--- a
+++ b/utils/nn.py
@@ -0,0 +1,64 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Aug 13 22:19:12 2015
+
+@author: rc, alex
+"""
+
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation, Flatten
+from keras.layers.convolutional import Convolution2D
+from keras.layers.recurrent import GRU, LSTM
+from keras.layers.normalization import BatchNormalization
+from keras.layers.advanced_activations import PReLU
+from keras.optimizers import SGD, Adam
+from keras.regularizers import l2
+
+
+def buildNN(architecture,training_params,input_dim):
+    """Lay out a Neural Network as described in the YAML file."""
+    current_units = input_dim
+    
+    model = Sequential()
+    
+    for layer in architecture:
+        layer_name = layer.keys()[0]
+        layer_params = layer[layer_name]
+        if layer_name == 'Activation':
+            model.add(Activation(layer_params['type']))
+        if layer_name == 'PReLU':
+            model.add(PReLU(current_units))
+        if layer_name == 'Dropout':
+            model.add(Dropout(layer_params['p']))
+        if layer_name == 'Dense':
+            model.add(Dense(current_units, layer_params['num_units'], init='glorot_uniform'))
+            current_units = layer_params['num_units']
+        if layer_name == 'Conv':
+            # a filter covers all predictions for a single time sample
+            # strides are made between time samples
+            model.add(Convolution2D(layer_params['nb_filters'], 1, current_units, 1,
+                                    subsample=(current_units,1),
+                                    init='glorot_uniform'))
+            current_units = layer_params['nb_filters']*training_params['num_strides']
+        if layer_name == 'GRU':
+            model.add(GRU(current_units,layer_params['num_units'], return_sequences=layer_params['next_GRU']))
+            current_units = layer_params['num_units']
+        if layer_name == 'LSTM':
+            model.add(LSTM(current_units,layer_params['num_units'], return_sequences=layer_params['next_GRU']))
+            current_units = layer_params['num_units']
+        if layer_name == 'BatchNormalization':
+            model.add(BatchNormalization((current_units,)))
+        if layer_name == 'Flatten':
+            model.add(Flatten())
+        if layer_name == 'Output':
+            model.add(Dense(current_units, 6, init='glorot_uniform'))
+            model.add(Activation('sigmoid'))
+    if not training_params.has_key('optim') or training_params['optim'] == 'sgd':
+        optim = SGD(lr=training_params['lr'], decay=float(training_params['decay']), momentum=training_params['momentum'], nesterov=True)    
+    elif training_params['optim'] == 'adam':
+        optim = Adam(lr=training_params['lr'])
+    
+    model.compile(loss='binary_crossentropy', optimizer=optim)
+    
+    return model
+    
\ No newline at end of file