From 2ddb47074e1095ff41898c564e1ccd7c755db991 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:34:58 +0100
Subject: [PATCH 01/22] Create README.md

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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
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+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]
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+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]
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+
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+
+Iteration 975/1000
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+
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+
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+
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+
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+
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+
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+
+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
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+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
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+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]

From 3251a02d15646f27ee8b090e85169e8b3e6a1d25 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:40:17 +0100
Subject: [PATCH 02/22] first commit

---
 tasks/adding/feedforward_controller.py |  28 ++++
 tasks/adding/train.py                  | 181 +++++++++++++++++++++++++
 2 files changed, 209 insertions(+)
 create mode 100644 tasks/adding/feedforward_controller.py
 create mode 100644 tasks/adding/train.py

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

From 1bcf71c21b6c8138679acc30a4fcb8cd54c4d265 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:44:08 +0100
Subject: [PATCH 03/22] first commit

---
 tasks/adding/dnc | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 tasks/adding/dnc

diff --git a/tasks/adding/dnc b/tasks/adding/dnc
new file mode 100644
index 00000000..8a531aa7
--- /dev/null
+++ b/tasks/adding/dnc
@@ -0,0 +1 @@
+../../dnc/

From 36124ff143bf94b55f01678669c5a6403a192a2b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:44:57 +0100
Subject: [PATCH 04/22] Delete dnc

---
 tasks/adding/dnc | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 tasks/adding/dnc

diff --git a/tasks/adding/dnc b/tasks/adding/dnc
deleted file mode 100644
index 8a531aa7..00000000
--- a/tasks/adding/dnc
+++ /dev/null
@@ -1 +0,0 @@
-../../dnc/

From 6d6fc6771c39228938afbd8d6a980aeb58dc204d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:45:46 +0100
Subject: [PATCH 05/22] first commit

---
 tasks/adding/checkpoints/README.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 tasks/adding/checkpoints/README.md

diff --git a/tasks/adding/checkpoints/README.md b/tasks/adding/checkpoints/README.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/tasks/adding/checkpoints/README.md
@@ -0,0 +1 @@
+

From 37ab2eca660d9dc6f29ec77b0380828e263c9b67 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:46:12 +0100
Subject: [PATCH 06/22] first commit

---
 tasks/adding/logs/README.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 tasks/adding/logs/README.md

diff --git a/tasks/adding/logs/README.md b/tasks/adding/logs/README.md
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/tasks/adding/logs/README.md
@@ -0,0 +1 @@
+

From c94a3e7c2c055f18d8b915b2440a55af81349485 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:52:43 +0100
Subject: [PATCH 07/22] first commit

---
 tasks/adding/dnc | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 tasks/adding/dnc

diff --git a/tasks/adding/dnc b/tasks/adding/dnc
new file mode 100644
index 00000000..8a531aa7
--- /dev/null
+++ b/tasks/adding/dnc
@@ -0,0 +1 @@
+../../dnc/

From 8c8b9e34be28e68171193db527df8ff5ab60c601 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:53:02 +0100
Subject: [PATCH 08/22] Update dnc


From f300c87f379208c6d93590a898c08bb2a3c2053f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:55:42 +0100
Subject: [PATCH 09/22] Delete dnc

---
 tasks/adding/dnc | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 tasks/adding/dnc

diff --git a/tasks/adding/dnc b/tasks/adding/dnc
deleted file mode 100644
index 8a531aa7..00000000
--- a/tasks/adding/dnc
+++ /dev/null
@@ -1 +0,0 @@
-../../dnc/

From 938d64b25c72c704672ad6374b4199ccfed12cf7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:57:31 +0100
Subject: [PATCH 10/22] Create README

---
 tasks/adding/dnc/README | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 tasks/adding/dnc/README

diff --git a/tasks/adding/dnc/README b/tasks/adding/dnc/README
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/tasks/adding/dnc/README
@@ -0,0 +1 @@
+

From b1cf05f7abdc3e2b4ee8c8cbc5c7230720eecb13 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:58:44 +0100
Subject: [PATCH 11/22] Delete README.md

---
 tasks/adding/logs/README.md | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 tasks/adding/logs/README.md

diff --git a/tasks/adding/logs/README.md b/tasks/adding/logs/README.md
deleted file mode 100644
index 8b137891..00000000
--- a/tasks/adding/logs/README.md
+++ /dev/null
@@ -1 +0,0 @@
-

From 414fa8bbf55e5d3783906c22b74e7818fc5e604b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 16:59:24 +0100
Subject: [PATCH 12/22] Add files via upload

---
 tasks/adding/dnc/controller.py | 225 +++++++++++++++++++++
 tasks/adding/dnc/dnc.py        | 261 ++++++++++++++++++++++++
 tasks/adding/dnc/memory.py     | 355 +++++++++++++++++++++++++++++++++
 tasks/adding/dnc/utility.py    |  60 ++++++
 4 files changed, 901 insertions(+)
 create mode 100644 tasks/adding/dnc/controller.py
 create mode 100644 tasks/adding/dnc/dnc.py
 create mode 100644 tasks/adding/dnc/memory.py
 create mode 100644 tasks/adding/dnc/utility.py

diff --git a/tasks/adding/dnc/controller.py b/tasks/adding/dnc/controller.py
new file mode 100644
index 00000000..cc4dccaf
--- /dev/null
+++ b/tasks/adding/dnc/controller.py
@@ -0,0 +1,225 @@
+import tensorflow as tf
+import numpy as np
+
+class BaseController:
+
+    def __init__(self, input_size, output_size, memory_read_heads, memory_word_size, batch_size=1):
+        """
+        constructs a controller as described in the DNC paper:
+        http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html
+
+        Parameters:
+        ----------
+        input_size: int
+            the size of the data input vector
+        output_size: int
+            the size of the data output vector
+        memory_read_heads: int
+            the number of read haeds in the associated external memory
+        memory_word_size: int
+            the size of the word in the associated external memory
+        batch_size: int
+            the size of the input data batch [optional, usually set by the DNC object]
+        """
+
+        self.input_size = input_size
+        self.output_size = output_size
+        self.read_heads = memory_read_heads
+        self.word_size = memory_word_size
+        self.batch_size = batch_size
+
+        # indicates if the internal neural network is recurrent
+        # by the existence of recurrent_update and get_state methods
+        has_recurrent_update = callable(getattr(self, 'update_state', None))
+        has_get_state = callable(getattr(self, 'get_state', None))
+        self.has_recurrent_nn =  has_recurrent_update and has_get_state
+
+        # the actual size of the neural network input after flatenning and
+        # concatenating the input vector with the previously read vctors from memory
+        self.nn_input_size = self.word_size * self.read_heads + self.input_size
+
+        self.interface_vector_size = self.word_size * self.read_heads + 3 * self.word_size + 5 * self.read_heads + 3
+
+        # define network vars
+        with tf.name_scope("controller"):
+            self.network_vars()
+
+            nn_output_size = None
+            with tf.variable_scope("shape_inference"):
+                nn_output_size = self.get_nn_output_size()
+
+            initial_std = lambda in_nodes: np.min(1e-2, np.sqrt(2.0 / in_nodes))
+
+            # defining internal weights of the controller
+            self.interface_weights = tf.Variable(
+                tf.truncated_normal([nn_output_size, self.interface_vector_size], stddev=initial_std(nn_output_size)),
+                name='interface_weights'
+            )
+            self.nn_output_weights = tf.Variable(
+                tf.truncated_normal([nn_output_size, self.output_size], stddev=initial_std(nn_output_size)),
+                name='nn_output_weights'
+            )
+            self.mem_output_weights = tf.Variable(
+                tf.truncated_normal([self.word_size * self.read_heads, self.output_size],  stddev=initial_std(self.word_size * self.read_heads)),
+                name='mem_output_weights'
+            )
+
+
+    def network_vars(self):
+        """
+        defines the variables needed by the internal neural network
+        [the variables should be attributes of the class, i.e. self.*]
+        """
+        raise NotImplementedError("network_vars is not implemented")
+
+
+    def network_op(self, X):
+        """
+        defines the controller's internal neural network operation
+
+        Parameters:
+        ----------
+        X: Tensor (batch_size, word_size * read_haeds + input_size)
+            the input data concatenated with the previously read vectors from memory
+
+        Returns: Tensor (batch_size, nn_output_size)
+        """
+        raise NotImplementedError("network_op method is not implemented")
+
+
+    def get_nn_output_size(self):
+        """
+        retrives the output size of the defined neural network
+
+        Returns: int
+            the output's size
+
+        Raises: ValueError
+        """
+
+        input_vector =  np.zeros([self.batch_size, self.nn_input_size], dtype=np.float32)
+        output_vector = None
+
+        if self.has_recurrent_nn:
+            output_vector,_ = self.network_op(input_vector, self.get_state())
+        else:
+            output_vector = self.network_op(input_vector)
+
+        shape = output_vector.get_shape().as_list()
+
+        if len(shape) > 2:
+            raise ValueError("Expected the neural network to output a 1D vector, but got %dD" % (len(shape) - 1))
+        else:
+            return shape[1]
+
+
+    def parse_interface_vector(self, interface_vector):
+        """
+        pasres the flat interface_vector into its various components with their
+        correct shapes
+
+        Parameters:
+        ----------
+        interface_vector: Tensor (batch_size, interface_vector_size)
+            the flattened inetrface vector to be parsed
+
+        Returns: dict
+            a dictionary with the components of the interface_vector parsed
+        """
+
+        parsed = {}
+
+        r_keys_end = self.word_size * self.read_heads
+        r_strengths_end = r_keys_end + self.read_heads
+        w_key_end = r_strengths_end + self.word_size
+        erase_end = w_key_end + 1 + self.word_size
+        write_end = erase_end + self.word_size
+        free_end = write_end + self.read_heads
+
+        r_keys_shape = (-1, self.word_size, self.read_heads)
+        r_strengths_shape = (-1, self.read_heads)
+        w_key_shape = (-1, self.word_size, 1)
+        write_shape = erase_shape = (-1, self.word_size)
+        free_shape = (-1, self.read_heads)
+        modes_shape = (-1, 3, self.read_heads)
+
+        # parsing the vector into its individual components
+        parsed['read_keys'] = tf.reshape(interface_vector[:, :r_keys_end], r_keys_shape)
+        parsed['read_strengths'] = tf.reshape(interface_vector[:, r_keys_end:r_strengths_end], r_strengths_shape)
+        parsed['write_key'] = tf.reshape(interface_vector[:, r_strengths_end:w_key_end], w_key_shape)
+        parsed['write_strength'] = tf.reshape(interface_vector[:, w_key_end], (-1, 1))
+        parsed['erase_vector'] = tf.reshape(interface_vector[:, w_key_end + 1:erase_end], erase_shape)
+        parsed['write_vector'] = tf.reshape(interface_vector[:, erase_end:write_end], write_shape)
+        parsed['free_gates'] = tf.reshape(interface_vector[:, write_end:free_end], free_shape)
+        parsed['allocation_gate'] = tf.expand_dims(interface_vector[:, free_end], 1)
+        parsed['write_gate'] = tf.expand_dims(interface_vector[:, free_end + 1], 1)
+        parsed['read_modes'] = tf.reshape(interface_vector[:, free_end + 2:], modes_shape)
+
+        # transforming the components to ensure they're in the right ranges
+        parsed['read_strengths'] = 1 + tf.nn.softplus(parsed['read_strengths'])
+        parsed['write_strength'] = 1 + tf.nn.softplus(parsed['write_strength'])
+        parsed['erase_vector'] = tf.nn.sigmoid(parsed['erase_vector'])
+        parsed['free_gates'] = tf.nn.sigmoid(parsed['free_gates'])
+        parsed['allocation_gate'] = tf.nn.sigmoid(parsed['allocation_gate'])
+        parsed['write_gate'] = tf.nn.sigmoid(parsed['write_gate'])
+        parsed['read_modes'] = tf.nn.softmax(parsed['read_modes'], 1)
+
+        return parsed
+
+    def process_input(self, X, last_read_vectors, state=None):
+        """
+        processes input data through the controller network and returns the
+        pre-output and interface_vector
+
+        Parameters:
+        ----------
+        X: Tensor (batch_size, input_size)
+            the input data batch
+        last_read_vectors: (batch_size, word_size, read_heads)
+            the last batch of read vectors from memory
+        state: Tuple
+            state vectors if the network is recurrent
+
+        Returns: Tuple
+            pre-output: Tensor (batch_size, output_size)
+            parsed_interface_vector: dict
+        """
+
+        flat_read_vectors = tf.reshape(last_read_vectors, (-1, self.word_size * self.read_heads))
+        complete_input = tf.concat(1, [X, flat_read_vectors])
+        nn_output, nn_state = None, None
+
+        if self.has_recurrent_nn:
+            nn_output, nn_state = self.network_op(complete_input, state)
+        else:
+            nn_output = self.network_op(complete_input)
+
+        pre_output = tf.matmul(nn_output, self.nn_output_weights)
+        interface = tf.matmul(nn_output, self.interface_weights)
+        parsed_interface = self.parse_interface_vector(interface)
+
+        if self.has_recurrent_nn:
+            return pre_output, parsed_interface, nn_state
+        else:
+            return pre_output, parsed_interface
+
+
+    def final_output(self, pre_output, new_read_vectors):
+        """
+        returns the final output by taking rececnt memory changes into account
+
+        Parameters:
+        ----------
+        pre_output: Tensor (batch_size, output_size)
+            the ouput vector from the input processing step
+        new_read_vectors: Tensor (batch_size, words_size, read_heads)
+            the newly read vectors from the updated memory
+
+        Returns: Tensor (batch_size, output_size)
+        """
+
+        flat_read_vectors = tf.reshape(new_read_vectors, (-1, self.word_size * self.read_heads))
+
+        final_output = pre_output + tf.matmul(flat_read_vectors, self.mem_output_weights)
+
+        return final_output
diff --git a/tasks/adding/dnc/dnc.py b/tasks/adding/dnc/dnc.py
new file mode 100644
index 00000000..db91927f
--- /dev/null
+++ b/tasks/adding/dnc/dnc.py
@@ -0,0 +1,261 @@
+import tensorflow as tf
+from memory import Memory
+import os
+
+class DNC:
+
+    def __init__(self, controller_class, input_size, output_size, max_sequence_length,
+                 memory_words_num = 256, memory_word_size = 64, memory_read_heads = 4, batch_size = 1):
+        """
+        constructs a complete DNC architecture as described in the DNC paper
+        http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html
+
+        Parameters:
+        -----------
+        controller_class: BaseController
+            a concrete implementation of the BaseController class
+        input_size: int
+            the size of the input vector
+        output_size: int
+            the size of the output vector
+        max_sequence_length: int
+            the maximum length of an input sequence
+        memory_words_num: int
+            the number of words that can be stored in memory
+        memory_word_size: int
+            the size of an individual word in memory
+        memory_read_heads: int
+            the number of read heads in the memory
+        batch_size: int
+            the size of the data batch
+        """
+
+        self.input_size = input_size
+        self.output_size = output_size
+        self.max_sequence_length = max_sequence_length
+        self.words_num = memory_words_num
+        self.word_size = memory_word_size
+        self.read_heads = memory_read_heads
+        self.batch_size = batch_size
+
+        self.memory = Memory(self.words_num, self.word_size, self.read_heads, self.batch_size)
+        self.controller = controller_class(self.input_size, self.output_size, self.read_heads, self.word_size, self.batch_size)
+
+        # input data placeholders
+        self.input_data = tf.placeholder(tf.float32, [batch_size, None, input_size], name='input')
+        self.target_output = tf.placeholder(tf.float32, [batch_size, None, output_size], name='targets')
+        self.sequence_length = tf.placeholder(tf.int32, name='sequence_length')
+
+        self.input_padding = tf.zeros([batch_size, max_sequence_length, input_size])
+
+        self.build_graph()
+
+
+    def _step_op(self, step, controller_state=None):
+        """
+        performs a step operation on the input step data
+
+        Parameters:
+        ----------
+        step: Tensor (batch_size, input_size)
+        controller_state: Tuple
+            the state of the controller if it's recurrent
+
+        Returns: Tuple
+            output: Tensor (batch_size, output_size)
+            memory_view: dict
+        """
+
+        last_read_vectors = self.memory.read_vectors
+        pre_output, interface, nn_state = None, None, None
+
+        if self.controller.has_recurrent_nn:
+            pre_output, interface, nn_state = self.controller.process_input(step, last_read_vectors, controller_state)
+        else:
+            pre_output, interface = self.controller.process_input(step, last_read_vectors)
+
+        usage_vector, write_weighting, memory_matrix, link_matrix, precedence_vector = self.memory.write(
+            interface['write_key'],
+            interface['write_strength'],
+            interface['free_gates'],
+            interface['allocation_gate'],
+            interface['write_gate'],
+            interface['write_vector'],
+            interface['erase_vector']
+        )
+
+        read_weightings, read_vectors = self.memory.read(
+            interface['read_keys'],
+            interface['read_strengths'],
+            link_matrix,
+            interface['read_modes'],
+            memory_matrix
+        )
+
+        return [
+
+            # report new memory state to be updated outside the condition branch
+            usage_vector,
+            write_weighting,
+            memory_matrix,
+            link_matrix,
+            precedence_vector,
+            read_weightings,
+            read_vectors,
+
+            self.controller.final_output(pre_output, read_vectors),
+            interface['free_gates'],
+            interface['allocation_gate'],
+            interface['write_gate'],
+
+            # report new state of RNN if exists
+            nn_state[0] if nn_state is not None else tf.zeros(1),
+            nn_state[1] if nn_state is not None else tf.zeros(1)
+        ]
+
+
+    def _dummy_op(self, controller_state=None):
+        """
+        returns dummy outputs for padded, out of sequence inputs
+        """
+
+        return [
+
+            # report the current memory state unchanged
+            self.memory.usage_vector,
+            self.memory.write_weighting,
+            self.memory.memory_matrix,
+            self.memory.link_matrix,
+            self.memory.precedence_vector,
+            self.memory.read_weightings,
+            self.memory.read_vectors,
+
+            tf.zeros([self.batch_size, self.output_size]),
+            tf.zeros([self.batch_size, self.read_heads]),
+            tf.zeros([self.batch_size, 1]),
+            tf.zeros([self.batch_size, 1]),
+
+            # report state of RNN if exists
+            controller_state[0] if controller_state is not None else tf.zeros(1),
+            controller_state[1] if controller_state is not None else tf.zeros(1)
+        ]
+
+
+    def build_graph(self):
+        """
+        builds the computational graph that performs a step-by-step evaluation
+        of the input data batches
+        """
+
+        padding = tf.slice(self.input_padding, [0, self.sequence_length, 0], [-1, -1, -1])
+        data = tf.concat(1, [self.input_data, padding])
+
+        time_steps = tf.unpack(data, num=self.max_sequence_length, axis=1)
+
+        outputs = []
+        free_gates = []
+        allocation_gates = []
+        write_gates = []
+        read_weightings = []
+        write_weightings = []
+        usage_vectors = []
+        dependencies = [tf.no_op()]
+
+        controller_state = self.controller.get_state() if self.controller.has_recurrent_nn else None
+
+        with tf.variable_scope("sequence_loop") as scope:
+            for t, step in enumerate(time_steps):
+
+                with tf.control_dependencies(dependencies):
+                    output_list = tf.cond(t < self.sequence_length,
+                        # if step is within the sequence_length, perform regualr operations
+                        lambda: self._step_op(step, controller_state),
+                        # otherwise: perform dummy operation
+                        lambda: self._dummy_op(controller_state)
+                    )
+
+                    scope.reuse_variables()
+
+                    # update memory parameters
+                    self.memory.usage_vector = output_list[0]
+                    self.memory.write_weighting = output_list[1]
+                    self.memory.memory_matrix = output_list[2]
+                    self.memory.link_matrix = output_list[3]
+                    self.memory.precedence_vector = output_list[4]
+                    self.memory.read_weightings = output_list[5]
+                    self.memory.read_vectors = output_list[6]
+
+                    if self.controller.has_recurrent_nn:
+                        controller_state = (output_list[11], output_list[12])
+
+
+                    outputs.append(output_list[7])
+
+                    # collecting memory view for the current step
+                    free_gates.append(output_list[8])
+                    allocation_gates.append(output_list[9])
+                    write_gates.append(output_list[10])
+                    read_weightings.append(output_list[5])
+                    write_weightings.append(output_list[1])
+                    usage_vectors.append(output_list[0])
+
+                    # just to make sure iterations run serially
+                    dependencies = [tf.identity(output_list[0])]
+
+        if self.controller.has_recurrent_nn:            
+            dependencies.append(self.controller.update_state(controller_state))
+
+        with tf.control_dependencies(dependencies):
+            self.packed_output = tf.slice(tf.pack(outputs, axis=1), [0, 0, 0], [-1, self.sequence_length, -1])
+            self.packed_memory_view = {
+                'free_gates': tf.slice(tf.pack(free_gates, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
+                'allocation_gates': tf.slice(tf.pack(allocation_gates, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
+                'write_gates': tf.slice(tf.pack(write_gates, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
+                'read_weightings': tf.slice(tf.pack(read_weightings, axis=1), [0, 0, 0, 0], [-1, self.sequence_length, -1, -1]),
+                'write_weightings': tf.slice(tf.pack(write_weightings, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
+                'usage_vectors': tf.slice(tf.pack(usage_vectors, axis=1), [0, 0, 0], [-1, self.sequence_length, -1])
+            }
+
+
+    def get_outputs(self):
+        """
+        returns the graph nodes for the output and memory view
+
+        Returns: Tuple
+            outputs: Tensor (batch_size, time_steps, output_size)
+            memory_view: dict
+        """
+        return self.packed_output, self.packed_memory_view
+
+
+    def save(self, session, ckpts_dir, name):
+        """
+        saves the current values of the model's parameters to a checkpoint
+
+        Parameters:
+        ----------
+        session: tf.Session
+            the tensorflow session to save
+        ckpts_dir: string
+            the path to the checkpoints directories
+        name: string
+            the name of the checkpoint subdirectory
+        """
+        checkpoint_dir = os.path.join(ckpts_dir, name)
+
+        if not os.path.exists(checkpoint_dir):
+            os.makedirs(checkpoint_dir)
+
+        tf.train.Saver(tf.trainable_variables()).save(session, os.path.join(checkpoint_dir, 'model.ckpt'))
+
+
+    def restore(self, session, ckpts_dir, name):
+        """
+        session: tf.Session
+            the tensorflow session to restore into
+        ckpts_dir: string
+            the path to the checkpoints directories
+        name: string
+            the name of the checkpoint subdirectory
+        """
+        tf.train.Saver(tf.trainable_variables()).restore(session, os.path.join(ckpts_dir, name, 'model.ckpt'))
diff --git a/tasks/adding/dnc/memory.py b/tasks/adding/dnc/memory.py
new file mode 100644
index 00000000..b1d0cc8d
--- /dev/null
+++ b/tasks/adding/dnc/memory.py
@@ -0,0 +1,355 @@
+import tensorflow as tf
+import numpy as np
+import utility
+
+class Memory:
+
+    def __init__(self, words_num=256, word_size=64, read_heads=4, batch_size=1):
+        """
+        constructs a memory matrix with read heads and a write head as described
+        in the DNC paper
+        http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html
+
+        Parameters:
+        ----------
+        words_num: int
+            the maximum number of words that can be stored in the memory at the
+            same time
+        word_size: int
+            the size of the individual word in the memory
+        read_heads: int
+            the number of read heads that can read simultaneously from the memory
+        batch_size: int
+            the size of input data batch
+        """
+
+        self.words_num = words_num
+        self.word_size = word_size
+        self.read_heads = read_heads
+        self.batch_size = batch_size
+
+        with tf.name_scope('external_memory'):
+
+            self.memory_matrix = tf.fill([batch_size, words_num, word_size], 1e-6)
+            self.usage_vector = tf.zeros([batch_size, words_num, ])
+            self.precedence_vector = tf.zeros([batch_size, words_num, ])
+            self.link_matrix = tf.zeros([batch_size, words_num, words_num])
+
+            self.write_weighting = tf.fill([batch_size, words_num, ], 1e-6)
+            self.read_weightings = tf.fill([batch_size, words_num, read_heads], 1e-6)
+
+            self.read_vectors = tf.fill([batch_size, word_size, read_heads], 1e-6)
+
+            # a words_num x words_num identity matrix
+            self.I = tf.constant(np.identity(words_num, dtype=np.float32))
+
+
+    def get_lookup_weighting(self, keys, strengths):
+        """
+        retrives a content-based adderssing weighting given the keys
+
+        Parameters:
+        ----------
+        keys: Tensor (batch_size, word_size, number_of_keys)
+            the keys to query the memory with
+        strengths: Tensor (batch_size, number_of_keys, )
+            the list of strengths for each lookup key
+
+        Returns: Tensor (batch_size, words_num, number_of_keys)
+            The list of lookup weightings for each provided key
+        """
+
+        normalized_memory = tf.nn.l2_normalize(self.memory_matrix, 2)
+        normalized_keys = tf.nn.l2_normalize(keys, 1)
+
+        similiarity = tf.batch_matmul(normalized_memory, normalized_keys)
+        strengths = tf.expand_dims(strengths, 1)
+
+        return tf.nn.softmax(similiarity * strengths, 1)
+
+
+    def update_usage_vector(self, free_gates):
+        """
+        updates and returns the usgae vector given the values of the free gates
+
+        Parameters:
+        ----------
+        free_gates: Tensor (batch_size, read_heads, )
+
+        Returns: Tensor (batch_size, words_num, )
+            the updated usage vector
+        """
+        free_gates = tf.expand_dims(free_gates, 1)
+
+        retention_vector = tf.reduce_prod(1 - self.read_weightings * free_gates, 2)
+        updated_usage = (self.usage_vector + self.write_weighting - self.usage_vector * self.write_weighting)  * retention_vector
+
+        return updated_usage
+
+
+    def get_allocation_weighting(self, sorted_usage, free_list):
+        """
+        retreives the writing allocation weighting based on the usage free list
+
+        Parameters:
+        ----------
+        sorted_usage: Tensor (batch_size, words_num, )
+            the usage vector sorted ascndingly
+        free_list: Tensor (batch, words_num, )
+            the original indecies of the sorted usage vector
+
+        Returns: Tensor (batch_size, words_num, )
+            the allocation weighting for each word in memory
+        """
+
+        shifted_cumprod = tf.cumprod(sorted_usage, axis = 1, exclusive=True)
+        unordered_allocation_weighting = (1 - sorted_usage) * shifted_cumprod
+
+        allocation_weighting_batches = []
+        for b in range(self.batch_size):
+            allocation_weighting = tf.zeros([self.words_num])
+            unpacked_free_list = tf.unpack(free_list[b])
+            for pos, original_indx in enumerate(unpacked_free_list):
+                mask = tf.squeeze(tf.slice(self.I, [original_indx, 0], [1, -1]))
+                allocation_weighting += mask * unordered_allocation_weighting[b, pos]
+            allocation_weighting_batches.append(allocation_weighting)
+
+        return tf.pack(allocation_weighting_batches)
+
+
+    def update_write_weighting(self, lookup_weighting, allocation_weighting, write_gate, allocation_gate):
+        """
+        updates and returns the current write_weighting
+
+        Parameters:
+        ----------
+        lookup_weighting: Tensor (batch_size, words_num, 1)
+            the weight of the lookup operation in writing
+        allocation_weighting: Tensor (batch_size, words_num)
+            the weight of the allocation operation in writing
+        write_gate: (batch_size, 1)
+            the fraction of writing to be done
+        allocation_gate: (batch_size, 1)
+            the fraction of allocation to be done
+
+        Returns: Tensor (batch_size, words_num)
+            the updated write_weighting
+        """
+
+        # remove the dimension of 1 from the lookup_weighting
+        lookup_weighting = tf.squeeze(lookup_weighting)
+
+        updated_write_weighting = write_gate * (allocation_gate * allocation_weighting + (1 - allocation_gate) * lookup_weighting)
+
+        return updated_write_weighting
+
+
+    def update_memory(self, write_weighting, write_vector, erase_vector):
+        """
+        updates and returns the memory matrix given the weighting and write and erase vectors
+
+        Parameters:
+        ----------
+        write_weighting: Tensor (batch_size, words_num)
+            the weight of writing at each memory location
+        write_vector: Tensor (batch_size, word_size)
+            a vector specifying what to write
+        erase_vector: Tensor (batch_size, word_size)
+            a vector specifying what to erase from memory
+
+        Returns: Tensor (batch_size, words_num, word_size)
+            the updated memory matrix
+        """
+
+        # expand data with a dimension of 1 at multiplication-adjacent location
+        # to force matmul to behave as an outer product
+        write_weighting = tf.expand_dims(write_weighting, 2)
+        write_vector = tf.expand_dims(write_vector, 1)
+        erase_vector = tf.expand_dims(erase_vector, 1)
+
+        erasing = self.memory_matrix * (1 - tf.batch_matmul(write_weighting, erase_vector))
+        writing = tf.batch_matmul(write_weighting, write_vector)
+        updated_memory = erasing + writing
+
+        return updated_memory
+
+
+    def update_precedence_vector(self, write_weighting):
+        """
+        updates the precedence vector given the latest write weighting
+
+        Parameters:
+        ----------
+        write_weighting: Tensor (batch_size,words_num)
+            the latest write weighting for the memory
+
+        Returns: Tensor (batch_size, words_num)
+            the updated precedence vector
+        """
+
+        reset_factor = 1 - tf.reduce_sum(write_weighting, 1, keep_dims=True)
+        updated_precedence_vector = reset_factor * self.precedence_vector + write_weighting
+
+        return updated_precedence_vector
+
+
+    def update_link_matrix(self, write_weighting):
+        """
+        updates and returns the temporal link matrix gievn for the latest write
+
+        Parameters:
+        ----------
+        write_weighting: Tensor (batch_size, words_num)
+            the latest write_weighting for the memorye
+
+        Returns: Tensor (batch_size, words_num, words_num)
+            the updated temporal link matrix
+        """
+
+        write_weighting = tf.expand_dims(write_weighting, 2)
+        precedence_vector = tf.expand_dims(self.precedence_vector, 1)
+
+        reset_factor = 1 - utility.pairwise_add(write_weighting, is_batch=True)
+        updated_link_matrix = reset_factor * self.link_matrix + tf.batch_matmul(write_weighting, precedence_vector)
+        updated_link_matrix = (1 - self.I) * updated_link_matrix  # eliminates self-links
+
+        return updated_link_matrix
+
+
+    def get_directional_weightings(self, link_matrix):
+        """
+        computes and returns the forward and backward reading weightings
+
+        Parameters:
+        ----------
+        link_matrix: Tensor (batch_size, words_num, words_num)
+            the temporal link matrix
+
+        Returns: Tuple
+            forward weighting: Tensor (batch_size, words_num, read_heads),
+            backward weighting: Tensor (batch_size, words_num, read_heads)
+        """
+
+        forward_weighting = tf.batch_matmul(link_matrix, self.read_weightings)
+        backward_weighting = tf.batch_matmul(link_matrix, self.read_weightings, adj_x=True)
+
+        return forward_weighting, backward_weighting
+
+
+    def update_read_weightings(self, lookup_weightings, forward_weighting, backward_weighting, read_mode):
+        """
+        updates and returns the current read_weightings
+
+        Parameters:
+        ----------
+        lookup_weightings: Tensor (batch_size, words_num, read_heads)
+            the content-based read weighting
+        forward_weighting: Tensor (batch_size, words_num, read_heads)
+            the forward direction read weighting
+        backward_weighting: Tensor (batch_size, words_num, read_heads)
+            the backward direction read weighting
+        read_mode: Tesnor (batch_size, 3, read_heads)
+            the softmax distribution between the three read modes
+
+        Returns: Tensor (batch_size, words_num, read_heads)
+        """
+
+        backward_mode = tf.expand_dims(read_mode[:, 0, :], 1) * backward_weighting
+        lookup_mode = tf.expand_dims(read_mode[:, 1, :], 1) * lookup_weightings
+        forward_mode = tf.expand_dims(read_mode[:, 2, :], 1) * forward_weighting
+        updated_read_weightings = backward_mode + lookup_mode + forward_mode
+
+        return updated_read_weightings
+
+
+    def update_read_vectors(self, memory_matrix, read_weightings):
+        """
+        reads, updates, and returns the read vectors of the recently updated memory
+
+        Parameters:
+        ----------
+        memory_matrix: Tensor (batch_size, words_num, word_size)
+            the recently updated memory matrix
+        read_weightings: Tensor (batch_size, words_num, read_heads)
+            the amount of info to read from each memory location by each read head
+
+        Returns: Tensor (word_size, read_heads)
+        """
+
+        updated_read_vectors = tf.batch_matmul(memory_matrix, read_weightings, adj_x=True)
+
+        return updated_read_vectors
+
+
+    def write(self, key, strength, free_gates, allocation_gate, write_gate, write_vector, erase_vector):
+        """
+        defines the complete pipeline of writing to memory gievn the write variables
+
+        Parameters:
+        ----------
+        key: Tensor (batch_size, word_size, 1)
+            the key to query the memory location with
+        strength: (batch_size, 1)
+            the strength of the query key
+        free_gates: Tensor (batch_size, read_heads)
+            the degree to which location at read haeds will be freed
+        allocation_gate: (batch_size, 1)
+            the fraction of writing that is being allocated in a new locatio
+        write_gate: (batch_size, 1)
+            the amount of information to be written to memory
+        write_vector: Tensor (batch_size, word_size)
+            specifications of what to write to memory
+        erase_vector: Tensor(batch_size, word_size)
+            specifications of what to erase from memory
+
+        Returns : Tuple
+            the updated usage vector: Tensor (batch_size, words_num)
+            the updated write_weighting: Tensor(batch_size, words_num)
+            the updated memory_matrix: Tensor (batch_size, words_num, words_size)
+            the updated link matrix: Tensor(batch_size, words_num, words_num)
+            the updated precedence vector: Tensor (batch_size, words_num)
+        """
+
+        lookup_weighting = self.get_lookup_weighting(key, strength)
+        usage_vector = self.update_usage_vector(free_gates)
+
+        sorted_usage, free_list = tf.nn.top_k(-1 * usage_vector, self.words_num)
+        sorted_usage = -1 * sorted_usage
+
+        allocation_weighting = self.get_allocation_weighting(sorted_usage, free_list)
+        write_weighting = self.update_write_weighting(lookup_weighting, allocation_weighting, write_gate, allocation_gate)
+        memory_matrix = self.update_memory(write_weighting, write_vector, erase_vector)
+        link_matrix = self.update_link_matrix(write_weighting)
+        precedence_vector = self.update_precedence_vector(write_weighting)
+
+        return usage_vector, write_weighting, memory_matrix, link_matrix, precedence_vector
+
+
+    def read(self, keys, strengths, link_matrix, read_modes, memory_matrix):
+        """
+        defines the complete pipeline for reading from memory
+
+        Parameters:
+        ----------
+        keys: Tensor (batch_size, word_size, read_heads)
+            the kyes to query the memory locations with
+        strengths: Tensor (batch_size, read_heads)
+            the strength of each read key
+        link_matrix: Tensor (batch_size, words_num, words_num)
+            the updated link matrix from the last writing
+        read_modes: Tensor (batch_size, 3, read_heads)
+            the softmax distribution between the three read modes
+        memory_matrix: Tensor (batch_size, words_num, word_size)
+            the updated memory matrix from the last writing
+
+        Returns: Tuple
+            the updated read_weightings: Tensor(batch_size, words_num, read_heads)
+            the recently read vectors: Tensor (batch_size, word_size, read_heads)
+        """
+
+        lookup_weighting = self.get_lookup_weighting(keys, strengths)
+        forward_weighting, backward_weighting = self.get_directional_weightings(link_matrix)
+        read_weightings = self.update_read_weightings(lookup_weighting, forward_weighting, backward_weighting, read_modes)
+        read_vectors = self.update_read_vectors(memory_matrix, read_weightings)
+
+        return read_weightings, read_vectors
diff --git a/tasks/adding/dnc/utility.py b/tasks/adding/dnc/utility.py
new file mode 100644
index 00000000..eee21a39
--- /dev/null
+++ b/tasks/adding/dnc/utility.py
@@ -0,0 +1,60 @@
+import tensorflow as tf
+import numpy as np
+from tensorflow.python.ops import gen_state_ops
+
+def pairwise_add(u, v=None, is_batch=False):
+    """
+    performs a pairwise summation between vectors (possibly the same)
+
+    Parameters:
+    ----------
+    u: Tensor (n, ) | (n, 1)
+    v: Tensor (n, ) | (n, 1) [optional]
+    is_batch: bool
+        a flag for whether the vectors come in a batch
+        ie.: whether the vectors has a shape of (b,n) or (b,n,1)
+
+    Returns: Tensor (n, n)
+    Raises: ValueError
+    """
+    u_shape = u.get_shape().as_list()
+
+    if len(u_shape) > 2 and not is_batch:
+        raise ValueError("Expected at most 2D tensors, but got %dD" % len(u_shape))
+    if len(u_shape) > 3 and is_batch:
+        raise ValueError("Expected at most 2D tensor batches, but got %dD" % len(u_shape))
+
+    if v is None:
+        v = u
+    else:
+        v_shape = v.get_shape().as_list()
+        if u_shape != v_shape:
+            raise VauleError("Shapes %s and %s do not match" % (u_shape, v_shape))
+
+    n = u_shape[0] if not is_batch else u_shape[1]
+
+    column_u = tf.reshape(u, (-1, 1) if not is_batch else (-1, n, 1))
+    U = tf.concat(1 if not is_batch else 2, [column_u] * n)
+
+    if v is u:
+        return U + tf.transpose(U, None if not is_batch else [0, 2, 1])
+    else:
+        row_v = tf.reshape(v, (1, -1) if not is_batch else (-1, 1, n))
+        V = tf.concat(0 if not is_batch else 1, [row_v] * n)
+
+        return U + V
+
+
+def decaying_softmax(shape, axis):
+    rank = len(shape)
+    max_val = shape[axis]
+
+    weights_vector = np.arange(1, max_val + 1, dtype=np.float32)
+    weights_vector = weights_vector[::-1]
+    weights_vector = np.exp(weights_vector) / np.sum(np.exp(weights_vector))
+
+    container = np.zeros(shape, dtype=np.float32)
+    broadcastable_shape = [1] * rank
+    broadcastable_shape[axis] = max_val
+
+    return container + np.reshape(weights_vector, broadcastable_shape)

From cd831bd3a245b0b612d3f1c061621ccd568f960b Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 17:00:05 +0100
Subject: [PATCH 13/22] Delete controller.py

---
 tasks/adding/dnc/controller.py | 225 ---------------------------------
 1 file changed, 225 deletions(-)
 delete mode 100644 tasks/adding/dnc/controller.py

diff --git a/tasks/adding/dnc/controller.py b/tasks/adding/dnc/controller.py
deleted file mode 100644
index cc4dccaf..00000000
--- a/tasks/adding/dnc/controller.py
+++ /dev/null
@@ -1,225 +0,0 @@
-import tensorflow as tf
-import numpy as np
-
-class BaseController:
-
-    def __init__(self, input_size, output_size, memory_read_heads, memory_word_size, batch_size=1):
-        """
-        constructs a controller as described in the DNC paper:
-        http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html
-
-        Parameters:
-        ----------
-        input_size: int
-            the size of the data input vector
-        output_size: int
-            the size of the data output vector
-        memory_read_heads: int
-            the number of read haeds in the associated external memory
-        memory_word_size: int
-            the size of the word in the associated external memory
-        batch_size: int
-            the size of the input data batch [optional, usually set by the DNC object]
-        """
-
-        self.input_size = input_size
-        self.output_size = output_size
-        self.read_heads = memory_read_heads
-        self.word_size = memory_word_size
-        self.batch_size = batch_size
-
-        # indicates if the internal neural network is recurrent
-        # by the existence of recurrent_update and get_state methods
-        has_recurrent_update = callable(getattr(self, 'update_state', None))
-        has_get_state = callable(getattr(self, 'get_state', None))
-        self.has_recurrent_nn =  has_recurrent_update and has_get_state
-
-        # the actual size of the neural network input after flatenning and
-        # concatenating the input vector with the previously read vctors from memory
-        self.nn_input_size = self.word_size * self.read_heads + self.input_size
-
-        self.interface_vector_size = self.word_size * self.read_heads + 3 * self.word_size + 5 * self.read_heads + 3
-
-        # define network vars
-        with tf.name_scope("controller"):
-            self.network_vars()
-
-            nn_output_size = None
-            with tf.variable_scope("shape_inference"):
-                nn_output_size = self.get_nn_output_size()
-
-            initial_std = lambda in_nodes: np.min(1e-2, np.sqrt(2.0 / in_nodes))
-
-            # defining internal weights of the controller
-            self.interface_weights = tf.Variable(
-                tf.truncated_normal([nn_output_size, self.interface_vector_size], stddev=initial_std(nn_output_size)),
-                name='interface_weights'
-            )
-            self.nn_output_weights = tf.Variable(
-                tf.truncated_normal([nn_output_size, self.output_size], stddev=initial_std(nn_output_size)),
-                name='nn_output_weights'
-            )
-            self.mem_output_weights = tf.Variable(
-                tf.truncated_normal([self.word_size * self.read_heads, self.output_size],  stddev=initial_std(self.word_size * self.read_heads)),
-                name='mem_output_weights'
-            )
-
-
-    def network_vars(self):
-        """
-        defines the variables needed by the internal neural network
-        [the variables should be attributes of the class, i.e. self.*]
-        """
-        raise NotImplementedError("network_vars is not implemented")
-
-
-    def network_op(self, X):
-        """
-        defines the controller's internal neural network operation
-
-        Parameters:
-        ----------
-        X: Tensor (batch_size, word_size * read_haeds + input_size)
-            the input data concatenated with the previously read vectors from memory
-
-        Returns: Tensor (batch_size, nn_output_size)
-        """
-        raise NotImplementedError("network_op method is not implemented")
-
-
-    def get_nn_output_size(self):
-        """
-        retrives the output size of the defined neural network
-
-        Returns: int
-            the output's size
-
-        Raises: ValueError
-        """
-
-        input_vector =  np.zeros([self.batch_size, self.nn_input_size], dtype=np.float32)
-        output_vector = None
-
-        if self.has_recurrent_nn:
-            output_vector,_ = self.network_op(input_vector, self.get_state())
-        else:
-            output_vector = self.network_op(input_vector)
-
-        shape = output_vector.get_shape().as_list()
-
-        if len(shape) > 2:
-            raise ValueError("Expected the neural network to output a 1D vector, but got %dD" % (len(shape) - 1))
-        else:
-            return shape[1]
-
-
-    def parse_interface_vector(self, interface_vector):
-        """
-        pasres the flat interface_vector into its various components with their
-        correct shapes
-
-        Parameters:
-        ----------
-        interface_vector: Tensor (batch_size, interface_vector_size)
-            the flattened inetrface vector to be parsed
-
-        Returns: dict
-            a dictionary with the components of the interface_vector parsed
-        """
-
-        parsed = {}
-
-        r_keys_end = self.word_size * self.read_heads
-        r_strengths_end = r_keys_end + self.read_heads
-        w_key_end = r_strengths_end + self.word_size
-        erase_end = w_key_end + 1 + self.word_size
-        write_end = erase_end + self.word_size
-        free_end = write_end + self.read_heads
-
-        r_keys_shape = (-1, self.word_size, self.read_heads)
-        r_strengths_shape = (-1, self.read_heads)
-        w_key_shape = (-1, self.word_size, 1)
-        write_shape = erase_shape = (-1, self.word_size)
-        free_shape = (-1, self.read_heads)
-        modes_shape = (-1, 3, self.read_heads)
-
-        # parsing the vector into its individual components
-        parsed['read_keys'] = tf.reshape(interface_vector[:, :r_keys_end], r_keys_shape)
-        parsed['read_strengths'] = tf.reshape(interface_vector[:, r_keys_end:r_strengths_end], r_strengths_shape)
-        parsed['write_key'] = tf.reshape(interface_vector[:, r_strengths_end:w_key_end], w_key_shape)
-        parsed['write_strength'] = tf.reshape(interface_vector[:, w_key_end], (-1, 1))
-        parsed['erase_vector'] = tf.reshape(interface_vector[:, w_key_end + 1:erase_end], erase_shape)
-        parsed['write_vector'] = tf.reshape(interface_vector[:, erase_end:write_end], write_shape)
-        parsed['free_gates'] = tf.reshape(interface_vector[:, write_end:free_end], free_shape)
-        parsed['allocation_gate'] = tf.expand_dims(interface_vector[:, free_end], 1)
-        parsed['write_gate'] = tf.expand_dims(interface_vector[:, free_end + 1], 1)
-        parsed['read_modes'] = tf.reshape(interface_vector[:, free_end + 2:], modes_shape)
-
-        # transforming the components to ensure they're in the right ranges
-        parsed['read_strengths'] = 1 + tf.nn.softplus(parsed['read_strengths'])
-        parsed['write_strength'] = 1 + tf.nn.softplus(parsed['write_strength'])
-        parsed['erase_vector'] = tf.nn.sigmoid(parsed['erase_vector'])
-        parsed['free_gates'] = tf.nn.sigmoid(parsed['free_gates'])
-        parsed['allocation_gate'] = tf.nn.sigmoid(parsed['allocation_gate'])
-        parsed['write_gate'] = tf.nn.sigmoid(parsed['write_gate'])
-        parsed['read_modes'] = tf.nn.softmax(parsed['read_modes'], 1)
-
-        return parsed
-
-    def process_input(self, X, last_read_vectors, state=None):
-        """
-        processes input data through the controller network and returns the
-        pre-output and interface_vector
-
-        Parameters:
-        ----------
-        X: Tensor (batch_size, input_size)
-            the input data batch
-        last_read_vectors: (batch_size, word_size, read_heads)
-            the last batch of read vectors from memory
-        state: Tuple
-            state vectors if the network is recurrent
-
-        Returns: Tuple
-            pre-output: Tensor (batch_size, output_size)
-            parsed_interface_vector: dict
-        """
-
-        flat_read_vectors = tf.reshape(last_read_vectors, (-1, self.word_size * self.read_heads))
-        complete_input = tf.concat(1, [X, flat_read_vectors])
-        nn_output, nn_state = None, None
-
-        if self.has_recurrent_nn:
-            nn_output, nn_state = self.network_op(complete_input, state)
-        else:
-            nn_output = self.network_op(complete_input)
-
-        pre_output = tf.matmul(nn_output, self.nn_output_weights)
-        interface = tf.matmul(nn_output, self.interface_weights)
-        parsed_interface = self.parse_interface_vector(interface)
-
-        if self.has_recurrent_nn:
-            return pre_output, parsed_interface, nn_state
-        else:
-            return pre_output, parsed_interface
-
-
-    def final_output(self, pre_output, new_read_vectors):
-        """
-        returns the final output by taking rececnt memory changes into account
-
-        Parameters:
-        ----------
-        pre_output: Tensor (batch_size, output_size)
-            the ouput vector from the input processing step
-        new_read_vectors: Tensor (batch_size, words_size, read_heads)
-            the newly read vectors from the updated memory
-
-        Returns: Tensor (batch_size, output_size)
-        """
-
-        flat_read_vectors = tf.reshape(new_read_vectors, (-1, self.word_size * self.read_heads))
-
-        final_output = pre_output + tf.matmul(flat_read_vectors, self.mem_output_weights)
-
-        return final_output

From 49c7752ceb4b5636c9111a71b27a3b552b51f613 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 17:00:13 +0100
Subject: [PATCH 14/22] Delete README

---
 tasks/adding/dnc/README | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 tasks/adding/dnc/README

diff --git a/tasks/adding/dnc/README b/tasks/adding/dnc/README
deleted file mode 100644
index 8b137891..00000000
--- a/tasks/adding/dnc/README
+++ /dev/null
@@ -1 +0,0 @@
-

From 98d657ab33fc6496f9917117c33721d4ac13a41f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 17:00:27 +0100
Subject: [PATCH 15/22] Delete dnc.py

---
 tasks/adding/dnc/dnc.py | 261 ----------------------------------------
 1 file changed, 261 deletions(-)
 delete mode 100644 tasks/adding/dnc/dnc.py

diff --git a/tasks/adding/dnc/dnc.py b/tasks/adding/dnc/dnc.py
deleted file mode 100644
index db91927f..00000000
--- a/tasks/adding/dnc/dnc.py
+++ /dev/null
@@ -1,261 +0,0 @@
-import tensorflow as tf
-from memory import Memory
-import os
-
-class DNC:
-
-    def __init__(self, controller_class, input_size, output_size, max_sequence_length,
-                 memory_words_num = 256, memory_word_size = 64, memory_read_heads = 4, batch_size = 1):
-        """
-        constructs a complete DNC architecture as described in the DNC paper
-        http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html
-
-        Parameters:
-        -----------
-        controller_class: BaseController
-            a concrete implementation of the BaseController class
-        input_size: int
-            the size of the input vector
-        output_size: int
-            the size of the output vector
-        max_sequence_length: int
-            the maximum length of an input sequence
-        memory_words_num: int
-            the number of words that can be stored in memory
-        memory_word_size: int
-            the size of an individual word in memory
-        memory_read_heads: int
-            the number of read heads in the memory
-        batch_size: int
-            the size of the data batch
-        """
-
-        self.input_size = input_size
-        self.output_size = output_size
-        self.max_sequence_length = max_sequence_length
-        self.words_num = memory_words_num
-        self.word_size = memory_word_size
-        self.read_heads = memory_read_heads
-        self.batch_size = batch_size
-
-        self.memory = Memory(self.words_num, self.word_size, self.read_heads, self.batch_size)
-        self.controller = controller_class(self.input_size, self.output_size, self.read_heads, self.word_size, self.batch_size)
-
-        # input data placeholders
-        self.input_data = tf.placeholder(tf.float32, [batch_size, None, input_size], name='input')
-        self.target_output = tf.placeholder(tf.float32, [batch_size, None, output_size], name='targets')
-        self.sequence_length = tf.placeholder(tf.int32, name='sequence_length')
-
-        self.input_padding = tf.zeros([batch_size, max_sequence_length, input_size])
-
-        self.build_graph()
-
-
-    def _step_op(self, step, controller_state=None):
-        """
-        performs a step operation on the input step data
-
-        Parameters:
-        ----------
-        step: Tensor (batch_size, input_size)
-        controller_state: Tuple
-            the state of the controller if it's recurrent
-
-        Returns: Tuple
-            output: Tensor (batch_size, output_size)
-            memory_view: dict
-        """
-
-        last_read_vectors = self.memory.read_vectors
-        pre_output, interface, nn_state = None, None, None
-
-        if self.controller.has_recurrent_nn:
-            pre_output, interface, nn_state = self.controller.process_input(step, last_read_vectors, controller_state)
-        else:
-            pre_output, interface = self.controller.process_input(step, last_read_vectors)
-
-        usage_vector, write_weighting, memory_matrix, link_matrix, precedence_vector = self.memory.write(
-            interface['write_key'],
-            interface['write_strength'],
-            interface['free_gates'],
-            interface['allocation_gate'],
-            interface['write_gate'],
-            interface['write_vector'],
-            interface['erase_vector']
-        )
-
-        read_weightings, read_vectors = self.memory.read(
-            interface['read_keys'],
-            interface['read_strengths'],
-            link_matrix,
-            interface['read_modes'],
-            memory_matrix
-        )
-
-        return [
-
-            # report new memory state to be updated outside the condition branch
-            usage_vector,
-            write_weighting,
-            memory_matrix,
-            link_matrix,
-            precedence_vector,
-            read_weightings,
-            read_vectors,
-
-            self.controller.final_output(pre_output, read_vectors),
-            interface['free_gates'],
-            interface['allocation_gate'],
-            interface['write_gate'],
-
-            # report new state of RNN if exists
-            nn_state[0] if nn_state is not None else tf.zeros(1),
-            nn_state[1] if nn_state is not None else tf.zeros(1)
-        ]
-
-
-    def _dummy_op(self, controller_state=None):
-        """
-        returns dummy outputs for padded, out of sequence inputs
-        """
-
-        return [
-
-            # report the current memory state unchanged
-            self.memory.usage_vector,
-            self.memory.write_weighting,
-            self.memory.memory_matrix,
-            self.memory.link_matrix,
-            self.memory.precedence_vector,
-            self.memory.read_weightings,
-            self.memory.read_vectors,
-
-            tf.zeros([self.batch_size, self.output_size]),
-            tf.zeros([self.batch_size, self.read_heads]),
-            tf.zeros([self.batch_size, 1]),
-            tf.zeros([self.batch_size, 1]),
-
-            # report state of RNN if exists
-            controller_state[0] if controller_state is not None else tf.zeros(1),
-            controller_state[1] if controller_state is not None else tf.zeros(1)
-        ]
-
-
-    def build_graph(self):
-        """
-        builds the computational graph that performs a step-by-step evaluation
-        of the input data batches
-        """
-
-        padding = tf.slice(self.input_padding, [0, self.sequence_length, 0], [-1, -1, -1])
-        data = tf.concat(1, [self.input_data, padding])
-
-        time_steps = tf.unpack(data, num=self.max_sequence_length, axis=1)
-
-        outputs = []
-        free_gates = []
-        allocation_gates = []
-        write_gates = []
-        read_weightings = []
-        write_weightings = []
-        usage_vectors = []
-        dependencies = [tf.no_op()]
-
-        controller_state = self.controller.get_state() if self.controller.has_recurrent_nn else None
-
-        with tf.variable_scope("sequence_loop") as scope:
-            for t, step in enumerate(time_steps):
-
-                with tf.control_dependencies(dependencies):
-                    output_list = tf.cond(t < self.sequence_length,
-                        # if step is within the sequence_length, perform regualr operations
-                        lambda: self._step_op(step, controller_state),
-                        # otherwise: perform dummy operation
-                        lambda: self._dummy_op(controller_state)
-                    )
-
-                    scope.reuse_variables()
-
-                    # update memory parameters
-                    self.memory.usage_vector = output_list[0]
-                    self.memory.write_weighting = output_list[1]
-                    self.memory.memory_matrix = output_list[2]
-                    self.memory.link_matrix = output_list[3]
-                    self.memory.precedence_vector = output_list[4]
-                    self.memory.read_weightings = output_list[5]
-                    self.memory.read_vectors = output_list[6]
-
-                    if self.controller.has_recurrent_nn:
-                        controller_state = (output_list[11], output_list[12])
-
-
-                    outputs.append(output_list[7])
-
-                    # collecting memory view for the current step
-                    free_gates.append(output_list[8])
-                    allocation_gates.append(output_list[9])
-                    write_gates.append(output_list[10])
-                    read_weightings.append(output_list[5])
-                    write_weightings.append(output_list[1])
-                    usage_vectors.append(output_list[0])
-
-                    # just to make sure iterations run serially
-                    dependencies = [tf.identity(output_list[0])]
-
-        if self.controller.has_recurrent_nn:            
-            dependencies.append(self.controller.update_state(controller_state))
-
-        with tf.control_dependencies(dependencies):
-            self.packed_output = tf.slice(tf.pack(outputs, axis=1), [0, 0, 0], [-1, self.sequence_length, -1])
-            self.packed_memory_view = {
-                'free_gates': tf.slice(tf.pack(free_gates, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
-                'allocation_gates': tf.slice(tf.pack(allocation_gates, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
-                'write_gates': tf.slice(tf.pack(write_gates, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
-                'read_weightings': tf.slice(tf.pack(read_weightings, axis=1), [0, 0, 0, 0], [-1, self.sequence_length, -1, -1]),
-                'write_weightings': tf.slice(tf.pack(write_weightings, axis=1), [0, 0, 0], [-1, self.sequence_length, -1]),
-                'usage_vectors': tf.slice(tf.pack(usage_vectors, axis=1), [0, 0, 0], [-1, self.sequence_length, -1])
-            }
-
-
-    def get_outputs(self):
-        """
-        returns the graph nodes for the output and memory view
-
-        Returns: Tuple
-            outputs: Tensor (batch_size, time_steps, output_size)
-            memory_view: dict
-        """
-        return self.packed_output, self.packed_memory_view
-
-
-    def save(self, session, ckpts_dir, name):
-        """
-        saves the current values of the model's parameters to a checkpoint
-
-        Parameters:
-        ----------
-        session: tf.Session
-            the tensorflow session to save
-        ckpts_dir: string
-            the path to the checkpoints directories
-        name: string
-            the name of the checkpoint subdirectory
-        """
-        checkpoint_dir = os.path.join(ckpts_dir, name)
-
-        if not os.path.exists(checkpoint_dir):
-            os.makedirs(checkpoint_dir)
-
-        tf.train.Saver(tf.trainable_variables()).save(session, os.path.join(checkpoint_dir, 'model.ckpt'))
-
-
-    def restore(self, session, ckpts_dir, name):
-        """
-        session: tf.Session
-            the tensorflow session to restore into
-        ckpts_dir: string
-            the path to the checkpoints directories
-        name: string
-            the name of the checkpoint subdirectory
-        """
-        tf.train.Saver(tf.trainable_variables()).restore(session, os.path.join(ckpts_dir, name, 'model.ckpt'))

From 0fbce1db42ad0ce4ecde1b83705928fa60acd791 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 17:00:40 +0100
Subject: [PATCH 16/22] Delete memory.py

---
 tasks/adding/dnc/memory.py | 355 -------------------------------------
 1 file changed, 355 deletions(-)
 delete mode 100644 tasks/adding/dnc/memory.py

diff --git a/tasks/adding/dnc/memory.py b/tasks/adding/dnc/memory.py
deleted file mode 100644
index b1d0cc8d..00000000
--- a/tasks/adding/dnc/memory.py
+++ /dev/null
@@ -1,355 +0,0 @@
-import tensorflow as tf
-import numpy as np
-import utility
-
-class Memory:
-
-    def __init__(self, words_num=256, word_size=64, read_heads=4, batch_size=1):
-        """
-        constructs a memory matrix with read heads and a write head as described
-        in the DNC paper
-        http://www.nature.com/nature/journal/vaop/ncurrent/full/nature20101.html
-
-        Parameters:
-        ----------
-        words_num: int
-            the maximum number of words that can be stored in the memory at the
-            same time
-        word_size: int
-            the size of the individual word in the memory
-        read_heads: int
-            the number of read heads that can read simultaneously from the memory
-        batch_size: int
-            the size of input data batch
-        """
-
-        self.words_num = words_num
-        self.word_size = word_size
-        self.read_heads = read_heads
-        self.batch_size = batch_size
-
-        with tf.name_scope('external_memory'):
-
-            self.memory_matrix = tf.fill([batch_size, words_num, word_size], 1e-6)
-            self.usage_vector = tf.zeros([batch_size, words_num, ])
-            self.precedence_vector = tf.zeros([batch_size, words_num, ])
-            self.link_matrix = tf.zeros([batch_size, words_num, words_num])
-
-            self.write_weighting = tf.fill([batch_size, words_num, ], 1e-6)
-            self.read_weightings = tf.fill([batch_size, words_num, read_heads], 1e-6)
-
-            self.read_vectors = tf.fill([batch_size, word_size, read_heads], 1e-6)
-
-            # a words_num x words_num identity matrix
-            self.I = tf.constant(np.identity(words_num, dtype=np.float32))
-
-
-    def get_lookup_weighting(self, keys, strengths):
-        """
-        retrives a content-based adderssing weighting given the keys
-
-        Parameters:
-        ----------
-        keys: Tensor (batch_size, word_size, number_of_keys)
-            the keys to query the memory with
-        strengths: Tensor (batch_size, number_of_keys, )
-            the list of strengths for each lookup key
-
-        Returns: Tensor (batch_size, words_num, number_of_keys)
-            The list of lookup weightings for each provided key
-        """
-
-        normalized_memory = tf.nn.l2_normalize(self.memory_matrix, 2)
-        normalized_keys = tf.nn.l2_normalize(keys, 1)
-
-        similiarity = tf.batch_matmul(normalized_memory, normalized_keys)
-        strengths = tf.expand_dims(strengths, 1)
-
-        return tf.nn.softmax(similiarity * strengths, 1)
-
-
-    def update_usage_vector(self, free_gates):
-        """
-        updates and returns the usgae vector given the values of the free gates
-
-        Parameters:
-        ----------
-        free_gates: Tensor (batch_size, read_heads, )
-
-        Returns: Tensor (batch_size, words_num, )
-            the updated usage vector
-        """
-        free_gates = tf.expand_dims(free_gates, 1)
-
-        retention_vector = tf.reduce_prod(1 - self.read_weightings * free_gates, 2)
-        updated_usage = (self.usage_vector + self.write_weighting - self.usage_vector * self.write_weighting)  * retention_vector
-
-        return updated_usage
-
-
-    def get_allocation_weighting(self, sorted_usage, free_list):
-        """
-        retreives the writing allocation weighting based on the usage free list
-
-        Parameters:
-        ----------
-        sorted_usage: Tensor (batch_size, words_num, )
-            the usage vector sorted ascndingly
-        free_list: Tensor (batch, words_num, )
-            the original indecies of the sorted usage vector
-
-        Returns: Tensor (batch_size, words_num, )
-            the allocation weighting for each word in memory
-        """
-
-        shifted_cumprod = tf.cumprod(sorted_usage, axis = 1, exclusive=True)
-        unordered_allocation_weighting = (1 - sorted_usage) * shifted_cumprod
-
-        allocation_weighting_batches = []
-        for b in range(self.batch_size):
-            allocation_weighting = tf.zeros([self.words_num])
-            unpacked_free_list = tf.unpack(free_list[b])
-            for pos, original_indx in enumerate(unpacked_free_list):
-                mask = tf.squeeze(tf.slice(self.I, [original_indx, 0], [1, -1]))
-                allocation_weighting += mask * unordered_allocation_weighting[b, pos]
-            allocation_weighting_batches.append(allocation_weighting)
-
-        return tf.pack(allocation_weighting_batches)
-
-
-    def update_write_weighting(self, lookup_weighting, allocation_weighting, write_gate, allocation_gate):
-        """
-        updates and returns the current write_weighting
-
-        Parameters:
-        ----------
-        lookup_weighting: Tensor (batch_size, words_num, 1)
-            the weight of the lookup operation in writing
-        allocation_weighting: Tensor (batch_size, words_num)
-            the weight of the allocation operation in writing
-        write_gate: (batch_size, 1)
-            the fraction of writing to be done
-        allocation_gate: (batch_size, 1)
-            the fraction of allocation to be done
-
-        Returns: Tensor (batch_size, words_num)
-            the updated write_weighting
-        """
-
-        # remove the dimension of 1 from the lookup_weighting
-        lookup_weighting = tf.squeeze(lookup_weighting)
-
-        updated_write_weighting = write_gate * (allocation_gate * allocation_weighting + (1 - allocation_gate) * lookup_weighting)
-
-        return updated_write_weighting
-
-
-    def update_memory(self, write_weighting, write_vector, erase_vector):
-        """
-        updates and returns the memory matrix given the weighting and write and erase vectors
-
-        Parameters:
-        ----------
-        write_weighting: Tensor (batch_size, words_num)
-            the weight of writing at each memory location
-        write_vector: Tensor (batch_size, word_size)
-            a vector specifying what to write
-        erase_vector: Tensor (batch_size, word_size)
-            a vector specifying what to erase from memory
-
-        Returns: Tensor (batch_size, words_num, word_size)
-            the updated memory matrix
-        """
-
-        # expand data with a dimension of 1 at multiplication-adjacent location
-        # to force matmul to behave as an outer product
-        write_weighting = tf.expand_dims(write_weighting, 2)
-        write_vector = tf.expand_dims(write_vector, 1)
-        erase_vector = tf.expand_dims(erase_vector, 1)
-
-        erasing = self.memory_matrix * (1 - tf.batch_matmul(write_weighting, erase_vector))
-        writing = tf.batch_matmul(write_weighting, write_vector)
-        updated_memory = erasing + writing
-
-        return updated_memory
-
-
-    def update_precedence_vector(self, write_weighting):
-        """
-        updates the precedence vector given the latest write weighting
-
-        Parameters:
-        ----------
-        write_weighting: Tensor (batch_size,words_num)
-            the latest write weighting for the memory
-
-        Returns: Tensor (batch_size, words_num)
-            the updated precedence vector
-        """
-
-        reset_factor = 1 - tf.reduce_sum(write_weighting, 1, keep_dims=True)
-        updated_precedence_vector = reset_factor * self.precedence_vector + write_weighting
-
-        return updated_precedence_vector
-
-
-    def update_link_matrix(self, write_weighting):
-        """
-        updates and returns the temporal link matrix gievn for the latest write
-
-        Parameters:
-        ----------
-        write_weighting: Tensor (batch_size, words_num)
-            the latest write_weighting for the memorye
-
-        Returns: Tensor (batch_size, words_num, words_num)
-            the updated temporal link matrix
-        """
-
-        write_weighting = tf.expand_dims(write_weighting, 2)
-        precedence_vector = tf.expand_dims(self.precedence_vector, 1)
-
-        reset_factor = 1 - utility.pairwise_add(write_weighting, is_batch=True)
-        updated_link_matrix = reset_factor * self.link_matrix + tf.batch_matmul(write_weighting, precedence_vector)
-        updated_link_matrix = (1 - self.I) * updated_link_matrix  # eliminates self-links
-
-        return updated_link_matrix
-
-
-    def get_directional_weightings(self, link_matrix):
-        """
-        computes and returns the forward and backward reading weightings
-
-        Parameters:
-        ----------
-        link_matrix: Tensor (batch_size, words_num, words_num)
-            the temporal link matrix
-
-        Returns: Tuple
-            forward weighting: Tensor (batch_size, words_num, read_heads),
-            backward weighting: Tensor (batch_size, words_num, read_heads)
-        """
-
-        forward_weighting = tf.batch_matmul(link_matrix, self.read_weightings)
-        backward_weighting = tf.batch_matmul(link_matrix, self.read_weightings, adj_x=True)
-
-        return forward_weighting, backward_weighting
-
-
-    def update_read_weightings(self, lookup_weightings, forward_weighting, backward_weighting, read_mode):
-        """
-        updates and returns the current read_weightings
-
-        Parameters:
-        ----------
-        lookup_weightings: Tensor (batch_size, words_num, read_heads)
-            the content-based read weighting
-        forward_weighting: Tensor (batch_size, words_num, read_heads)
-            the forward direction read weighting
-        backward_weighting: Tensor (batch_size, words_num, read_heads)
-            the backward direction read weighting
-        read_mode: Tesnor (batch_size, 3, read_heads)
-            the softmax distribution between the three read modes
-
-        Returns: Tensor (batch_size, words_num, read_heads)
-        """
-
-        backward_mode = tf.expand_dims(read_mode[:, 0, :], 1) * backward_weighting
-        lookup_mode = tf.expand_dims(read_mode[:, 1, :], 1) * lookup_weightings
-        forward_mode = tf.expand_dims(read_mode[:, 2, :], 1) * forward_weighting
-        updated_read_weightings = backward_mode + lookup_mode + forward_mode
-
-        return updated_read_weightings
-
-
-    def update_read_vectors(self, memory_matrix, read_weightings):
-        """
-        reads, updates, and returns the read vectors of the recently updated memory
-
-        Parameters:
-        ----------
-        memory_matrix: Tensor (batch_size, words_num, word_size)
-            the recently updated memory matrix
-        read_weightings: Tensor (batch_size, words_num, read_heads)
-            the amount of info to read from each memory location by each read head
-
-        Returns: Tensor (word_size, read_heads)
-        """
-
-        updated_read_vectors = tf.batch_matmul(memory_matrix, read_weightings, adj_x=True)
-
-        return updated_read_vectors
-
-
-    def write(self, key, strength, free_gates, allocation_gate, write_gate, write_vector, erase_vector):
-        """
-        defines the complete pipeline of writing to memory gievn the write variables
-
-        Parameters:
-        ----------
-        key: Tensor (batch_size, word_size, 1)
-            the key to query the memory location with
-        strength: (batch_size, 1)
-            the strength of the query key
-        free_gates: Tensor (batch_size, read_heads)
-            the degree to which location at read haeds will be freed
-        allocation_gate: (batch_size, 1)
-            the fraction of writing that is being allocated in a new locatio
-        write_gate: (batch_size, 1)
-            the amount of information to be written to memory
-        write_vector: Tensor (batch_size, word_size)
-            specifications of what to write to memory
-        erase_vector: Tensor(batch_size, word_size)
-            specifications of what to erase from memory
-
-        Returns : Tuple
-            the updated usage vector: Tensor (batch_size, words_num)
-            the updated write_weighting: Tensor(batch_size, words_num)
-            the updated memory_matrix: Tensor (batch_size, words_num, words_size)
-            the updated link matrix: Tensor(batch_size, words_num, words_num)
-            the updated precedence vector: Tensor (batch_size, words_num)
-        """
-
-        lookup_weighting = self.get_lookup_weighting(key, strength)
-        usage_vector = self.update_usage_vector(free_gates)
-
-        sorted_usage, free_list = tf.nn.top_k(-1 * usage_vector, self.words_num)
-        sorted_usage = -1 * sorted_usage
-
-        allocation_weighting = self.get_allocation_weighting(sorted_usage, free_list)
-        write_weighting = self.update_write_weighting(lookup_weighting, allocation_weighting, write_gate, allocation_gate)
-        memory_matrix = self.update_memory(write_weighting, write_vector, erase_vector)
-        link_matrix = self.update_link_matrix(write_weighting)
-        precedence_vector = self.update_precedence_vector(write_weighting)
-
-        return usage_vector, write_weighting, memory_matrix, link_matrix, precedence_vector
-
-
-    def read(self, keys, strengths, link_matrix, read_modes, memory_matrix):
-        """
-        defines the complete pipeline for reading from memory
-
-        Parameters:
-        ----------
-        keys: Tensor (batch_size, word_size, read_heads)
-            the kyes to query the memory locations with
-        strengths: Tensor (batch_size, read_heads)
-            the strength of each read key
-        link_matrix: Tensor (batch_size, words_num, words_num)
-            the updated link matrix from the last writing
-        read_modes: Tensor (batch_size, 3, read_heads)
-            the softmax distribution between the three read modes
-        memory_matrix: Tensor (batch_size, words_num, word_size)
-            the updated memory matrix from the last writing
-
-        Returns: Tuple
-            the updated read_weightings: Tensor(batch_size, words_num, read_heads)
-            the recently read vectors: Tensor (batch_size, word_size, read_heads)
-        """
-
-        lookup_weighting = self.get_lookup_weighting(keys, strengths)
-        forward_weighting, backward_weighting = self.get_directional_weightings(link_matrix)
-        read_weightings = self.update_read_weightings(lookup_weighting, forward_weighting, backward_weighting, read_modes)
-        read_vectors = self.update_read_vectors(memory_matrix, read_weightings)
-
-        return read_weightings, read_vectors

From 1e489dc16e2ec88fdb6fbf50031df161d54e61c7 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 17:00:47 +0100
Subject: [PATCH 17/22] Delete utility.py

---
 tasks/adding/dnc/utility.py | 60 -------------------------------------
 1 file changed, 60 deletions(-)
 delete mode 100644 tasks/adding/dnc/utility.py

diff --git a/tasks/adding/dnc/utility.py b/tasks/adding/dnc/utility.py
deleted file mode 100644
index eee21a39..00000000
--- a/tasks/adding/dnc/utility.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import tensorflow as tf
-import numpy as np
-from tensorflow.python.ops import gen_state_ops
-
-def pairwise_add(u, v=None, is_batch=False):
-    """
-    performs a pairwise summation between vectors (possibly the same)
-
-    Parameters:
-    ----------
-    u: Tensor (n, ) | (n, 1)
-    v: Tensor (n, ) | (n, 1) [optional]
-    is_batch: bool
-        a flag for whether the vectors come in a batch
-        ie.: whether the vectors has a shape of (b,n) or (b,n,1)
-
-    Returns: Tensor (n, n)
-    Raises: ValueError
-    """
-    u_shape = u.get_shape().as_list()
-
-    if len(u_shape) > 2 and not is_batch:
-        raise ValueError("Expected at most 2D tensors, but got %dD" % len(u_shape))
-    if len(u_shape) > 3 and is_batch:
-        raise ValueError("Expected at most 2D tensor batches, but got %dD" % len(u_shape))
-
-    if v is None:
-        v = u
-    else:
-        v_shape = v.get_shape().as_list()
-        if u_shape != v_shape:
-            raise VauleError("Shapes %s and %s do not match" % (u_shape, v_shape))
-
-    n = u_shape[0] if not is_batch else u_shape[1]
-
-    column_u = tf.reshape(u, (-1, 1) if not is_batch else (-1, n, 1))
-    U = tf.concat(1 if not is_batch else 2, [column_u] * n)
-
-    if v is u:
-        return U + tf.transpose(U, None if not is_batch else [0, 2, 1])
-    else:
-        row_v = tf.reshape(v, (1, -1) if not is_batch else (-1, 1, n))
-        V = tf.concat(0 if not is_batch else 1, [row_v] * n)
-
-        return U + V
-
-
-def decaying_softmax(shape, axis):
-    rank = len(shape)
-    max_val = shape[axis]
-
-    weights_vector = np.arange(1, max_val + 1, dtype=np.float32)
-    weights_vector = weights_vector[::-1]
-    weights_vector = np.exp(weights_vector) / np.sum(np.exp(weights_vector))
-
-    container = np.zeros(shape, dtype=np.float32)
-    broadcastable_shape = [1] * rank
-    broadcastable_shape[axis] = max_val
-
-    return container + np.reshape(weights_vector, broadcastable_shape)

From b16f1bc1ff4ac96b32efedce0cdab719a354e27a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sat, 7 Jan 2017 17:01:39 +0100
Subject: [PATCH 18/22] Delete README.md

---
 tasks/adding/checkpoints/README.md | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 tasks/adding/checkpoints/README.md

diff --git a/tasks/adding/checkpoints/README.md b/tasks/adding/checkpoints/README.md
deleted file mode 100644
index 8b137891..00000000
--- a/tasks/adding/checkpoints/README.md
+++ /dev/null
@@ -1 +0,0 @@
-

From 094096992e55a487579015546f382e6518a906ee Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sun, 15 Jan 2017 16:09:49 +0100
Subject: [PATCH 19/22] uploading the v2 version (without tf.resume_sum)

---
 tasks/adding/train_v2.py | 230 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 230 insertions(+)
 create mode 100644 tasks/adding/train_v2.py

diff --git a/tasks/adding/train_v2.py b/tasks/adding/train_v2.py
new file mode 100644
index 00000000..f2751462
--- /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 / 2) 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")

From 34d649c6f6c8d2606bc222c70106a26bd78b38d0 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sun, 15 Jan 2017 16:24:49 +0100
Subject: [PATCH 20/22] Update train_v2.py

---
 tasks/adding/train_v2.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tasks/adding/train_v2.py b/tasks/adding/train_v2.py
index f2751462..50fa84f3 100644
--- a/tasks/adding/train_v2.py
+++ b/tasks/adding/train_v2.py
@@ -153,7 +153,7 @@ def generate_data(length, size):
                 try:
                     llprint("\rIteration %d/%d" % (i, end))
 
-                    # We use for training just (sequence_max_length / 2) examples
+                    # 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)
 
@@ -187,7 +187,7 @@ def generate_data(length, size):
                         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 "Text input:     ", sums_text[:-2] + " = - "
                         print "Target_output", target_output
                         output_ = output_ * weights
                         print "DNC output", output_

From 57b05426592bfb480672de012c2db85849e83d9d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sun, 15 Jan 2017 17:34:28 +0100
Subject: [PATCH 21/22] Create README.md

---
 tasks/argmax/README.md | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 tasks/argmax/README.md

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 @@
+

From 7db6af731deb5e16708caf8ffe24ba3fc4506ec3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Samuel=20Grav=C3=A1n?= <zeta27almacen@gmail.com>
Date: Sun, 15 Jan 2017 21:34:01 +0100
Subject: [PATCH 22/22] first commit

---
 tasks/argmax/feedforward_controller.py |  28 ++++
 tasks/argmax/train_v2.py               | 223 +++++++++++++++++++++++++
 2 files changed, 251 insertions(+)
 create mode 100644 tasks/argmax/feedforward_controller.py
 create mode 100644 tasks/argmax/train_v2.py

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")