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cv_test.py
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cv_test.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import CV
import CNN
from sklearn.model_selection import train_test_split
from keras.models import Sequential
from keras.layers import (Dense, Dropout, Conv1D, GlobalAveragePooling1D,
MaxPooling1D, Flatten, Conv2D, Activation,
MaxPooling2D, BatchNormalization, LSTM)
from keras import callbacks
import datetime
import os
from keras.models import Model
from keras.models import load_model
from keras import layers
import keras
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from keras.utils import multi_gpu_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
k = 3
epochs = 80
verbose = 0
# 0 | 1 | 2| 3| 4| 5| 6| 7 | 8 | 9 | 10|11|12|13|14|15| 16| 17| 18| 19| 20| 21 |22|23|24|25|26| 27|28|29|30| 31 |
# Fp1|Fp2|F7|F3|Fz|F4|F8|FC5|FC1|FC2|FC6|T7|C3|Cz|C4|T8|TP9|CP5|CP1|CP2|CP6|TP10|P7|P3|Pz|P4|P8|PO9|O1|Oz|O2|PO10|
def reshape_1D_conv(X):
X_rashaped = np.array([X[i, :, :].flatten()
for i in range(X.shape[0])]) # concatenate
X_rashaped = X_rashaped.reshape(X_rashaped.shape[0],
X_rashaped.shape[1],
1) # add 3rd dimension
return X_rashaped
def reshape_2D_conv(X):
X_reshaped = X.reshape(X.shape[0], X.shape[1], X.shape[2], 1)
return X_reshaped
# Read data
y = np.load('y.npy')
# ################ TRAIN MODELS #########################
# ConvLSTM2D
# X = np.load('EEG_2D_LSTM.npy')
# X = X.reshape(X.shape[0], X.shape[1], X.shape[2], X.shape[3], 1)
# CNN.ConvLSTM(X, y, epochs=epochs, name='ConvLSTM', num_GPU=num_GPU,
# optimizer='adam', batch_size=64, loss='categorical_crossentropy',
# metrics=['accuracy'], test_split_size=0.1, verbose=1)
print("LSTM")
LSTM
X = np.abs(np.load('stft-1D-100.npy'))
X = np.transpose(X,(0,2,1))
print(X.shape)
# CV.LSTMNN(X, y, epochs=20, name='lstm-stft', folds=3, test_size=0.1, verbose=1,
# batch_size=32, optimizer='adam', loss='categorical_crossentropy',
# metrics=['accuracy'], shuffle=True)
CNN.LSTMNN(X, y, epochs=20, name='LSTM-stft', test_split_size=0.2, verbose=1,
num_GPU=4, batch_size=32, optimizer='adam',
loss='categorical_crossentropy', metrics=['accuracy'],
shuffle=True)
exit()
X = np.load('EEG.npy')
###3
X = np.load('./person/P1X.npy')
y = np.load('./person/P1y.npy')
####
X = reshape_1D_conv(X)
print(X.shape)
print("start")
CV.CNN1D(X, y, epochs=50, verbose=2, name="TimeAllCH", folds=5, batch_size = 20)
'''
# channels all
X = np.load('EEG.npy')
X = reshape_1D_conv(X)
print("start")
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="TimeAllCH", folds=k, optimizer='rmsprop')
# channels 7:21
X = np.load('EEG.npy')
X = reshape_1D_conv(X[:, :, 7:21])
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="Time7:21CH", folds=k)
# Short Time Fourier 1D
X = np.abs(np.load('stft-1D-50.npy'))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="STFT-50 1D", folds=k)
X = np.abs(np.load('stft-1D-100.npy'))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="STFT-100 1D", folds=k)
X = np.abs(np.load('stft-1D-150.npy'))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="STFT-150 1D", folds=k)
# Short Time Fourier 1D with log
X = np.abs(np.load('stft-1D-50.npy'))
X = np.log10(X.clip(min=000000.1))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="STFT-50 log 1D", folds=k)
X = np.abs(np.load('stft-1D-100.npy'))
X = np.log10(X.clip(min=000000.1))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="STFT-100 log 1D", folds=k)
X = np.abs(np.load('stft-1D-150.npy'))
X = np.log10(X.clip(min=000000.1))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="STFT-150 log 1D", folds=k)
# Short Time Fourier 2D
print("Short Time Fourier 2D")
X = np.abs(np.load('stft-2D-50.npy'))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="STFT-50 log 2D", folds=k)
X = np.abs(np.load('stft-2D-100.npy'))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="STFT-100 log 2D", folds=k)
X = np.abs(np.load('stft-2D-100.npy'))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="STFT-150 log 2D", folds=k)
# STFT 2D with Log Transformation
X = np.abs(np.load('stft-2D-50.npy'))
X = np.log10(X.clip(min=000000.1))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="STFT-50 log 2D", folds=k)
X = np.abs(np.load('stft-2D-100.npy'))
X = np.log10(X.clip(min=000000.1))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="STFT-100 log 2D", folds=k)
X = np.abs(np.load('stft-2D-150.npy'))
X = np.log10(X.clip(min=000000.1))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="STFT-150 log 2D", folds=k)
# Multitaper 1D
print("Multitaper 1D")
X = np.abs(np.load('mt-1D-50.npy'))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="MT 50 1D", folds=k)
X = np.abs(np.load('mt-1D-100.npy'))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="MT 100 1D", folds=k)
X = np.abs(np.load('mt-1D-150.npy'))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="MT 150 1D", folds=k)
# MultiTaper 1D with log
X = np.abs(np.load('mt-1D-50.npy'))
X = np.log10(X.clip(min=000000.1))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="MT 50 log 1D", folds=k)
X = np.abs(np.load('mt-1D-100.npy'))
X = np.log10(X.clip(min=000000.1))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="MT 100 log 1D", folds=k)
X = np.abs(np.load('mt-1D-150.npy'))
X = np.log10(X.clip(min=000000.1))
X = reshape_1D_conv(X)
CV.CNN1D(X, y, epochs=epochs, verbose=verbose, name="MT 150 log 1D", folds=k)
# Multitaper 2D
X = np.abs(np.load('mt-2D-50.npy'))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="MT 50 2D", folds=k)
X = np.abs(np.load('mt-2D-100.npy'))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="MT 100 2D", folds=k)
X = np.abs(np.load('mt-2D-150.npy'))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="MT 150 2D", folds=k)
# Multitaper 2D with Log
X = np.abs(np.load('stft-2D-50.npy'))
X = np.log10(X.clip(min=000000.1))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="MT 50 log 2D", folds=k)
X = np.abs(np.load('stft-2D-100.npy'))
X = np.log10(X.clip(min=000000.1))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="MT 100 log 2D", folds=k)
X = np.abs(np.load('stft-2D-150.npy'))
X = np.log10(X.clip(min=000000.1))
CV.CNN2D(X, y, epochs=epochs, verbose=verbose, name="MT 150 log 2D", folds=k)
'''