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+### Common Settings
+
+The model is trained on 2-layer feedforward controller (with hidden sizes 128 and 256 respectively) with the following set of hyperparameters:
+
+- RMSProp Optimizer with learning rate of 10⁻⁴, momentum of 0.9.
+- Memory word size of 10, with a single read head.
+- A batch size of 1.
+- input_size = 3.
+- output_size = 1.
+- sequence_max_length = 100.
+- words_count = 15.
+- word_size = 10.
+- read_heads = 1.
+
+A square loss function of the form: (y - y_)**2 is used. Where both 'y' and 'y_' are scalar numbers.
+ 
+The input is a (1, random_length, 3) tensor, where the 3 is for a one-hot encoding vector of size 3, where:
+
+010 is a '0'
+100 is a '1'
+001 is the end mark
+
+So, and example of an input of length 10 will be the next 3D-tensor:
+
+  [[[ 0.  1.  0.]
+  [ 0.  1.  0.]
+  [ 0.  1.  0.]
+  [ 1.  0.  0.]
+  [ 0.  1.  0.]
+  [ 0.  1.  0.]
+  [ 1.  0.  0.]
+  [ 0.  1.  0.]
+  [ 0.  1.  0.]
+  [ 0.  0.  1.]]]
+
+This input is a represenation of a sequence of adding 0 or 1 values in the form of:
+
+0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + (end_mark)
+
+The target outoput is a 3D-tensor with the result of this adding task. In the example above:
+
+[[[2.0]]]
+
+The DNC output is a 3D-tensor of shape (1, random_length, 1). For example:
+
+  [[[ 0.45]
+  [ -0.11]
+  [ 1.3]
+  [ 5.0]
+  [ 0.5]
+  [ 0.1]
+  [ 1.0]
+  [ -0.5]
+  [ 0.33]
+  [ 0.12]]]
+
+The target output and the DNC output are both then reduced with tf.reduce_sum() so we end up with two scalar values. For example:
+
+Target_output: 2.0
+DNC_output:    5.89
+
+And we apply then the square loss function:
+
+loss = (Target_o - DNC_o)**2 
+
+and finally the gradient update. 
+
+### Results
+
+The model is going to recieve as input a random length sequence of 0 or 1 values like:
+
+Input: 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1
+
+Then it will return a scalar value for this input adding proccess. For example, the DNC will output something like: 3.98824.
+This value will be the predicted result for the input adding sequence (we are going to truncate the integer part of the result):
+
+DNC prediction:   1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1  = 3 [3.98824]
+
+Once we train the model with:
+
+```
+$python tasks/copy/train.py --iterations=50000
+```
+
+we can see that the model learns in less than 1000 iterations to compute this adding function, and the loss drop from:
+
+Iteration 0/1000
+Avg. Logistic Loss: 24.9968
+
+to:
+
+Iteration 1000/1000
+Avg. Logistic Loss: 0.0076
+
+It seems like the DNC model is able to learn this pseudo-code:
+
+function(x):
+   if (x == [ 1.  0.  0.])
+       return (near) 1.0 (float values)
+   else
+       return (near) 0.0 (float values)
+
+
+### Generalization test
+
+We use for the model a sequence_max_length = 100, but in the training proccess we use just random length sequences up to 10 (sequence_max_length/10). Once the train is finished, we let the trained model to generalize to random length sequences up to 100 (sequence_max_length).
+
+Results show that the model successfully generalize the adding task even with sequence 10 times larger than the training ones.
+
+These are real data outputs:
+
+Building Computational Graph ... Done!
+Initializing Variables ... Done!
+
+Iteration 0/1000
+Avg. Logistic Loss: 24.9968
+Real value:  0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1  = 5
+Predicted:   0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1  = 0 [0.000319847]
+
+Iteration 100/1000
+Avg. Logistic Loss: 5.8042
+Real value:  0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1  = 5
+Predicted:   0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1  = 6 [6.1732]
+
+Iteration 200/1000
+Avg. Logistic Loss: 0.7492
+Real value:  1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1  = 9
+Predicted:   1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1  = 8 [8.91952]
+
+Iteration 300/1000
+Avg. Logistic Loss: 0.0253
+Real value:  0 + 1 + 1  = 2
+Predicted:   0 + 1 + 1  = 2 [2.0231]
+
+Iteration 400/1000
+Avg. Logistic Loss: 0.0089
+Real value:  0 + 1 + 0 + 0 + 0 + 1 + 1  = 3
+Predicted:   0 + 1 + 0 + 0 + 0 + 1 + 1  = 2 [2.83419]
+
+Iteration 500/1000
+Avg. Logistic Loss: 0.0444
+Real value:  1 + 0 + 1 + 1  = 3
+Predicted:   1 + 0 + 1 + 1  = 2 [2.95937]
+
+Iteration 600/1000
+Avg. Logistic Loss: 0.0093
+Real value:  1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1  = 4
+Predicted:   1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1  = 3 [3.98824]
+
+Iteration 700/1000
+Avg. Logistic Loss: 0.0224
+Real value:  0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0  = 6
+Predicted:   0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0  = 5 [5.93554]
+
+Iteration 800/1000
+Avg. Logistic Loss: 0.0115
+Real value:  0 + 0  = 0
+Predicted:   0 + 0  = -1 [-0.0118587]
+
+Iteration 900/1000
+Avg. Logistic Loss: 0.0023
+Real value:  1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0  = 5
+Predicted:   1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0  = 4 [4.97147]
+
+Iteration 1000/1000
+Avg. Logistic Loss: 0.0076
+Real value:  1 + 0 + 0 + 1 + 1 + 0 + 0 + 1  = 4Done!
+
+Testing generalization...
+
+Iteration 0/1000
+Predicted:   1 + 0 + 0 + 1 + 1 + 0 + 0 + 1  = 4 [4.123]
+
+Saving Checkpoint ...
+Real value:  1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0  = 6
+Predicted:   1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0  = 6 [6.24339]
+
+Iteration 1/1000
+Real value:  1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1  = 11
+Predicted:   1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1  = 11 [11.1931]
+
+Iteration 2/1000
+Real value:  0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1  = 33
+Predicted:   0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1  = 32 [32.9866]
+
+Iteration 3/1000
+Real value:  1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1  = 16
+Predicted:   1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1  = 16 [16.1541]
+
+Iteration 4/1000
+Real value:  1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0  = 44
+Predicted:   1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0  = 43 [43.5211]
+
+Iteration 5/1000
+Real value:  1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0  = 33
+Predicted:   1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0  = 33 [33.081]
+
+Iteration 6/1000
+Real value:  1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0  = 12
+Predicted:   1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0  = 12 [12.2167]
+
+Iteration 7/1000
+Real value:  1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1  = 10
+Predicted:   1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1  = 10 [10.1519]
+
+Iteration 8/1000
+Real value:  0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1  = 33
+Predicted:   0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1  = 33 [33.1624]
+
+Iteration 9/1000
+Real value:  0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0  = 17
+Predicted:   0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0  = 17 [17.2243]
+
+Iteration 10/1000
+Real value:  1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1  = 25
+Predicted:   1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1  = 25 [25.0643]
+
+Iteration 11/1000
+Real value:  0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1  = 42
+Predicted:   0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1  = 41 [41.6496]
+
+Iteration 12/1000
+Real value:  1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1  = 42
+Predicted:   1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1  = 41 [41.4914]
+
+Iteration 13/1000
+Real value:  0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1  = 19
+Predicted:   0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1  = 19 [19.2566]
+
+Iteration 14/1000
+Real value:  0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0  = 25
+Predicted:   0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0  = 25 [25.0976]
+
+Iteration 15/1000
+Real value:  1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0  = 48
+Predicted:   1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0  = 47 [47.1815]
+
+Iteration 16/1000
+Real value:  1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1  = 8
+Predicted:   1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1  = 8 [8.11703]
+
+Iteration 17/1000
+Real value:  0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0  = 45
+Predicted:   0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0  = 44 [44.3639]
+
+Iteration 18/1000
+Real value:  1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1  = 15
+Predicted:   1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1  = 15 [15.2815]
+
+Iteration 19/1000
+Real value:  1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1  = 40
+Predicted:   1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1  = 39 [39.928]
+
+Iteration 20/1000
+Real value:  1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0  = 19
+Predicted:   1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0  = 19 [19.2498]
+
+Iteration 21/1000
+Real value:  0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1  = 38
+Predicted:   0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1  = 37 [37.7267]
+
+Iteration 22/1000
+Real value:  0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0  = 21
+Predicted:   0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0  = 21 [21.2265]
+
+Iteration 23/1000
+Real value:  1 + 1  = 2
+Predicted:   1 + 1  = 2 [2.01672]
+
+Iteration 24/1000
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+Real value:  1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1  = 48
+Predicted:   1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1  = 47 [47.3965]
+
+Iteration 969/1000
+Real value:  0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0  = 9
+Predicted:   0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0  = 9 [9.24582]
+
+Iteration 970/1000
+Real value:  1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1  = 46
+Predicted:   1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1  = 45 [45.223]
+
+Iteration 971/1000
+Real value:  1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0  = 23
+Predicted:   1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0  = 22 [22.9858]
+
+Iteration 972/1000
+Real value:  0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0  = 36
+Predicted:   0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0  = 35 [35.8836]
+
+Iteration 973/1000
+Real value:  0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1  = 19
+Predicted:   0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1  = 19 [19.4778]
+
+Iteration 974/1000
+Real value:  0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0  = 23
+Predicted:   0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0  = 23 [23.1942]
+
+Iteration 975/1000
+Real value:  1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1  = 10
+Predicted:   1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1  = 10 [10.0486]
+
+Iteration 976/1000
+Real value:  1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0  = 21
+Predicted:   1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0  = 21 [21.1451]
+
+Iteration 977/1000
+Real value:  0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0  = 27
+Predicted:   0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0  = 27 [27.0197]
+
+Iteration 978/1000
+Real value:  0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1  = 29
+Predicted:   0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1  = 28 [28.9907]
+
+Iteration 979/1000
+Real value:  1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1  = 59
+Predicted:   1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1  = 57 [57.7236]
+
+Iteration 980/1000
+Real value:  1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1  = 46
+Predicted:   1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1  = 45 [45.3227]
+
+Iteration 981/1000
+Real value:  1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0  = 22
+Predicted:   1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0  = 21 [21.9706]
+
+Iteration 982/1000
+Real value:  0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1  = 45
+Predicted:   0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1  = 44 [44.3632]
+
+Iteration 983/1000
+Real value:  0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0  = 31
+Predicted:   0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0  = 31 [31.0851]
+
+Iteration 984/1000
+Real value:  1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0  = 4
+Predicted:   1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0  = 4 [4.10352]
+
+Iteration 985/1000
+Real value:  0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1  = 9
+Predicted:   0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1  = 9 [9.1832]
+
+Iteration 986/1000
+Real value:  0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1  = 7
+Predicted:   0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1  = 7 [7.10321]
+
+Iteration 987/1000
+Real value:  1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0  = 45
+Predicted:   1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0  = 44 [44.2793]
+
+Iteration 988/1000
+Real value:  0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0  = 45
+Predicted:   0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0  = 44 [44.552]
+
+Iteration 989/1000
+Real value:  1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1  = 10
+Predicted:   1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1  = 10 [10.0709]
+
+Iteration 990/1000
+Real value:  0 + 0 + 0 + 1 + 1 + 1 + 1  = 4
+Predicted:   0 + 0 + 0 + 1 + 1 + 1 + 1  = 4 [4.07279]
+
+Iteration 991/1000
+Real value:  0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0  = 18
+Predicted:   0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 0  = 18 [18.1536]
+
+Iteration 992/1000
+Real value:  1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0  = 13
+Predicted:   1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 1 + 1 + 0  = 13 [13.0738]
+
+Iteration 993/1000
+Real value:  0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0  = 7
+Predicted:   0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 1 + 0  = 7 [7.21095]
+
+Iteration 994/1000
+Real value:  1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1  = 51
+Predicted:   1 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 1 + 1  = 50 [50.052]
+
+Iteration 995/1000
+Real value:  0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0  = 8
+Predicted:   0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1 + 1 + 1 + 1 + 1 + 0  = 8 [8.13173]
+
+Iteration 996/1000
+Real value:  1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1  = 40
+Predicted:   1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 0 + 0 + 1 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 1 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 1 + 0 + 0 + 1 + 1  = 39 [39.8887]
+
+Iteration 997/1000
+Real value:  0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1  = 11
+Predicted:   0 + 0 + 0 + 0 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 0 + 1  = 11 [11.3195]
+
+Iteration 998/1000
+Real value:  0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1  = 16
+Predicted:   0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 1 + 1 + 1 + 0 + 1 + 0 + 0 + 1 + 0 + 0 + 1 + 0 + 1 + 1 + 1 + 0 + 0 + 0 + 1 + 0 + 1 + 0 + 0 + 1  = 16 [16.1729]
+
+Iteration 999/1000
+Real value:  0 + 0 + 0 + 1  = 1
+Predicted:   0 + 0 + 0 + 1  = 1 [1.06111]
+
+Iteration 1000/1000
+Real value:  0 + 0 + 1 + 0 + 0 + 0  = 1
+Predicted:   0 + 0 + 1 + 0 + 0 + 0  = 1 [1.09465]
diff --git a/tasks/adding/feedforward_controller.py b/tasks/adding/feedforward_controller.py
new file mode 100644
index 00000000..ce5fd9e0
--- /dev/null
+++ b/tasks/adding/feedforward_controller.py
@@ -0,0 +1,28 @@
+import numpy as np
+import tensorflow as tf
+from dnc.controller import BaseController
+
+
+"""
+A 2-Layers feedforward neural network with 128, 256 nodes respectively
+"""
+
+class FeedforwardController(BaseController):
+
+    def network_vars(self):
+        initial_std = lambda in_nodes: np.min(1e-2, np.sqrt(2.0 / in_nodes))
+        input_ = self.nn_input_size
+
+        self.W1 = tf.Variable(tf.truncated_normal([input_, 128], stddev=initial_std(input_)), name='layer1_W')
+        self.W2 = tf.Variable(tf.truncated_normal([128, 256], stddev=initial_std(128)), name='layer2_W')
+        self.b1 = tf.Variable(tf.zeros([128]), name='layer1_b')
+        self.b2 = tf.Variable(tf.zeros([256]), name='layer2_b')
+
+
+    def network_op(self, X):
+        l1_output = tf.matmul(X, self.W1) + self.b1
+        l1_activation = tf.nn.relu(l1_output)
+
+        l2_output = tf.matmul(l1_activation, self.W2) + self.b2
+
+        return l2_output
diff --git a/tasks/adding/train.py b/tasks/adding/train.py
new file mode 100644
index 00000000..8e97161d
--- /dev/null
+++ b/tasks/adding/train.py
@@ -0,0 +1,181 @@
+import warnings
+warnings.filterwarnings('ignore')
+
+import tensorflow as tf
+import numpy as np
+import getopt
+import sys
+import os
+
+from dnc.dnc import DNC
+from feedforward_controller import FeedforwardController
+
+
+def llprint(message):
+    sys.stdout.write(message)
+    sys.stdout.flush()
+
+
+def onehot(x, n):
+    ret = np.zeros(n).astype(np.float32)
+    ret[x] = 1.0
+    return ret
+
+
+def generate_data(length, size):
+
+    content = np.random.randint(0, size - 1, length)
+
+    seqlen = length + 1
+    x_seq_list = [float('nan')] * seqlen
+    sums = 0.0
+    sums_text = ""
+    for i in range(seqlen):
+        if (i < length):
+            x_seq_list[i] = onehot(content[i], size)
+            sums += content[i]
+            sums_text += str(content[i]) + " + "
+        else:
+            x_seq_list[i] = onehot(size - 1, size)
+
+    x_seq_list = np.array(x_seq_list)
+    x_seq_list = x_seq_list.reshape((1,) + x_seq_list.shape)
+    sums = np.array(sums)
+    sums = sums.reshape(1, 1, 1)
+
+    return x_seq_list, sums, sums_text
+
+
+def cross_entropy(prediction, target):
+    return tf.square(prediction - target)
+
+
+if __name__ == '__main__':
+
+    dirname = os.path.dirname(__file__)
+    ckpts_dir = os.path.join(dirname , 'checkpoints')
+    tb_logs_dir = os.path.join(dirname, 'logs')
+
+    batch_size = 1
+    input_size = 3
+    output_size = 1
+    sequence_max_length = 100
+    words_count = 15
+    word_size = 10
+    read_heads = 1
+
+    learning_rate = 1e-4
+    momentum = 0.9
+
+    from_checkpoint = None
+    iterations = 1000
+
+    options,_ = getopt.getopt(sys.argv[1:], '', ['checkpoint=', 'iterations='])
+
+    for opt in options:
+        if opt[0] == '--checkpoint':
+            from_checkpoint = opt[1]
+        elif opt[0] == '--iterations':
+            iterations = int(opt[1])
+
+    graph = tf.Graph()
+
+    with graph.as_default():
+        with tf.Session(graph=graph) as session:
+
+            llprint("Building Computational Graph ... ")
+
+            optimizer = tf.train.RMSPropOptimizer(learning_rate, momentum=momentum)
+            summerizer = tf.summary.FileWriter(tb_logs_dir, session.graph)
+
+            ncomputer = DNC(
+                FeedforwardController,
+                input_size,
+                output_size,
+                sequence_max_length + 1,
+                words_count,
+                word_size,
+                read_heads,
+                batch_size
+            )
+
+            # squash the DNC output to a scalar number
+            squashed_target_output = tf.reduce_sum(ncomputer.target_output)
+            output, _ = ncomputer.get_outputs()
+            squashed_output = tf.reduce_sum(output)
+
+            loss = cross_entropy(squashed_output, squashed_target_output)
+            gradients = optimizer.compute_gradients(loss)
+            apply_gradients = optimizer.apply_gradients(gradients)
+
+            summerize_loss = tf.summary.scalar("Loss", loss)
+
+            summerize_op = tf.summary.merge([summerize_loss])
+            no_summerize = tf.no_op()
+
+            llprint("Done!\n")
+
+            llprint("Initializing Variables ... ")
+            session.run(tf.global_variables_initializer())
+            llprint("Done!\n")
+
+            if from_checkpoint is not None:
+                llprint("Restoring Checkpoint %s ... " % (from_checkpoint))
+                ncomputer.restore(session, ckpts_dir, from_checkpoint)
+                llprint("Done!\n")
+
+            last_100_losses = []
+
+            for i in range(iterations + 1):
+                # We use for training just (sequence_max_length / 10) examples
+                random_length = np.random.randint(2, (sequence_max_length / 10) + 1)
+                input_data, target_output, sums_text = generate_data(random_length, input_size)
+
+                summerize = (i % 100 == 0)
+                take_checkpoint = (i != 0) and (i % iterations == 0)
+
+                target_out, output, loss_value, _, summary = session.run([
+                    squashed_target_output,
+                    squashed_output,
+                    loss,
+                    apply_gradients,
+                    summerize_op if summerize else no_summerize
+                ], feed_dict={
+                    ncomputer.input_data: input_data,
+                    ncomputer.target_output: target_output,
+                    ncomputer.sequence_length: random_length + 1
+                })
+
+                last_100_losses.append(loss_value)
+                summerizer.add_summary(summary, i)
+
+                if summerize:
+                    llprint("\rIteration %d/%d" % (i, iterations))
+                    llprint("\nAvg. Logistic Loss: %.4f\n" % (np.mean(last_100_losses)))
+                    print "Real value: ", sums_text[:-2] + ' = ' + str(int(target_output[0]))
+                    print "Predicted:  ", sums_text[:-2] + ' = ' + str(int(output // 1)) + " [" + str(output) + "]"
+                    last_100_losses = []
+
+                if take_checkpoint:
+                    llprint("\nSaving Checkpoint ... "),
+                    ncomputer.save(session, ckpts_dir, 'step-%d' % (i))
+                    llprint("Done!\n")
+
+            llprint("\nTesting generalization...\n")
+            for i in xrange(iterations + 1):
+                llprint("\nIteration %d/%d" % (i, iterations))
+                # We test now the learned generalization using sequence_max_length examples
+                random_length = np.random.randint(2, sequence_max_length + 1)
+                input_data, target_output, sums_text = generate_data(random_length, input_size)
+
+                target_out, output = session.run([
+                    squashed_target_output,
+                    squashed_output
+                ], feed_dict={
+                    ncomputer.input_data: input_data,
+                    ncomputer.target_output: target_output,
+                    ncomputer.sequence_length: random_length + 1
+                })
+
+                print "\nReal value: ", sums_text[:-2] + ' = ' + str(int(target_output[0]))
+                print "Predicted:  ", sums_text[:-2] + ' = ' + str(int(output // 1)) + " [" + str(output) + "]"
\ No newline at end of file
diff --git a/tasks/adding/train_v2.py b/tasks/adding/train_v2.py
new file mode 100644
index 00000000..50fa84f3
--- /dev/null
+++ b/tasks/adding/train_v2.py
@@ -0,0 +1,230 @@
+import warnings
+warnings.filterwarnings('ignore')
+
+import tensorflow as tf
+import numpy as np
+import getopt
+import sys
+import time
+import os
+
+from dnc.dnc import DNC
+from feedforward_controller import FeedforwardController
+
+
+def llprint(message):
+    sys.stdout.write(message)
+    sys.stdout.flush()
+
+
+def onehot(index, size):
+    vec = np.zeros(size, dtype=np.float32)
+    vec[index] = 1.0
+    return vec
+
+
+def generate_data(length, size):
+
+    content = np.random.randint(0, size - 1, length)
+
+    seqlen = length + 1
+    x_seq_list = [float('nan')] * seqlen
+    sums = 0.0
+    sums_text = ""
+    for i in range(seqlen):
+        if (i < length):
+            x_seq_list[i] = onehot(content[i], size)
+            sums += content[i]
+            sums_text += str(content[i]) + " + "
+        else:
+            x_seq_list[i] = onehot(size - 1, size)
+
+    x_seq_list = np.array(x_seq_list)
+    x_seq_list = x_seq_list.reshape((1,) + x_seq_list.shape)
+    x_seq_list = np.reshape(x_seq_list, (1, -1, size))
+
+    target_output = np.zeros((1, 1, seqlen), dtype=np.float32)
+    target_output[:, -1, -1] = sums
+    target_output = np.reshape(target_output, (1, -1, 1))
+
+    weights_vec = np.zeros((1, 1, seqlen), dtype=np.float32)
+    weights_vec[:, -1, -1] = 1.0
+    weights_vec = np.reshape(weights_vec, (1, -1, 1))
+
+    return x_seq_list, target_output, sums_text, weights_vec
+
+
+if __name__ == '__main__':
+
+    dirname = os.path.dirname(__file__)
+    ckpts_dir = os.path.join(dirname , 'checkpoints')
+    tb_logs_dir = os.path.join(dirname, 'logs')
+
+    batch_size = 1
+    input_size = 3
+    output_size = 1
+    sequence_max_length = 100
+    words_count = 15
+    word_size = 10
+    read_heads = 1
+
+    learning_rate = 1e-4
+    momentum = 0.9
+
+    from_checkpoint = None
+    iterations = 1000
+    start_step = 0
+
+    options,_ = getopt.getopt(sys.argv[1:], '', ['checkpoint=', 'iterations=', 'start='])
+
+    for opt in options:
+        if opt[0] == '--checkpoint':
+            from_checkpoint = opt[1]
+        elif opt[0] == '--iterations':
+            iterations = int(opt[1])
+        elif opt[0] == '--start':
+            start_step = int(opt[1])
+
+    graph = tf.Graph()
+    with graph.as_default():
+        with tf.Session(graph=graph) as session:
+
+            llprint("Building Computational Graph ... ")
+
+            optimizer = tf.train.RMSPropOptimizer(learning_rate, momentum=momentum)
+            summerizer = tf.train.SummaryWriter(tb_logs_dir, session.graph)
+
+            ncomputer = DNC(
+                FeedforwardController,
+                input_size,
+                output_size,
+                sequence_max_length,
+                words_count,
+                word_size,
+                read_heads,
+                batch_size
+            )
+
+            output, _ = ncomputer.get_outputs()
+
+            loss_weights = tf.placeholder(tf.float32, [batch_size, None, 1])
+            loss = tf.reduce_mean(tf.square((loss_weights * output) - ncomputer.target_output))
+
+            summeries = []
+
+            gradients = optimizer.compute_gradients(loss)
+            for i, (grad, var) in enumerate(gradients):
+                if grad is not None:
+                    gradients[i] = (tf.clip_by_value(grad, -10, 10), var)
+            for (grad, var) in gradients:
+                if grad is not None:
+                    summeries.append(tf.histogram_summary(var.name + '/grad', grad))
+
+            apply_gradients = optimizer.apply_gradients(gradients)
+
+            summeries.append(tf.scalar_summary("Loss", loss))
+
+            summerize_op = tf.merge_summary(summeries)
+            no_summerize = tf.no_op()
+
+            llprint("Done!\n")
+
+            llprint("Initializing Variables ... ")
+            session.run(tf.initialize_all_variables())
+            llprint("Done!\n")
+
+            if from_checkpoint is not None:
+                llprint("Restoring Checkpoint %s ... " % (from_checkpoint))
+                ncomputer.restore(session, ckpts_dir, from_checkpoint)
+                llprint("Done!\n")
+
+
+            last_100_losses = []
+
+            start = 0 if start_step == 0 else start_step + 1
+            end = start_step + iterations + 1
+
+            start_time_100 = time.time()
+            end_time_100 = None
+            avg_100_time = 0.
+            avg_counter = 0
+
+            for i in xrange(start, end + 1):
+                try:
+                    llprint("\rIteration %d/%d" % (i, end))
+
+                    # We use for training just (sequence_max_length / 10) examples
+                    seq_len = np.random.randint(2, (sequence_max_length / 10) + 1)
+                    input_data, target_output, sums_text, weights = generate_data(seq_len, input_size)
+
+                    summerize = (i % 100 == 0)
+                    take_checkpoint = (i != 0) and (i % end == 0)
+
+                    output_, loss_value, _, summary = session.run([
+                        output,
+                        loss,
+                        apply_gradients,
+                        summerize_op if summerize else no_summerize
+                    ], feed_dict={
+                        ncomputer.input_data: input_data,
+                        ncomputer.target_output: target_output,
+                        ncomputer.sequence_length: seq_len + 1,
+                        loss_weights: weights
+                    })
+
+                    last_100_losses.append(loss_value)
+                    summerizer.add_summary(summary, i)
+
+                    if summerize:
+                        llprint("\n\nAvg. Cross-Entropy: %.7f\n" % (np.mean(last_100_losses)))
+
+                        end_time_100 = time.time()
+                        elapsed_time = (end_time_100 - start_time_100) / 60
+                        avg_counter += 1
+                        avg_100_time += (1. / avg_counter) * (elapsed_time - avg_100_time)
+                        estimated_time = (avg_100_time * ((end - i) / 100.)) / 60.
+
+                        print "Avg. 100 iterations time: %.2f minutes" % (avg_100_time)
+                        print "Approx. time to completion: %.2f hours" % (estimated_time)
+                        print "DNC input", input_data
+                        print "Text input:     ", sums_text[:-2] + " = - "
+                        print "Target_output", target_output
+                        output_ = output_ * weights
+                        print "DNC output", output_
+                        print "Real operation:   ", sums_text[:-2] + ' = ' + str(int(target_output[-1, -1, -1]))
+                        print "Predicted result: ", sums_text[:-2] + ' = ' + str(int(round(output_[-1, -1, -1]))) + " [" + str(output_[-1, -1, -1]) + "]"
+
+                        start_time_100 = time.time()
+                        last_100_losses = []
+
+                    if take_checkpoint:
+                        llprint("\nSaving Checkpoint ... "),
+                        ncomputer.save(session, ckpts_dir, 'step-%d' % (i))
+                        llprint("Done!\n")
+                except KeyboardInterrupt:
+
+                    llprint("\nSaving Checkpoint ... "),
+                    ncomputer.save(session, ckpts_dir, 'step-%d' % (i))
+                    llprint("Done!\n")
+                    sys.exit(0)
+
+            llprint("\nTesting generalization...\n")
+            for i in xrange(50):
+                llprint("\nIteration %d/%d" % (i, iterations))
+                # We test now the learned generalization using sequence_max_length examples
+                seq_len = np.random.randint(2, sequence_max_length + 1)
+                input_data, target_output, sums_text, weights = generate_data(seq_len, input_size)
+
+                output_ = session.run([output], feed_dict={
+                        ncomputer.input_data: input_data,
+                        ncomputer.target_output: target_output,
+                        ncomputer.sequence_length: seq_len + 1,
+                        loss_weights: weights
+                })
+
+                output_ = output_ * weights
+
+                print "\nReal operation:   ", sums_text[:-2] + ' = ' + str(int(target_output[-1, -1, -1]))
+                print "Predicted result: ", sums_text[:-2] + ' = ' + str(int(round(output_[-1, -1, -1]))) + " [" + str(output_[-1, -1, -1]) + "]"
+
+            llprint("Done!\n")
diff --git a/tasks/argmax/README.md b/tasks/argmax/README.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/tasks/argmax/README.md
@@ -0,0 +1 @@
+
diff --git a/tasks/argmax/feedforward_controller.py b/tasks/argmax/feedforward_controller.py
new file mode 100644
index 00000000..ce5fd9e0
--- /dev/null
+++ b/tasks/argmax/feedforward_controller.py
@@ -0,0 +1,28 @@
+import numpy as np
+import tensorflow as tf
+from dnc.controller import BaseController
+
+
+"""
+A 2-Layers feedforward neural network with 128, 256 nodes respectively
+"""
+
+class FeedforwardController(BaseController):
+
+    def network_vars(self):
+        initial_std = lambda in_nodes: np.min(1e-2, np.sqrt(2.0 / in_nodes))
+        input_ = self.nn_input_size
+
+        self.W1 = tf.Variable(tf.truncated_normal([input_, 128], stddev=initial_std(input_)), name='layer1_W')
+        self.W2 = tf.Variable(tf.truncated_normal([128, 256], stddev=initial_std(128)), name='layer2_W')
+        self.b1 = tf.Variable(tf.zeros([128]), name='layer1_b')
+        self.b2 = tf.Variable(tf.zeros([256]), name='layer2_b')
+
+
+    def network_op(self, X):
+        l1_output = tf.matmul(X, self.W1) + self.b1
+        l1_activation = tf.nn.relu(l1_output)
+
+        l2_output = tf.matmul(l1_activation, self.W2) + self.b2
+
+        return l2_output
diff --git a/tasks/argmax/train_v2.py b/tasks/argmax/train_v2.py
new file mode 100644
index 00000000..a4380a04
--- /dev/null
+++ b/tasks/argmax/train_v2.py
@@ -0,0 +1,223 @@
+import warnings
+warnings.filterwarnings('ignore')
+
+import tensorflow as tf
+import numpy as np
+import getopt
+import sys
+import time
+import os
+
+from dnc.dnc import DNC
+from feedforward_controller import FeedforwardController
+
+
+def llprint(message):
+    sys.stdout.write(message)
+    sys.stdout.flush()
+
+
+def onehot(index, size):
+    vec = np.zeros(size, dtype=np.float32)
+    vec[index] = 1.0
+    return vec
+
+
+def generate_data(length, size):
+
+    content = np.random.randint(0, size - 1, length)
+
+    seqlen = length + 1
+    x_seq_list = [float('nan')] * seqlen
+    max_value = 0
+    max_ind = 0
+    for i in range(seqlen):
+        if (i < length):
+            x_seq_list[i] = onehot(content[i], size)
+            if (max_value < content[i]):
+                max_value = content[i]
+                max_ind = i
+        else:
+            x_seq_list[i] = onehot(size - 1, size)
+
+    x_seq_list = np.array(x_seq_list)
+    x_seq_list = x_seq_list.reshape((1,) + x_seq_list.shape)
+    x_seq_list = np.reshape(x_seq_list, (1, -1, size))
+
+    target_output = np.zeros((1, 1, seqlen), dtype=np.float32)
+    target_output[:, -1, -1] = max_ind
+    target_output = np.reshape(target_output, (1, -1, 1))
+
+    weights_vec = np.zeros((1, 1, seqlen), dtype=np.float32)
+    weights_vec[:, -1, -1] = 1.0
+    weights_vec = np.reshape(weights_vec, (1, -1, 1))
+
+    return x_seq_list, target_output, weights_vec
+
+
+if __name__ == '__main__':
+
+    dirname = os.path.dirname(__file__)
+    ckpts_dir = os.path.join(dirname , 'checkpoints')
+    tb_logs_dir = os.path.join(dirname, 'logs')
+
+    batch_size = 1
+    input_size = 10
+    output_size = 1
+    sequence_max_length = 25
+    words_count = 256
+    word_size = 64
+    read_heads = 2
+
+    learning_rate = 1e-4
+    momentum = 0.9
+
+    from_checkpoint = None
+    iterations = 10000
+    start_step = 0
+
+    options,_ = getopt.getopt(sys.argv[1:], '', ['checkpoint=', 'iterations=', 'start='])
+
+    for opt in options:
+        if opt[0] == '--checkpoint':
+            from_checkpoint = opt[1]
+        elif opt[0] == '--iterations':
+            iterations = int(opt[1])
+        elif opt[0] == '--start':
+            start_step = int(opt[1])
+
+    graph = tf.Graph()
+    with graph.as_default():
+        with tf.Session(graph=graph) as session:
+
+            llprint("Building Computational Graph ... ")
+
+            optimizer = tf.train.RMSPropOptimizer(learning_rate, momentum=momentum)
+            summerizer = tf.train.SummaryWriter(tb_logs_dir, session.graph)
+
+            ncomputer = DNC(
+                FeedforwardController,
+                input_size,
+                output_size,
+                sequence_max_length,
+                words_count,
+                word_size,
+                read_heads,
+                batch_size
+            )
+
+            output, _ = ncomputer.get_outputs()
+
+            loss_weights = tf.placeholder(tf.float32, [batch_size, None, 1])
+            loss = tf.reduce_mean(tf.square((loss_weights * output) - ncomputer.target_output))
+
+            summeries = []
+
+            gradients = optimizer.compute_gradients(loss)
+            apply_gradients = optimizer.apply_gradients(gradients)
+
+            summeries.append(tf.scalar_summary("Loss", loss))
+
+            summerize_op = tf.merge_summary(summeries)
+            no_summerize = tf.no_op()
+
+            llprint("Done!\n")
+
+            llprint("Initializing Variables ... ")
+            session.run(tf.initialize_all_variables())
+            llprint("Done!\n")
+
+            if from_checkpoint is not None:
+                llprint("Restoring Checkpoint %s ... " % (from_checkpoint))
+                ncomputer.restore(session, ckpts_dir, from_checkpoint)
+                llprint("Done!\n")
+
+
+            last_100_losses = []
+
+            start = 0 if start_step == 0 else start_step + 1
+            end = start_step + iterations + 1
+
+            start_time_100 = time.time()
+            end_time_100 = None
+            avg_100_time = 0.
+            avg_counter = 0
+
+            for i in xrange(start, end + 1):
+                try:
+
+                    # We use for training just (sequence_max_length / 2) examples
+                    seq_len = np.random.randint(2, (sequence_max_length / 2) + 1)
+                    input_data, target_output, weights = generate_data(seq_len, input_size)
+
+                    summerize = (i % 100 == 0)
+                    take_checkpoint = (i != 0) and (i % end == 0)
+
+                    output_, loss_value, _, summary = session.run([
+                        output,
+                        loss,
+                        apply_gradients,
+                        summerize_op if summerize else no_summerize
+                    ], feed_dict={
+                        ncomputer.input_data: input_data,
+                        ncomputer.target_output: target_output,
+                        ncomputer.sequence_length: seq_len + 1,
+                        loss_weights: weights
+                    })
+
+                    last_100_losses.append(loss_value)
+                    summerizer.add_summary(summary, i)
+
+                    if summerize:
+                        llprint("\rIteration %d/%d" % (i, end))
+                        llprint("\nAvg. Cross-Entropy: %.7f\n" % (np.mean(last_100_losses)))
+
+                        end_time_100 = time.time()
+                        elapsed_time = (end_time_100 - start_time_100) / 60
+                        avg_counter += 1
+                        avg_100_time += (1. / avg_counter) * (elapsed_time - avg_100_time)
+                        estimated_time = (avg_100_time * ((end - i) / 100.)) / 60.
+
+                        print "Avg. 100 iterations time: %.2f minutes" % (avg_100_time)
+                        print "Approx. time to completion: %.2f hours" % (estimated_time)
+                        print "DNC input", input_data
+                        print "Target_output", target_output
+                        output_ = output_ * weights
+                        print "DNC output", output_
+                        print "Real argmax(X): ", str(int(target_output[-1, -1, -1]))
+                        print "Predicted f(X): ", str(int(round(output_[-1, -1, -1])))
+
+                        start_time_100 = time.time()
+                        last_100_losses = []
+
+                    if take_checkpoint:
+                        llprint("\nSaving Checkpoint ... "),
+                        ncomputer.save(session, ckpts_dir, 'step-%d' % (i))
+                        llprint("Done!\n")
+                except KeyboardInterrupt:
+
+                    llprint("\nSaving Checkpoint ... "),
+                    ncomputer.save(session, ckpts_dir, 'step-%d' % (i))
+                    llprint("Done!\n")
+                    sys.exit(0)
+
+            llprint("\nTesting generalization...\n")
+            for i in xrange(100):
+                llprint("\nIteration %d/%d" % (i, 100))
+                # We test now the learned generalization using sequence_max_length examples
+                seq_len = np.random.randint(2, sequence_max_length + 1)
+                input_data, target_output, weights = generate_data(seq_len, input_size)
+
+                output_ = session.run([output], feed_dict={
+                        ncomputer.input_data: input_data,
+                        ncomputer.target_output: target_output,
+                        ncomputer.sequence_length: seq_len + 1,
+                        loss_weights: weights
+                })
+
+                output_ = output_ * weights
+
+                print "\nReal argmax(X): ", str(int(target_output[-1, -1, -1]))
+                print "Predicted f(X):   ", str(int(round(output_[-1, -1, -1])))
+
+            llprint("Done!\n")