import numpy as np
import pandas as pd
import math
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Conv1D, Conv2D, MaxPooling2D, Flatten
from keras.callbacks import ModelCheckpoint
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import scipy.io as sio
from os import listdir
from os.path import isfile, join
import keras
from sklearn.metrics import accuracy_score
from keras import backend as K
import sys
K.set_image_data_format('channels_last') #For problems with ordering
number_of_classes = 4
def change(x):
return np.argmax(x, axis=1)
if sys.argv[1] == 'cinc':
#Loading of .mat files from training directory. Only 9000 time steps from every ECG file is loaded
mypath = 'training2017/'
onlyfiles = [f for f in listdir(mypath) if (isfile(join(mypath, f)) and f[0] == 'A')]
bats = [f for f in onlyfiles if f[7] == 'm']
mats = [f for f in bats if (np.shape(sio.loadmat(mypath + f)['val'])[1] >= 9000)] #Choic of only 9k time steps
if not mats:
raise ValueError("No valid .mat files found with at least 9000 time steps.")
check = np.shape(sio.loadmat(mypath + mats[0])['val'])[1]
X = np.zeros((len(mats), check))
for i, mat in enumerate(mats):
X[i, :] = sio.loadmat(join(mypath, mat))['val'][0, :9000]
#Transformation from literals (Noisy, Arithm, Other, Normal)
target_train = np.zeros((len(mats), 1))
Train_data = pd.read_csv(mypath + 'REFERENCE.csv', sep=',', header=None, names=None)
for i in range(len(mats)):
if Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'N':
target_train[i] = 0
elif Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'A':
target_train[i] = 1
elif Train_data.loc[Train_data[0] == mats[i][:6], 1].values == 'O':
target_train[i] = 2
else:
target_train[i] = 3
'''Label_set = np.zeros((len(mats), number_of_classes))
for i in range(np.shape(target_train)[0]):
dummy = np.zeros((number_of_classes))
dummy[int(target_train[i])] = 1
Label_set[i, :] = dummy'''
Label_set = np.eye(number_of_classes)[target_train.astype(int)]
elif sys.argv[1] == 'mit':
print('In proces...')
sys.exit()
#X = np.abs(numpy.fft.fft(X)) #some stuff
# Normalization part
#scaler = MinMaxScaler(feature_range=(0, 1))
#X = scaler.fit_transform(X)
train_len = 0.8 #Choice of training size
X_train = X[:int(train_len*len(mats)), :]
Y_train = Label_set[:int(train_len*len(mats)), :]
X_val = X[int(train_len*len(mats)):, :]
Y_val = Label_set[int(train_len*len(mats)):, :]
# reshape input to be [samples, tensor shape (30 x 300)]
n = 20
m = 450
c = 1 #number of channels
X_train = numpy.reshape(X_train, (X_train.shape[0], n, m, c))
X_val = numpy.reshape(X_val, (X_val.shape[0], n, m, c))
image_size = (n, m, c)
# create and fit the CNN network
batch_size = 32
model = Sequential()
#model.load_weights('my_model_weights.h5')
#64 conv
model.add(Conv2D(64, (3, 3), activation='relu', input_shape=image_size, padding='same'))
model.add(Conv2D(64, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
#128 conv
model.add(Conv2D(128, (3, 3), activation='relu', padding='same' ))
model.add(Conv2D(128, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# #256 conv
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(Conv2D(256, (3, 3), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# #512 conv
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(Conv2D(512, (3, 3), activation='relu'))
# model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
#Dense part
model.add(Flatten())
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(4096, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1000, activation='relu'))
model.add(Dense(number_of_classes, activation='softmax'))
#Callbacks and accuracy calculation
#early_stopping = keras.callbacks.EarlyStopping(monitor='val_acc', min_delta=0, patience=50, verbose=1, mode='auto')
model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath="Keras_models/weights.{epoch:02d}-{val_acc:.2f}.hdf5", monitor='val_loss', save_weights_only=True, period=1, verbose=1, save_best_only=False)
model.fit(X_train, Y_train, epochs=250, batch_size=batch_size, validation_data=(X_val, Y_val), verbose=2, shuffle=False, callbacks=[checkpointer])
model.save('Keras_models/my_model_' + str(i) + '_' + str(j) + '_' + str() + '.h5')
predictions = model.predict(X_val)
score = accuracy_score(change(Y_val), change(predictions))
print(score)
# Data[i - starti, j - starti] = str(format(score, '.5f'))
# Output = pd.DataFrame(Data)
# name = str(batch_size) + '.csv'
# Output.to_csv(path_or_buf='Keras_models/' + name, index=None, header=None)
Datasets
Models
