y_list = []
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for session_i in range(nr_sessions):
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X_session_list = []
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y_session_list = []
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for subject_i in range(nr_subjects):
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session_data_X = X[0:session_lengths[subject_i][session_i]]
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session_data_y = y[0:session_lengths[subject_i][session_i]]
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if session_i > 0:
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start_index = X_list[session_i-1].shape[0]
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session_data_X = X[start_index : start_index + session_lengths[subject_i][session_i]]
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session_data_y = y[start_index : start_index + session_lengths[subject_i][session_i]]
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X_session_list.append(session_data_X)
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y_session_list.append(session_data_y)
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X_list.append(np.concatenate(X_session_list))
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y_list.append(np.concatenate(y_session_list))
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X_test = []
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y_test = []
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X_train = []
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y_train = []
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for i in range(nr_sessions):
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if i in test_session_indexes:
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X_test.append(X_list[i])
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y_test.append(y_list[i])
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else:
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X_train.append(X_list[i])
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y_train.append(y_list[i])
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X_test = np.concatenate(X_test)
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y_test = np.concatenate(y_test)
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X_train = np.concatenate(X_train)
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y_train = np.concatenate(y_train)
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return X_train, X_test, y_train, y_test
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# Trains the model
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# Input: Keras.model, batch_size, nr epochs, training, and validation data
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# Ouput: History
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def train( model, X_train, y_train, verbose, batch_size=64, epochs=30,
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X_validation=None, y_validation=None):
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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='categorical_crossentropy',
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metrics=['accuracy'])
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#csv_path = str(Path.cwd()) + '/logs/{}/{}_train_log.csv'.format(MODEL_NAME, MODEL_NAME)
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#csv_logger = CSVLogger(csv_path, append=False)
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if X_validation.any():
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history = model.fit(X_train,
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y_train,
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validation_data=(X_validation, y_validation),
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batch_size=batch_size,
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epochs=epochs,
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verbose=verbose)
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else:
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history = model.fit(X_train,
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y_train,
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batch_size=batch_size,
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epochs=epochs,
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verbose=verbose)
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return history
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# Gives nr of datapoints for chosen session
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# Input: session_lengths 2d-list, session_nr, nr of subjects
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# Ouput: int(datapoints)
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def get_nr_in_session(session_lengths:list, session_nr, nr_subjects=5):
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summ = 0
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for i in range(nr_subjects):
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summ += session_lengths[i][session_nr-1]
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return summ
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# Prints session and training data
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# Input: None
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# Ouput: None -> print
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def print_session_train_data(X_train, X_test, y_train, y_test, session_lengths, session_nr):
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print(X_train.size)
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print(X_train.shape)
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print(X_test.shape)
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print(y_train.shape)
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print(y_test.shape)
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print('Datapoints in session ' + str(session_nr) + ':', get_nr_in_session(session_lengths, session_nr))
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print('Should be remaining:', 2806 - get_nr_in_session(session_lengths, session_nr))
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# Reshapes training og test data into batches NOT RELEVANT?
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# Input: training, test data (and validation), batch_size
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# Ouput: training, test data (and validation)
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def batch_formatting(X_train, X_test, y_train, y_test, batch_size=64, nr_classes=5, X_validation=None, y_validation=None):
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train_splits = X_train.shape[0] // batch_size
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train_rest = X_train.shape[0] % batch_size
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test_splits = X_test.shape[0] // batch_size
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test_rest = X_test.shape[0] % batch_size
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X_train = X_train[:-train_rest]
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y_train = y_train[:-train_rest]
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X_test = X_test[:-test_rest]
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y_test = y_test[:-test_rest]
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X_train_batch = np.reshape(X_train, (batch_size, train_splits, 208))
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y_train_batch = np.reshape(y_train, (batch_size, train_splits, nr_classes))
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X_test_batch = np.reshape(X_test, (batch_size, test_splits, 208))
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y_test_batch = np.reshape(y_test, (batch_size, test_splits, nr_classes))
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if X_validation != None:
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val_splits = X_validation.shape[0] // batch_size
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val_rest = X_validation.shape[0] % batch_size
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X_validation = X_validation[:-val_rest]
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y_validation = y_validation[:-val_rest]
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X_val_batch = np.reshape(X_validation, (batch_size, val_splits, 208))
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y_val_batch = np.reshape(y_validation, (batch_size, val_splits))
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return X_train_batch, X_test_batch, y_train_batch, y_test_batch, X_val_batch, y_val_batch
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return X_train_batch, X_test_batch, y_train_batch, y_test_batch
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# Retrieves data sets for each session as test set and evalutes. DOES USE prediction_csv_logger as default
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# the average of networks trained om them
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# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs
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# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
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def session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, log_to_csv=True, batch_size=64, epochs=30):
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session_training_results = []
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for i in range(nr_sessions):
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X_train_session, X_test_session, y_train_session, y_test_session = prepare_datasets_sessions(X, y, session_lengths, i)
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# Model:
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if model_name == 'LSTM':
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model = LSTM(input_shape=(1, 208))
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elif model_name == 'GRU':
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model = GRU(input_shape=(1, 208))
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elif model_name == 'CNN_1D':
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X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1))
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X_test_session = np.reshape(X_test_session, (X_test_session.shape[0], 208, 1))
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model = CNN_1D(input_shape=(208, 1))
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elif model_name == 'FFN':
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model = FFN(input_shape=(1, 208))
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else:
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raise Exception('Model not found')
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#model.summary()
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train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs)
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test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=2)
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session_training_results.append(test_acc)
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if log_to_csv:
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prediction_csv_logger(X_test_session, y_test_session, model_name, model, i)
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del model
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K.clear_session()
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#print('Session', i, 'as test data gives accuracy:', test_acc)
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average_result = statistics.mean((session_training_results))
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return average_result, session_training_results
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# Retrieves data sets for each session as train set and evalutes on the others.
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# the average of networks trained om them
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# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs
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# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
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def inverse_session_cross_validation(model_name:str, X, y, session_lengths, nr_sessions, log_to_csv=True, batch_size=64, epochs=30):
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session_training_results = []
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for i in range(nr_sessions):
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X_test_session, X_train_session, y_test_session, y_train_session = prepare_datasets_sessions(X, y, session_lengths, i)
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# Model:
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if model_name == 'LSTM':
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model = LSTM(input_shape=(1, 208))
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elif model_name == 'GRU':
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model = GRU(input_shape=(1, 208))
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elif model_name == 'CNN_1D':
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X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1))
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X_test_session = np.reshape(X_test_session, (X_test_session.shape[0], 208, 1))
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model = CNN_1D(input_shape=(208, 1))
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elif model_name == 'FFN':
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model = FFN(input_shape=(1, 208))
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else:
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raise Exception('Model not found')
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train(model, X_train_session, y_train_session, verbose=1, batch_size=batch_size, epochs=epochs)
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test_loss, test_acc = model.evaluate(X_test_session, y_test_session, verbose=0)
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session_training_results.append(test_acc)
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if log_to_csv:
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custom_path = '/{}_train_session{}_log.csv'
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prediction_csv_logger(X_test_session, y_test_session, model_name, model, i, custom_path)
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del model
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K.clear_session()
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#print('Session', i, 'as test data gives accuracy:', test_acc)
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average_result = statistics.mean((session_training_results))
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return average_result, session_training_results
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# Takes in test data and logs input data and the prediction from a model
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# Input: raw data, session_lengths list, total nr of sessions, batch_size, and nr of epochs
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# Ouput: tuple(cross validation average, list(result for each dataset(len=nr_sessions)))
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def prediction_csv_logger(X, y, model_name, model, session_nr, custom_path=None):
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csv_path = str(Path.cwd()) + '/logs/{}/{}_session{}_log.csv'.format(model_name, model_name, session_nr+1)
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if custom_path:
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path = str(Path.cwd()) + '/logs/{}' + custom_path
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csv_path = path.format(model_name, model_name, session_nr+1)
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layerOutput = model.predict(X, verbose=0)
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with open(csv_path, 'w') as csv_file:
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writer = csv.writer(csv_file)
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writer.writerow(['input', 'prediction', 'solution'])
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data = zip(X, layerOutput, y)
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writer.writerows(data)
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csv_file.close()
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# Prints info about session data
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# Input: session_lengths
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# Output: None -> print
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def get_session_info(session_lengths_soft, session_lengths_hard):
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print('Soft: {}\nHard: {}'.format(session_lengths_soft, session_lengths_hard))
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soft_avg_sess = np.average(list(np.average(x) for x in session_lengths_soft))
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soft_avg_sub = np.sum(list(np.average(x) for x in session_lengths_soft))
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hard_avg_sub = np.sum(list(np.average(x) for x in session_lengths_hard))
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hard_avg_sess = np.average(list(np.average(x) for x in session_lengths_hard))
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print('Avg session:', soft_avg_sess, hard_avg_sess)
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print('Avg sub:', soft_avg_sub, hard_avg_sub)
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# Reduces the size of the train and test set with values [0.0, 1.0]
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# Input: Data sets, how much to reduce train set, how much to reduce test set with
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# Output: Reduced data sets
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def reduce_data_set_sizes(X_train, X_test, y_train, y_test, train_reduction=0.5, test_reduction=0, nr_subjects=5):
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X_train = np.array_split(X_train, nr_subjects)
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y_train = np.array_split(y_train, nr_subjects)
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X_test = np.array_split(X_test, nr_subjects)
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y_test = np.array_split(y_test, nr_subjects)
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train_keep = int(X_train[0].shape[0] * (1 - train_reduction))
|
|
test_keep = int(X_test[0].shape[0] * (1 - test_reduction))
|
|
|
|
for i in range(nr_subjects):
|
|
#print(len(X_train[i]))
|
|
X_train[i] = X_train[i][:train_keep]
|
|
y_train[i] = y_train[i][:train_keep]
|
|
X_test[i] = X_test[i][:test_keep]
|
|
y_test[i] = y_test[i][:test_keep]
|
|
#print(len(X_train[i]))
|
|
|
|
X_train = np.concatenate(X_train, axis=0)
|
|
y_train = np.concatenate(y_train, axis=0)
|
|
X_test = np.concatenate(X_test, axis=0)
|
|
y_test = np.concatenate(y_test, axis=0)
|
|
|
|
return X_train, X_test, y_train, y_test
|
|
|
|
# ----- PLOTS ------
|
|
|
|
# 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_train_history(history, val_data=False):
|
|
|
|
fig, axs = plt.subplots(2)
|
|
|
|
# create accuracy sublpot
|
|
axs[0].plot(history.history["accuracy"], label="train accuracy")
|
|
if val_data:
|
|
axs[0].plot(history.history["val_accuracy"], label="validation accuracy")
|
|
axs[0].set_ylabel("Accuracy")
|
|
axs[0].legend(loc="lower right")
|
|
axs[0].set_title("Accuracy eval")
|
|
|
|
# create error sublpot
|
|
axs[1].plot(history.history["loss"], label="train error")
|
|
if val_data:
|
|
axs[1].plot(history.history["val_loss"], label="validation error")
|
|
axs[1].set_ylabel("Error")
|
|
axs[1].set_xlabel("Epoch")
|
|
axs[1].legend(loc="upper right")
|
|
axs[1].set_title("Error eval")
|
|
|
|
plt.show()
|
|
|
|
# Plots the training history of four networks inverse cross-validated (single trained)
|
|
# Input: data, nr of sessions in total, batch_size and epochs
|
|
# Ouput: None -> plot
|
|
def plot_comp_spread_single(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
|
|
|
|
history_dict = {'GRU': [],
|
|
'LSTM': [],
|
|
'FFN': [],
|
|
'CNN_1D': []}
|
|
|
|
for i in range(nr_sessions):
|
|
|
|
X_test_session, X_train_session, y_test_session, y_train_session = prepare_datasets_sessions(X, y, session_lengths, i)
|
|
|
|
model_GRU = GRU(input_shape=(1, 208))
|
|
GRU_h = train(model_GRU, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs)
|
|
history_dict['GRU'].append(GRU_h)
|
|
del model_GRU
|
|
K.clear_session()
|
|
|
|
model_LSTM = LSTM(input_shape=(1, 208))
|
|
LSTM_h = train(model_LSTM, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs)
|
|
history_dict['LSTM'].append(LSTM_h)
|
|
del model_LSTM
|
|
K.clear_session()
|
|
|
|
model_FFN = FFN(input_shape=(1, 208))
|
|
FFN_h = train(model_FFN, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs)
|
|
history_dict['FFN'].append(FFN_h)
|
|
del model_FFN
|
|
K.clear_session()
|
|
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1))
|
|
X_test_session = np.reshape(X_test_session, (X_test_session.shape[0], 208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs)
|
|
history_dict['CNN_1D'].append(CNN_1D_h)
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# Logging data to CSV. Just copy, not implemented
|
|
'''
|
|
# Log data stream to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_acc_data.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
|
|
data = zip(*history_dict.values(), *history_dict_val.values())
|
|
writer.writerows(data)
|
|
csv_file.close()
|
|
|
|
# Log best results to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_best.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
|
|
writer.writerow( [np.max(history_dict.get('GRU_train')), np.max(history_dict.get('LSTM_train')), np.max(history_dict.get('FFN_train')), np.max(history_dict.get('CNN_1D_train')),
|
|
np.max(history_dict_val.get('GRU_val')), np.max(history_dict_val.get('LSTM_val')), np.max(history_dict_val.get('FFN_val')), np.max(history_dict_val.get('CNN_1D_val'))] )
|
|
csv_file.close()
|
|
'''
|
|
|
|
fig, axs = plt.subplots(2, 2, sharey=True)
|
|
plt.ylim(0, 1)
|
|
|
|
# GRU plot:
|
|
axs[0, 0].plot(history_dict['GRU'][0].history["accuracy"])
|
|
axs[0, 0].plot(history_dict['GRU'][1].history["accuracy"], 'tab:orange')
|
|
axs[0, 0].plot(history_dict['GRU'][2].history["accuracy"], 'tab:green')
|
|
axs[0, 0].plot(history_dict['GRU'][3].history["accuracy"], 'tab:red')
|
|
axs[0, 0].set_title('GRU')
|
|
# LSTM plot:
|
|
axs[0, 1].plot(history_dict['LSTM'][0].history["accuracy"])
|
|
axs[0, 1].plot(history_dict['LSTM'][1].history["accuracy"], 'tab:orange')
|
|
axs[0, 1].plot(history_dict['LSTM'][2].history["accuracy"], 'tab:green')
|
|
axs[0, 1].plot(history_dict['LSTM'][3].history["accuracy"], 'tab:red')
|
|
axs[0, 1].set_title('LSTM')
|
|
# FFN plot:
|
|
axs[1, 0].plot(history_dict['FFN'][0].history["accuracy"])
|
|
axs[1, 0].plot(history_dict['FFN'][1].history["accuracy"], 'tab:orange')
|
|
axs[1, 0].plot(history_dict['FFN'][2].history["accuracy"], 'tab:green')
|
|
axs[1, 0].plot(history_dict['FFN'][3].history["accuracy"], 'tab:red')
|
|
axs[1, 0].set_title('FFN')
|
|
# CNN_1D plot:
|
|
axs[1, 1].plot(history_dict['CNN_1D'][0].history["accuracy"])
|
|
axs[1, 1].plot(history_dict['CNN_1D'][1].history["accuracy"], 'tab:orange')
|
|
axs[1, 1].plot(history_dict['CNN_1D'][2].history["accuracy"], 'tab:green')
|
|
axs[1, 1].plot(history_dict['CNN_1D'][3].history["accuracy"], 'tab:red')
|
|
axs[1, 1].set_title('CNN_1D')
|
|
|
|
for ax in axs.flat:
|
|
ax.set(xlabel='Epochs', ylabel='Accuracy')
|
|
|
|
# Hide x labels and tick labels for top plots and y ticks for right plots.
|
|
for ax in axs.flat:
|
|
ax.label_outer()
|
|
|
|
plt.show()
|
|
|
|
|
|
# Plots the average training history of four networks inverse cross-validated (single trained)
|
|
# Input: data, nr of sessions in total, batch_size and epochs
|
|
# Ouput: None -> plot
|
|
def plot_comp_accuracy_single(X, y, session_lengths, nr_sessions, batch_size=64, epochs=30):
|
|
#'''
|
|
history_dict = {'GRU_train': [],
|
|
'LSTM_train': [],
|
|
'FFN_train': [],
|
|
'CNN_1D_train': []}
|
|
history_dict_val = {'GRU_val': [],
|
|
'LSTM_val': [],
|
|
'FFN_val': [],
|
|
'CNN_1D_val': []}
|
|
|
|
for i in range(nr_sessions):
|
|
# Prepare data
|
|
X_val_session, X_train_session, y_val_session, y_train_session = prepare_datasets_sessions(X, y, session_lengths, i)
|
|
|
|
# GRU
|
|
model_GRU = GRU(input_shape=(1, 208))
|
|
GRU_h = train(model_GRU, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_session, y_validation=y_val_session)
|
|
history_dict['GRU_train'].append(GRU_h.history['accuracy'])
|
|
history_dict_val['GRU_val'].append(GRU_h.history['val_accuracy'])
|
|
del model_GRU
|
|
K.clear_session()
|
|
|
|
# LSTM
|
|
model_LSTM = LSTM(input_shape=(1, 208))
|
|
LSTM_h = train(model_LSTM, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_session, y_validation=y_val_session)
|
|
history_dict['LSTM_train'].append(LSTM_h.history['accuracy'])
|
|
history_dict_val['LSTM_val'].append(LSTM_h.history['val_accuracy'])
|
|
del model_LSTM
|
|
K.clear_session()
|
|
|
|
# FFN
|
|
model_FFN = FFN(input_shape=(1, 208))
|
|
FFN_h = train(model_FFN, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_session, y_validation=y_val_session)
|
|
history_dict['FFN_train'].append(FFN_h.history['accuracy'])
|
|
history_dict_val['FFN_val'].append(FFN_h.history['val_accuracy'])
|
|
del model_FFN
|
|
K.clear_session()
|
|
|
|
# CNN_1D
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
X_train_session = np.reshape(X_train_session, (X_train_session.shape[0], 208, 1))
|
|
X_val_session = np.reshape(X_val_session, (X_val_session.shape[0], 208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_session, y_train_session, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_session, y_validation=y_val_session)
|
|
history_dict['CNN_1D_train'].append(CNN_1D_h.history['accuracy'])
|
|
history_dict_val['CNN_1D_val'].append(CNN_1D_h.history['val_accuracy'])
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# Averaging out session training for each network
|
|
for key in history_dict:
|
|
history_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*history_dict[key])))
|
|
for key in history_dict_val:
|
|
history_dict_val[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*history_dict_val[key])))
|
|
|
|
'''
|
|
history_dict = {'GRU_train': [0.5, 0.8, 0.4, 0.8],
|
|
'LSTM_train': [0.5, 0.9, 0.3, 0.9],
|
|
'FFN_train': [0.75, 0.8, 0.2, 0.7],
|
|
'CNN_1D_train': [0.8, 0.95, 0.1, 0.6]}
|
|
history_dict_val = {'GRU_val': [0.5, 0.8, 0.4, 0.8],
|
|
'LSTM_val': [0.5, 0.9, 0.4, 0.8],
|
|
'FFN_val': [0.75, 0.8, 0.4, 0.8],
|
|
'CNN_1D_val': [0.8, 0.95, 0.4, 0.8]}
|
|
#'''
|
|
|
|
# Log data stream to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_acc_data.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
|
|
data = zip(*history_dict.values(), *history_dict_val.values())
|
|
writer.writerows(data)
|
|
csv_file.close()
|
|
|
|
# Log best results to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Network_acc_comparison_single/comparison_best.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['GRU_train_acc', 'LSTM_train_acc', 'FFN_train_acc', 'CNN_1D_train_acc', 'GRU_val_acc', 'LSTM_val_acc', 'FFN_val_acc', 'CNN_1D_val_acc'])
|
|
writer.writerow( [np.max(history_dict.get('GRU_train')), np.max(history_dict.get('LSTM_train')), np.max(history_dict.get('FFN_train')), np.max(history_dict.get('CNN_1D_train')),
|
|
np.max(history_dict_val.get('GRU_val')), np.max(history_dict_val.get('LSTM_val')), np.max(history_dict_val.get('FFN_val')), np.max(history_dict_val.get('CNN_1D_val'))] )
|
|
csv_file.close()
|
|
|
|
# Plot:
|
|
fig, axs = plt.subplots(2, sharey=True)
|
|
plt.ylim(0, 1)
|
|
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
|
|
fig.suptitle('Average accuracy with cross-session-training', fontsize=16)
|
|
|
|
axs[0].plot(history_dict['CNN_1D_train'], ':', label='CNN_1D')
|
|
axs[0].plot(history_dict['LSTM_train'], '--', label='LSTM')
|
|
axs[0].plot(history_dict['GRU_train'], '-', label='GRU')
|
|
axs[0].plot(history_dict['FFN_train'], '-.', label='FFN')
|
|
axs[0].set_title('Training accuracy')
|
|
|
|
axs[1].plot(history_dict_val['CNN_1D_val'], ':', label='CNN_1D')
|
|
axs[1].plot(history_dict_val['LSTM_val'], '--', label='LSTM')
|
|
axs[1].plot(history_dict_val['GRU_val'], '-', label='GRU')
|
|
axs[1].plot(history_dict_val['FFN_val'], '-.', label='FFN')
|
|
axs[1].set_title('Validation accuracy')
|
|
|
|
for ax in axs.flat:
|
|
ax.set(xlabel='Epochs', ylabel='Accuracy')
|
|
|
|
plt.legend(bbox_to_anchor=(1.05, 1.5), title='Models used\n', loc='center left')
|
|
plt.style.use('seaborn-dark-palette')
|
|
plt.show()
|
|
|
|
# Plots training and validation history for CNN_1D network with SOFT and HARD data (single trained)
|
|
# Input: SOFT and HARD raw data, respective session_lengths, *details
|
|
# Output: None -> plot
|
|
def plot_comp_SoftHard_single(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
|
|
#'''
|
|
train_dict = {'SOFT':[], 'HARD':[]}
|
|
val_dict = {'SOFT':[], 'HARD':[]}
|
|
|
|
for i in range(nr_sessions):
|
|
# Prepare data
|
|
X_val_soft, X_train_soft, y_val_soft, y_train_soft = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, i)
|
|
X_val_hard, X_train_hard, y_val_hard, y_train_hard = prepare_datasets_sessions(X_hard, y_hard, session_lengths_hard, i)
|
|
X_train_soft = np.reshape(X_train_soft, (X_train_soft.shape[0], 208, 1))
|
|
X_val_soft = np.reshape(X_val_soft, (X_val_soft.shape[0], 208, 1))
|
|
X_train_hard = np.reshape(X_train_hard, (X_train_hard.shape[0], 208, 1))
|
|
X_val_hard = np.reshape(X_val_hard, (X_val_hard.shape[0], 208, 1))
|
|
|
|
# CNN_1D SOFT
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_soft, y_train_soft, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_soft, y_validation=y_val_soft)
|
|
train_dict['SOFT'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['SOFT'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# CNN_1D HARD
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_hard, y_train_hard, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_hard, y_validation=y_val_hard)
|
|
train_dict['HARD'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['HARD'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# Averaging out session training for each network
|
|
for key in train_dict:
|
|
train_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*train_dict[key])))
|
|
for key in val_dict:
|
|
val_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*val_dict[key])))
|
|
|
|
|
|
'''
|
|
train_dict = {'SOFT': [0.1, 0.7, 0.5, 0.69],
|
|
'HARD': [0.55, 0.9, 0.3, 0.92]}
|
|
val_dict = {'SOFT': [0.34, 0.85, 0.41, 0.74],
|
|
'HARD': [0.63, 0.99, 0.49, 0.88]}
|
|
'''
|
|
|
|
# Log data stream to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_single/soft_hard_comparison_acc_data.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['soft_train_acc', 'hard_train_acc', 'soft_val_acc', 'hard_val_acc'])
|
|
data = zip(*train_dict.values(), *val_dict.values())
|
|
writer.writerows(data)
|
|
csv_file.close()
|
|
|
|
# Log best results to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_single/soft_hard_comparison_best.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['soft_train_best', 'hard_train_best', 'soft_val_best', 'hard_val_best'])
|
|
writer.writerow( [np.max(train_dict.get('SOFT')), np.max(train_dict.get('HARD')), np.max(val_dict.get('SOFT')), np.max(val_dict.get('HARD'))] )
|
|
csv_file.close()
|
|
|
|
# Plot:
|
|
fig, axs = plt.subplots(2, sharey=True)
|
|
plt.ylim(0, 1)
|
|
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
|
|
fig.suptitle('Model training (1x session) and validation (3x session) with Natural/Strong typing behavior', fontsize=16)
|
|
|
|
axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D Natural')
|
|
axs[0].plot(train_dict['HARD'], '--', label='CNN_1D Strong')
|
|
axs[0].set_title('Training accuracy')
|
|
|
|
axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D Natural')
|
|
axs[1].plot(val_dict['HARD'], '--', label='CNN_1D Strong')
|
|
axs[1].set_title('Validation accuracy')
|
|
|
|
for ax in axs.flat:
|
|
ax.set(xlabel='Epochs', ylabel='Accuracy')
|
|
|
|
plt.legend(bbox_to_anchor=(1.05, 1.5), title='Typing behavior evaluated\n', loc='center left')
|
|
plt.style.use('seaborn-dark-palette')
|
|
plt.show()
|
|
|
|
|
|
# Plots training and validation history for CNN_1D network with SOFT and HARD data (three-session-trained)
|
|
# Input: SOFT and HARD raw data, respective session_lengths, *details
|
|
# Output: None -> plot
|
|
def plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
|
|
#'''
|
|
train_dict = {'SOFT':[], 'HARD':[]}
|
|
val_dict = {'SOFT':[], 'HARD':[]}
|
|
|
|
for i in range(nr_sessions):
|
|
# Prepare data
|
|
X_train_soft, X_val_soft, y_train_soft, y_val_soft = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, i)
|
|
X_train_hard, X_val_hard, y_train_hard, y_val_hard = prepare_datasets_sessions(X_hard, y_hard, session_lengths_hard, i)
|
|
X_train_soft = np.reshape(X_train_soft, (X_train_soft.shape[0], 208, 1))
|
|
X_val_soft = np.reshape(X_val_soft, (X_val_soft.shape[0], 208, 1))
|
|
X_train_hard = np.reshape(X_train_hard, (X_train_hard.shape[0], 208, 1))
|
|
X_val_hard = np.reshape(X_val_hard, (X_val_hard.shape[0], 208, 1))
|
|
|
|
# CNN_1D SOFT
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_soft, y_train_soft, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_soft, y_validation=y_val_soft)
|
|
train_dict['SOFT'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['SOFT'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
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|
K.clear_session()
|
|
|
|
# CNN_1D HARD
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_hard, y_train_hard, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_hard, y_validation=y_val_hard)
|
|
train_dict['HARD'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['HARD'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# Averaging out session training for each network
|
|
for key in train_dict:
|
|
train_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*train_dict[key])))
|
|
for key in val_dict:
|
|
val_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*val_dict[key])))
|
|
|
|
|
|
'''
|
|
train_dict = {'SOFT': [0.1, 0.7, 0.5, 0.69],
|
|
'HARD': [0.55, 0.9, 0.3, 0.92]}
|
|
val_dict = {'SOFT': [0.34, 0.85, 0.41, 0.74],
|
|
'HARD': [0.63, 0.99, 0.49, 0.88]}
|
|
'''
|
|
|
|
# Log data stream to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_acc_data.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['soft_train_acc', 'hard_train_acc', 'soft_val_acc', 'hard_val_acc'])
|
|
data = zip(*train_dict.values(), *val_dict.values())
|
|
writer.writerows(data)
|
|
csv_file.close()
|
|
|
|
# Log best results to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_best.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['soft_train_best', 'hard_train_best', 'soft_val_best', 'hard_val_best'])
|
|
writer.writerow( [np.max(train_dict.get('SOFT')), np.max(train_dict.get('HARD')), np.max(val_dict.get('SOFT')), np.max(val_dict.get('HARD'))] )
|
|
csv_file.close()
|
|
|
|
# Plot:
|
|
fig, axs = plt.subplots(2, sharey=True)
|
|
plt.ylim(0, 1)
|
|
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
|
|
fig.suptitle('Model training (3x session) and validation (1x session) with Natural/Strong typing behavior', fontsize=16)
|
|
|
|
axs[0].plot(train_dict['SOFT'], ':', label='CNN_1D Natural')
|
|
axs[0].plot(train_dict['HARD'], '--', label='CNN_1D Strong')
|
|
axs[0].set_title('Training accuracy')
|
|
|
|
axs[1].plot(val_dict['SOFT'], ':', label='CNN_1D Natural')
|
|
axs[1].plot(val_dict['HARD'], '--', label='CNN_1D Strong')
|
|
axs[1].set_title('Validation accuracy')
|
|
|
|
for ax in axs.flat:
|
|
ax.set(xlabel='Epochs', ylabel='Accuracy')
|
|
|
|
plt.legend(bbox_to_anchor=(1.05, 1.5), title='Typing behavior evaluated\n', loc='center left')
|
|
plt.style.use('seaborn-dark-palette')
|
|
plt.show()
|
|
|
|
|
|
# Plots training and validation history for CNN_1D network with SOFT and HARD data (VAL, two data sets)
|
|
# Input: SOFT and HARD raw data, respective session_lengths, *details
|
|
# Output: None -> plot
|
|
def plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, nr_sessions, batch_size=64, epochs=30):
|
|
#'''
|
|
#train_dict = {'SOFT':[], 'HARD':[], 'SOFT_1':[], 'HARD_1':[]}
|
|
val_dict = {'SOFT':[], 'HARD':[], 'SOFT_1':[], 'HARD_1':[]}
|
|
|
|
for i in range(nr_sessions):
|
|
# Prepare data
|
|
X_train_soft, X_val_soft, y_train_soft, y_val_soft = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, i)
|
|
X_train_hard, X_val_hard, y_train_hard, y_val_hard = prepare_datasets_sessions(X_hard, y_hard, session_lengths_hard, i)
|
|
X_train_soft = np.reshape(X_train_soft, (X_train_soft.shape[0], 208, 1))
|
|
X_val_soft = np.reshape(X_val_soft, (X_val_soft.shape[0], 208, 1))
|
|
X_train_hard = np.reshape(X_train_hard, (X_train_hard.shape[0], 208, 1))
|
|
X_val_hard = np.reshape(X_val_hard, (X_val_hard.shape[0], 208, 1))
|
|
|
|
# CNN_1D SOFT
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_soft, y_train_soft, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_soft, y_validation=y_val_soft)
|
|
#train_dict['SOFT'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['SOFT'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# CNN_1D HARD
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_train_hard, y_train_hard, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_val_hard, y_validation=y_val_hard)
|
|
#train_dict['HARD'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['HARD'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# ------ Single:
|
|
|
|
# CNN_1D SOFT
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_val_soft, y_val_soft, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_train_soft, y_validation=y_train_soft)
|
|
#train_dict['SOFT_1'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['SOFT_1'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
# CNN_1D HARD
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1))
|
|
CNN_1D_h = train(model_CNN_1D, X_val_hard, y_val_hard, 1, batch_size=batch_size, epochs=epochs,
|
|
X_validation=X_train_hard, y_validation=y_train_hard)
|
|
#train_dict['HARD_1'].append(list(CNN_1D_h.history['accuracy']))
|
|
val_dict['HARD_1'].append(list(CNN_1D_h.history['val_accuracy']))
|
|
del model_CNN_1D
|
|
K.clear_session()
|
|
|
|
|
|
# Averaging out session training for each network
|
|
#for key in train_dict:
|
|
# train_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*train_dict[key])))
|
|
for key in val_dict:
|
|
val_dict[key] = list(np.average([x, y, z, c]) for x, y, z, c in list(zip(*val_dict[key])))
|
|
|
|
|
|
'''
|
|
train_dict = {'SOFT': [0.1, 0.7, 0.5, 0.69],
|
|
'HARD': [0.55, 0.9, 0.3, 0.92]}
|
|
val_dict = {'SOFT': [0.34, 0.85, 0.41, 0.74],
|
|
'HARD': [0.63, 0.99, 0.49, 0.88]}
|
|
'''
|
|
'''
|
|
# Log data stream to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_acc_data.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['soft_train_acc', 'hard_train_acc', 'soft_val_acc', 'hard_val_acc'])
|
|
data = zip(*train_dict.values(), *val_dict.values())
|
|
writer.writerows(data)
|
|
csv_file.close()
|
|
|
|
# Log best results to CSV
|
|
csv_path = str(Path.cwd()) + '/logs/Soft_hard_comparison_3/soft_hard_comparison_best.csv'
|
|
with open(csv_path, 'w') as csv_file:
|
|
writer = csv.writer(csv_file)
|
|
writer.writerow(['soft_train_best', 'hard_train_best', 'soft_val_best', 'hard_val_best'])
|
|
writer.writerow( [np.max(train_dict.get('SOFT')), np.max(train_dict.get('HARD')), np.max(val_dict.get('SOFT')), np.max(val_dict.get('HARD'))] )
|
|
csv_file.close()
|
|
'''
|
|
|
|
# Plot:
|
|
fig, axs = plt.subplots(2, sharey=True)
|
|
plt.ylim(0, 1)
|
|
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
|
|
fig.suptitle('Model training and validation with Natural/Strong typing behavior', fontsize=16)
|
|
|
|
axs[0].plot(val_dict['SOFT'], ':', label='CNN_1D Natural')
|
|
axs[0].plot(val_dict['HARD'], '--', label='CNN_1D Strong')
|
|
axs[0].set_title('Validation accuracy (3 session training)')
|
|
|
|
axs[1].plot(val_dict['SOFT_1'], ':', label='CNN_1D Natural')
|
|
axs[1].plot(val_dict['HARD_1'], '--', label='CNN_1D Strong')
|
|
axs[1].set_title('Validation accuracy (1 session training)')
|
|
|
|
for ax in axs.flat:
|
|
ax.set(xlabel='Epochs', ylabel='Accuracy')
|
|
|
|
plt.legend(bbox_to_anchor=(1.05, 1.5), title='Typing behavior evaluated\n', loc='center left')
|
|
plt.style.use('seaborn-dark-palette')
|
|
plt.show()
|
|
|
|
# Plots training and validation history for CNN_1D network with SOFT and HARD data from CSV file
|
|
# Input: None -> CSV from path
|
|
# Output: None -> plot & CSV log
|
|
def plot_N_S_val_comp():
|
|
|
|
df_3 = pd.read_csv('/Users/Markus/Prosjekter git/Slovakia 2021/logs/Soft_hard_comparison_3/soft_hard_comparison_acc_data.csv')[['soft_val_acc', 'hard_val_acc']]
|
|
df_1 = pd.read_csv('/Users/Markus/Prosjekter git/Slovakia 2021/logs/Soft_hard_comparison_single/soft_hard_comparison_acc_data.csv')[['soft_val_acc', 'hard_val_acc']]
|
|
|
|
df_3 = df_3.rename(columns={'soft_val_acc': 'natural_val_3', 'hard_val_acc': 'strong_val_3'})
|
|
df_1 = df_1.rename(columns={'soft_val_acc': 'natural_val_1', 'hard_val_acc': 'strong_val_1'})
|
|
comp_df = pd.concat([df_3, df_1], axis=1)
|
|
comp_df.to_csv('logs/Natural_Strong_comp_comb/N_S_val_comp.csv')
|
|
|
|
# Plot new N/S val comp:
|
|
fig, axs = plt.subplots(nrows=1, ncols=2, sharey=True, sharex=True, figsize=(13, 4))
|
|
plt.ylim(0, 1)
|
|
plt.subplots_adjust(hspace=1.0, top=0.85, bottom=0.15, right=0.75)
|
|
fig.text(0.435, 0.03, 'Epochs', ha='center')
|
|
fig.text(0.07, 0.5, 'Accuracy', va='center', rotation='vertical')
|
|
|
|
axs[0].plot(df_3['soft_val_acc'], ':', label='CNN_1D Natural')
|
|
axs[0].plot(df_3['hard_val_acc'], '--', label='CNN_1D Strong')
|
|
axs[0].set_title('Validation accuracy (3 session training)')
|
|
|
|
axs[1].plot(df_1['soft_val_acc'], ':', label='CNN_1D Natural')
|
|
axs[1].plot(df_1['hard_val_acc'], '--', label='CNN_1D Strong')
|
|
axs[1].set_title('Validation accuracy (1 session training)')
|
|
|
|
#for ax in axs:
|
|
# ax.set_xlabel('Epochs')
|
|
# ax.set_ylabel('Accuracy')
|
|
|
|
plt.legend(bbox_to_anchor=(1.75, 0.5), title='Typing behavior evaluated\n', loc='center right')
|
|
plt.ylim(0.50, 1.00)
|
|
plt.show()
|
|
|
|
|
|
# ----- MODELS ------
|
|
|
|
# Creates a keras.model with focus on LSTM layers
|
|
# Input: input shape, classes of classification
|
|
# Ouput: model:Keras.model
|
|
def LSTM(input_shape, nr_classes=5):
|
|
|
|
model = keras.Sequential(name='LSTM_model')
|
|
model.add(keras.layers.Bidirectional(keras.layers.LSTM(128), input_shape=input_shape, name='Bidirectional_LSTM'))
|
|
model.add(keras.layers.Dense(128, activation='relu', activity_regularizer=l2(0.005), name='Dense_relu'))
|
|
model.add(keras.layers.Dropout(0.3, name='Dropout'))
|
|
# Output layer
|
|
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Dense_relu_output'))
|
|
|
|
return model
|
|
|
|
# Creates a keras.model with focus on GRU layers
|
|
# Input: input shape, classes of classification
|
|
# Ouput: model:Keras.model
|
|
def GRU(input_shape, nr_classes=5):
|
|
|
|
model = keras.Sequential(name='GRU_model')
|
|
model.add(keras.layers.Bidirectional(keras.layers.GRU(128), input_shape=input_shape, name='Bidirectional_GRU'))
|
|
model.add(keras.layers.Dense(128, activation='relu', activity_regularizer=l2(0.005), name='Dense_relu'))
|
|
model.add(keras.layers.Dropout(0.3, name='Dropout'))
|
|
# Output layer:
|
|
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax'))
|
|
|
|
return model
|
|
|
|
# Creates a keras.model with a basic feed-forward-network
|
|
# Input: input shape, classes of classification
|
|
# Ouput: model:Keras.model
|
|
def FFN(input_shape, nr_classes=5):
|
|
|
|
model = keras.Sequential(name='FFN_model')
|
|
model.add(keras.layers.Reshape((input_shape[-1],), input_shape=input_shape))
|
|
model.add(keras.layers.Dense(256, activation='relu', input_shape=input_shape, name='Dense_relu_1'))
|
|
model.add(keras.layers.Dense(128, activation='relu', activity_regularizer=l2(0.005), name='Dense_relu_2'))
|
|
model.add(keras.layers.Dense(64, activation='relu', activity_regularizer=l2(0.005), name='Dense_relu_3'))
|
|
model.add(keras.layers.Dropout(0.3, name='Dropout'))
|
|
# Output layer:
|
|
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax'))
|
|
|
|
return model
|
|
|
|
# Creates a keras.model with focus on Convulotion layers
|
|
# Input: input shape, classes of classification
|
|
# Ouput: model:Keras.model
|
|
def CNN_1D(input_shape, nr_classes=5):
|
|
|
|
model = keras.Sequential(name='CNN_model')
|
|
model.add(keras.layers.Conv1D(32, kernel_size=5, activation='relu', input_shape=input_shape))
|
|
model.add(keras.layers.MaxPooling1D(pool_size=5))
|
|
model.add(keras.layers.Flatten())
|
|
model.add(keras.layers.Dense(128, activation='relu'))
|
|
model.add(keras.layers.Dropout(0.3))
|
|
# Ouput layer
|
|
model.add(keras.layers.Dense(nr_classes, activation='softmax', name='Softmax'))
|
|
|
|
return model
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
# ----- Load data ------
|
|
# X.shape = (2806, 1, 208)
|
|
# y.shape = (2806, nr_subjects)
|
|
# session_lengths.shape = (nr_subjects, nr_sessions)
|
|
'''
|
|
#X_soft, y_soft, session_lengths_soft = load_data_from_json(SOFT_DATA_PATH_MFCC, nr_classes=5)
|
|
#X_hard, y_hard, session_lengths_hard = load_data_from_json(HARD_DATA_PATH_MFCC, nr_classes=5)
|
|
'''
|
|
|
|
# PARAMS:
|
|
NR_SUBJECTS = 5
|
|
NR_SESSIONS = 4
|
|
BATCH_SIZE = 64
|
|
EPOCHS = 30
|
|
|
|
TEST_SESSION_NR = 4
|
|
VERBOSE = 1
|
|
MODEL_NAME = 'CNN_1D'
|
|
LOG = False
|
|
|
|
# ----- Get prepared data: train, validation, and test ------
|
|
# X_train.shape = (2806-X_test, 1, 208)
|
|
# X_test.shape = (X_test(from session nr. ?), 1, 208)
|
|
# y_train.shape = (2806-y_test, nr_subjects)
|
|
# y_test.shape = (y_test(from session nr. ?), nr_subjects)
|
|
'''
|
|
X_val, X_train, y_val, y_train = prepare_datasets_sessions(X_soft, y_soft, session_lengths_soft, TEST_SESSION_NR)
|
|
X_train, X_val, y_train, y_val = reduce_data_set_sizes(X_train, X_val, y_train, y_val, train_reduction=0.5, test_reduction=0)
|
|
print(X_soft.shape, y_soft.shape)
|
|
X_train, X_val, y_train, y_val = prepare_datasets_new([0, 1], X_soft, y_soft, session_lengths_soft)
|
|
print(X_train.shape, X_val.shape, y_train.shape, y_val.shape)
|
|
'''
|
|
|
|
|
|
# ----- Make model ------
|
|
'''
|
|
model_GRU = GRU(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
|
|
model_LSTM = LSTM(input_shape=(1, 208)) # (timestep, 13*16 MFCC coefficients)
|
|
model_CNN_1D = CNN_1D(input_shape=(208, 1)) # (timestep, 13*16 MFCC coefficients)
|
|
|
|
model_GRU.summary()
|
|
model_LSTM.summary()
|
|
model_CNN_1D.summary()
|
|
'''
|
|
|
|
# ----- Train network ------
|
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'''
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history_GRU = train(model_GRU, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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history_LSTM = train(model_LSTM, X_train, y_train, verbose=VERBOSE, batch_size=BATCH_SIZE, epochs=EPOCHS)
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history_CNN_1D = train( model_CNN_1D, np.reshape(X_train, (X_train.shape[0], 208, 1)),
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y_train, X_validation=np.reshape(X_val, (X_val.shape[0], 208, 1)), y_validation=y_val, verbose=VERBOSE,
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batch_size=BATCH_SIZE, epochs=EPOCHS)
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'''
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# ----- Plot train accuracy/error -----
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'''
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plot_train_history(history_CNN_1D, val_data=True)
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'''
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# ----- Evaluate model on test set ------
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'''
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test_loss, test_acc = model_GRU.evaluate(X_test, y_test, verbose=VERBOSE)
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print('\nTest accuracy GRU:', test_acc, '\n')
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test_loss, test_acc = model_LSTM.evaluate(X_test, y_test, verbose=VERBOSE)
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print('\nTest accuracy LSTM:', test_acc, '\n')
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test_loss, test_acc = model_CNN_1D.evaluate(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, verbose=0)
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print('\nTest accuracy CNN_1D:', test_acc, '\n')
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'''
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# ----- Store test predictions in CSV ------
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'''
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prediction_csv_logger(np.reshape(X_test, (X_test.shape[0], 208, 1)), y_test, MODEL_NAME, model_CNN_1D, TEST_SESSION_NR)
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'''
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# ----- Cross validation ------
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# Trained on three sessions, tested on one
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'''
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average_GRU = session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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average_LSTM = session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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average_FFN = session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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average_CNN = session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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print('\n')
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print('Cross-validated GRU:', average_GRU)
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print('Cross-validated LSTM:', average_LSTM)
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print('Cross-validated FFN:', average_FFN)
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print('Cross-validated CNN_1D:', average_CNN)
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print('\n')
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'''
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# ----- Inverse cross-validation ------
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# Trained on one session, tested on three
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'''
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average_GRU = inverse_session_cross_validation('GRU', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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average_LSTM = inverse_session_cross_validation('LSTM', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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average_FFN = inverse_session_cross_validation('FFN', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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average_CNN = inverse_session_cross_validation('CNN_1D', X, y, session_lengths, nr_sessions=NR_SESSIONS,
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log_to_csv=LOG,
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batch_size=BATCH_SIZE,
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epochs=EPOCHS)
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print('\n')
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print('Cross-validated one-session-train GRU:', average_GRU)
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print('Cross-validated one-session-train LSTM:', average_LSTM)
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print('Cross-validated one-session-train FFN:', average_FFN)
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print('Cross-validated one-session-train CNN_1D:', average_CNN)
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print('\n')
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'''
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# ----- PLOTTING ------
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'''
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plot_comp_spread_single(X, y, session_lengths, NR_SESSIONS, epochs=30)
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plot_comp_accuracy_single(X_soft, y_soft, session_lengths_soft, NR_SESSIONS, epochs=30)
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plot_comp_val_SoftHard(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
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plot_comp_SoftHard_3(X_soft, y_soft, X_hard, y_hard, session_lengths_soft, session_lengths_hard, NR_SESSIONS, epochs=30)
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plot_N_S_val_comp()
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'''
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