--- a
+++ b/src/graph.py
@@ -0,0 +1,113 @@
+from __future__ import division, print_function
+from keras.models import Model
+from keras.layers import Input, Conv1D, Dense, add, Flatten, Dropout,MaxPooling1D, Activation, BatchNormalization, Lambda
+from keras import backend as K
+from keras.optimizers import Adam
+from keras.saving import register_keras_serializable
+import tensorflow as tf
+
+@register_keras_serializable(package="custom")
+def zeropad(x):
+    """ 
+    zeropad and zeropad_output_shapes are from 
+    https://github.com/awni/ecg/blob/master/ecg/network.py
+    """
+    y = tf.zeros_like(x)
+    return tf.concat([x, y], axis=2)
+
+@register_keras_serializable(package="custom")
+def zeropad_output_shape(input_shape):
+    shape = list(input_shape)
+    assert len(shape) == 3
+    shape[2] *= 2
+    return tuple(shape)
+
+
+def ECG_model(config):
+    """ 
+    implementation of the model in https://www.nature.com/articles/s41591-018-0268-3 
+    also have reference to codes at 
+    https://github.com/awni/ecg/blob/master/ecg/network.py 
+    and 
+    https://github.com/fernandoandreotti/cinc-challenge2017/blob/master/deeplearn-approach/train_model.py
+    """
+    def first_conv_block(inputs, config):
+        layer = Conv1D(filters=config.filter_length,
+               kernel_size=config.kernel_size,
+               padding='same',
+               strides=1,
+               kernel_initializer='he_normal')(inputs)
+        layer = BatchNormalization()(layer)
+        layer = Activation('relu')(layer)
+
+        shortcut = MaxPooling1D(pool_size=1,
+                      strides=1)(layer)
+
+        layer =  Conv1D(filters=config.filter_length,
+               kernel_size=config.kernel_size,
+               padding='same',
+               strides=1,
+               kernel_initializer='he_normal')(layer)
+        layer = BatchNormalization()(layer)
+        layer = Activation('relu')(layer)
+        layer = Dropout(config.drop_rate)(layer)
+        layer =  Conv1D(filters=config.filter_length,
+                        kernel_size=config.kernel_size,
+                        padding='same',
+                        strides=1,
+                        kernel_initializer='he_normal')(layer)
+        return add([shortcut, layer])
+
+    def main_loop_blocks(layer, config):
+        filter_length = config.filter_length
+        n_blocks = 15
+        for block_index in range(n_blocks):
+
+            subsample_length = 2 if block_index % 2 == 0 else 1
+            shortcut = MaxPooling1D(pool_size=subsample_length)(layer)
+
+            # 5 is chosen instead of 4 from the original model
+            if block_index % 4 == 0 and block_index > 0 :
+                # double size of the network and match the shapes of both branches
+                shortcut = Lambda(zeropad, output_shape=zeropad_output_shape)(shortcut)
+                filter_length *= 2
+
+            layer = BatchNormalization()(layer)
+            layer = Activation('relu')(layer)
+            layer =  Conv1D(filters= filter_length,
+                            kernel_size=config.kernel_size,
+                            padding='same',
+                            strides=subsample_length,
+                            kernel_initializer='he_normal')(layer)
+            layer = BatchNormalization()(layer)
+            layer = Activation('relu')(layer)
+            layer = Dropout(config.drop_rate)(layer)
+            layer =  Conv1D(filters= filter_length,
+                            kernel_size=config.kernel_size,
+                            padding='same',
+                            strides= 1,
+                            kernel_initializer='he_normal')(layer)
+            layer = add([shortcut, layer])
+        return layer
+
+    def output_block(layer, config):
+        layer = BatchNormalization()(layer)
+        layer = Activation('relu')(layer)
+        layer = Flatten()(layer)
+        outputs = Dense(len_classes, activation='softmax')(layer)
+        model = Model(inputs=inputs, outputs=outputs)
+        
+        adam = Adam(learning_rate=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-7, amsgrad=False)
+        model.compile(optimizer= adam,
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+        model.summary()
+        return model
+
+    classes = ['N','V','/','A','F','~']#,'L','R',f','j','E','a']#,'J','Q','e','S'] are too few or not in the trainset, so excluded out
+    len_classes = len(classes)
+
+    inputs = Input(shape=(config.input_size, 1), name='input')
+    layer = first_conv_block(inputs, config)
+    layer = main_loop_blocks(layer, config)
+    return output_block(layer, config)