Training files now contain activations

scripts/loulou.py

@@ -123,12 +123,13 @@ def train(weights: list, trX: np.ndarray, trY: np.ndarray, teX: np.ndarray, teY:
                     temp_filename = '../trains/temp/' + \
                         filename + '_epoch_' + str(i) + '.npy'
                     temp_filename = os.path.join(path, temp_filename)
-                    utils.save(weights, temp_filename, reduce_output)
+                    utils.save(weights, activations_fn,
+                               temp_filename, reduce_output)
 
     # Save final file
     if filename:
         filename = os.path.join(path, '../trains/' + filename + '.npy')
-        utils.save(weights, filename, reduce_output)
+        utils.save(weights, activations_fn, filename, reduce_output)
 
     return accuracy
 

scripts/run.py

@@ -5,7 +5,6 @@
 import matplotlib.image as image
 
 from loulou import feed_forward
-from activations import relu
 from utils import convertJson
 
 if __name__ == '__main__':
@@ -19,7 +18,9 @@
     #   Loading weights matrix
     filename = sys.argv[1]
     try:
-        weights = np.load(filename)
+        file = np.load(filename)
+        weights = file['weights']
+        activations = file['activations']
     except FileNotFoundError:
         print(
             "Error ! Weights matrix file could not be opened, please check that it exists.")
@@ -35,12 +36,9 @@
         print("Image : ", img)
         exit()
 
-    # TODO pack activations list into `.npy` file
-    activation_fn = [activation_fn.relu]
-
     #   Shaping image onto matrix
     topred = 1 - img.reshape(784, 4).mean(axis=1)
     #   Making prediction
-    prediction = feed_forward(topred, weights, activation_fn)[-1]
+    prediction = feed_forward(topred, weights, activations)[-1]
     #   Printing json output
     print(convertJson(prediction))

scripts/utils.py

@@ -2,8 +2,9 @@
 import json
 
 
-def save(weights: list, filename: str, reduce_output: int) -> None:
-    np.save(filename, weights)
+def save(weights: list, activations_list: list, filename: str, reduce_output: int) -> None:
+    np.save(filename, {'weights': weights,
+                       'activations': activations_list})
     if reduce_output < 2:
         print('Data saved successfully into ', filename)