--- a
+++ b/deeplearn-approach/predict.py
@@ -0,0 +1,108 @@
+'''
+This function loads one random recording from CinC Challenge and use pre-trained model in predicting what it is using Residual Networks
+
+For more information visit: https://github.com/fernandoandreotti/cinc-challenge2017
+ 
+ Referencing this work
+   Andreotti, F., Carr, O., Pimentel, M.A.F., Mahdi, A., & De Vos, M. (2017). Comparing Feature Based 
+   Classifiers and Convolutional Neural Networks to Detect Arrhythmia from Short Segments of ECG. In 
+   Computing in Cardiology. Rennes (France).
+
+--
+ cinc-challenge2017, version 1.0, Sept 2017
+ Last updated : 27-09-2017
+ Released under the GNU General Public License
+
+ Copyright (C) 2017  Fernando Andreotti, Oliver Carr, Marco A.F. Pimentel, Adam Mahdi, Maarten De Vos
+ University of Oxford, Department of Engineering Science, Institute of Biomedical Engineering
+ fernando.andreotti@eng.ox.ac.uk
+   
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+ 
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+ GNU General Public License for more details.
+ 
+ You should have received a copy of the GNU General Public License
+ along with this program.  If not, see <http://www.gnu.org/licenses/>.
+'''
+
+
+# Download some random waveform from challenge database
+from random import randint
+import urllib.request
+record = "A{:05d}".format(randint(0, 999))
+urlfile = "https://www.physionet.org/physiobank/database/challenge/2017/training/A00/{}.mat".format(record)
+local_filename, headers = urllib.request.urlretrieve(urlfile)
+html = open(local_filename)
+print('Downloading record {} ..'.format(record))
+   
+# Load data
+import scipy.io
+mat_data = scipy.io.loadmat(local_filename)
+data = mat_data['val']
+
+# Parameters
+FS = 300
+maxlen = 30*FS
+classes = ['A', 'N', 'O','~']
+
+# Preprocessing data
+print("Preprocessing recording ..")    
+import numpy as np
+X = np.zeros((1,maxlen))
+data = np.nan_to_num(data) # removing NaNs and Infs
+data = data[0,0:maxlen]
+data = data - np.mean(data)
+data = data/np.std(data)
+X[0,:len(data)] = data.T # padding sequence
+data = X
+data = np.expand_dims(data, axis=2) # required by Keras
+del X
+
+
+# Load and apply model
+print("Loading model")    
+from keras.models import load_model
+model = load_model('ResNet_30s_34lay_16conv.hdf5')
+
+print("Applying model ..")    
+prob = model.predict(data)
+ann = np.argmax(prob)
+print("Record {} classified as {} with {:3.1f}% certainty".format(record,classes[ann],100*prob[0,ann]))
+
+# Visualising output of first 16 convolutions for some layers
+from keras import backend as K
+import matplotlib.pyplot as plt
+plt.plot(data[0,0:1000,0],)
+plt.title('Input signal')
+#plt.savefig('layinput.eps', format='eps', dpi=1000) # saving?
+
+for l in range(1,34):#range(1,34):
+    Np = 1000
+    ## Example of plotting first layer output
+    layer_name = 'conv1d_{}'.format(l)
+    layer_dict = dict([(layer.name, layer) for layer in model.layers])
+    layer_output = layer_dict[layer_name].output
+    
+    # K.learning_phase() is a flag that indicates if the network is in training or
+    # predict phase. It allow layer (e.g. Dropout) to only be applied during training
+    get_layer_output = K.function([model.layers[0].input, K.learning_phase()],
+                                   [layer_output])
+    filtout = get_layer_output([data,0])[0]
+    Npnew = int(Np*filtout.shape[1]/data.shape[1])
+    fig, ax = plt.subplots(nrows=4, ncols=4, sharex='col', sharey='row')    
+    count = 0
+    for row in ax:
+        for col in row:
+            col.plot(range(Npnew), filtout[0,0:Npnew,count],linewidth=1.0,color='olive')
+            count += 1
+    plt.suptitle('Layer {}'.format(l))
+    #plt.savefig('layoutput{}.eps'.format(l), format='eps', dpi=1000) # saving?
+            
+
+