diff --git a/results/MNIST_results/heatmap_png/heatmapMNIST_Stochastic_ST_first_conv_binaryfc.png b/results/MNIST_results/heatmap_png/heatmapMNIST_Stochastic_ST_first_conv_binaryfc.png
deleted file mode 100644
index a74f0fd120d2804c132d6f41060ad000b268ffbd..0000000000000000000000000000000000000000
Binary files a/results/MNIST_results/heatmap_png/heatmapMNIST_Stochastic_ST_first_conv_binaryfc.png and /dev/null differ
diff --git a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_NonBinaryNetfc.png b/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_NonBinaryNetfc.png
deleted file mode 100644
index 386d9d7ffd1a84e01a20889557dc73f99b1e9b2f..0000000000000000000000000000000000000000
Binary files a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_NonBinaryNetfc.png and /dev/null differ
diff --git a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_NonBinaryNetfc2.png b/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_NonBinaryNetfc2.png
deleted file mode 100644
index 7cc128b7ce5d94fd0670e0ad9dddbfe51103438c..0000000000000000000000000000000000000000
Binary files a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_NonBinaryNetfc2.png and /dev/null differ
diff --git a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_Stochastic_ST_first_conv_binaryfc.png b/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_Stochastic_ST_first_conv_binaryfc.png
deleted file mode 100644
index e56462adbfa0859344617da78614ae11608dfeca..0000000000000000000000000000000000000000
Binary files a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_Stochastic_ST_first_conv_binaryfc.png and /dev/null differ
diff --git a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_Stochastic_ST_first_conv_binaryfc2.png b/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_Stochastic_ST_first_conv_binaryfc2.png
deleted file mode 100644
index 4af6686bbe89af9da2d1ffd0b4b8d8c866a8749a..0000000000000000000000000000000000000000
Binary files a/results/MNIST_results/heatmap_png/heatmapOmniglot_classif_Stochastic_ST_first_conv_binaryfc2.png and /dev/null differ
diff --git a/utils/models.py b/utils/models.py
index 3f316e14e88e5cc155234b4dd6e5f5ddcaf215d9..344978e28399ef992475e1a630b8d65d42c0a3d5 100644
--- a/utils/models.py
+++ b/utils/models.py
@@ -211,7 +211,7 @@ class NoBinaryNetOmniglotClassification(Net):
     def __init__(self):
         super(NoBinaryNetOmniglotClassification, self).__init__()
 
-        self.layer1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, stride=2)
+        self.layer1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, stride=1)
         self.batchNorm1 = nn.BatchNorm2d(64)
         # self.dropout1 = nn.Dropout(0.5) #50 % probability 
         # self.maxPool1 = nn.MaxPool2d(kernel_size=2, stride=2)
@@ -304,7 +304,7 @@ class BinaryNetOmniglotClassification(Net):
         self.layer3 = nn.Conv2d(128, 256, kernel_size=3, padding=1, stride=2)
         self.batchNorm3 = nn.BatchNorm2d(256)
         # self.dropout3 = nn.Dropout(0.5)
-        #self.maxPool3 = nn.MaxPool2d(kernel_size=2, stride=2) 
+        #self.maxPool3 = nn.MaxPool2d(kernel_size=2, stride=2)
         if self.third_conv_layer:
             if self.mode == 'Deterministic':
                 self.act_layer3 = DeterministicBinaryActivation(estimator=estimator)
diff --git a/visualize/viz.py b/visualize/viz.py
index 591609d2f2b2d452c00cf65c95713e7c21693682..0293883dcd8ec1c72aeba3ed991dfc9138a56d90 100644
--- a/visualize/viz.py
+++ b/visualize/viz.py
@@ -369,6 +369,7 @@ def viz_filters(model):
             plt.show()
 
 
+<<<<<<< HEAD
 def get_activation(name, activation):
     def hook(model, input, output):
         activation[name] = output.detach()
@@ -376,6 +377,8 @@ def get_activation(name, activation):
     return hook
     
     
+=======
+>>>>>>> 5afd9c16b15a644c659fc0bb1142ff4983a49ae9
 def viz_heatmap(model, name_model, loader, index_data=None, save=True):
     activation = {}
     for name, m in model.named_modules():
@@ -434,6 +437,13 @@ def test_predict_few_examples(model, loader):
                      color=("green" if pred_arr[i] == labels_arr[i] else "red"))
 
 
+def get_activation(name, activation):
+    def hook(model, input, output):
+        activation[name] = output.detach()
+
+    return hook
+
+
 def get_train_data():
     return datasets.MNIST('./data', train=True, download=True, transform=transforms.Compose(
         [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]))
@@ -654,6 +664,12 @@ def standardize_and_clip(tensor, MNIST, min_value=0.0, max_value=1.0,
 
 
 def get_region_layer1(image, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w):
+<<<<<<< HEAD
+=======
+    """
+  return region of interest from index (x,y) in image
+  """
+>>>>>>> 5afd9c16b15a644c659fc0bb1142ff4983a49ae9
     # determine pixel high left of region of interest:
     index_col_hl = (ind_x * stride) - padding
     index_raw_hl = (ind_y * stride) - padding
@@ -683,10 +699,20 @@ def get_region_layer1(image, ind_x, ind_y, name, stride, padding, filter_size, l
     if region.shape != (filter_size, filter_size):
         region = cv2.resize(region, (filter_size, filter_size), interpolation=cv2.INTER_AREA)
 
+<<<<<<< HEAD
     return region, begin_col, end_col, begin_raw, end_raw
 
 
 def get_region_layer2(image, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w):
+=======
+    return region
+
+
+def get_region_layer2(image, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w):
+    """
+  return region of interest from index (x,y)
+  """
+>>>>>>> 5afd9c16b15a644c659fc0bb1142ff4983a49ae9
     region_shape = 7
     # determine pixel high left of region of interest:
     index_col_hl = (ind_x * stride) - padding
@@ -739,6 +765,12 @@ def get_filter_layer2():
 
 
 def get_region_layer3(image, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w):
+<<<<<<< HEAD
+=======
+    """
+  return region of interest from index (x,y)
+  """
+>>>>>>> 5afd9c16b15a644c659fc0bb1142ff4983a49ae9
     region_shape = 15
     # determine pixel high left of region of interest:
     index_col_hl = (ind_x * stride) - padding
@@ -807,6 +839,12 @@ def get_filter_layer3():
 
 
 def get_region_layer4(image, ind_x, ind_y, name, stride, padding, filter_size, len_img_h, len_img_w):
+<<<<<<< HEAD
+=======
+    """
+  return region of interest from index (x,y)
+  """
+>>>>>>> 5afd9c16b15a644c659fc0bb1142ff4983a49ae9
     region_shape = 31
     # determine pixel high left of region of interest:
     index_col_hl = (ind_x * stride) - padding
@@ -862,6 +900,44 @@ def get_region_layer4(image, ind_x, ind_y, name, stride, padding, filter_size, l
         region = cv2.resize(region, (region_shape, region_shape), interpolation=cv2.INTER_AREA)
 
     return region
+<<<<<<< HEAD
+
+
+def get_filter_layer4():
+    return np.array(([[1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [2, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 4, 2, 2],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1],
+                      [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1]]))
+
+
+=======
 
 
 def get_filter_layer4():
@@ -898,6 +974,7 @@ def get_filter_layer4():
                       [1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1]]))
 
 
+>>>>>>> 5afd9c16b15a644c659fc0bb1142ff4983a49ae9
 def get_all_regions_max(loader, activations):
     dataiter = iter(loader)
     images, _ = dataiter.next()