--- a
+++ b/CNN_ECG.py
@@ -0,0 +1,139 @@
+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)