diff --git a/vanishing_gradient.py b/vanishing_gradient.py
index 91b6ffd..5573246 100644
--- a/vanishing_gradient.py
+++ b/vanishing_gradient.py
@@ -3,6 +3,7 @@
 
 base_path = "C:\\Users\\Andy\\PycharmProjects\\Tensorboard\\"
 
+
 class Model(object):
     def __init__(self, input_size, label_size, activation, num_layers=6,
                  hidden_size=10):
@@ -16,8 +17,10 @@ def __init__(self, input_size, label_size, activation, num_layers=6,
 
     def _model_def(self):
         # create placeholder variables
-        self.input_images = tf.placeholder(tf.float32, shape=[None, self._input_size])
-        self.labels = tf.placeholder(tf.float32, shape=[None, self._label_size])
+        self.input_images = tf.placeholder(
+            tf.float32, shape=[None, self._input_size])
+        self.labels = tf.placeholder(
+            tf.float32, shape=[None, self._label_size])
         # create self._num_layers dense layers as the model
         input = self.input_images
         for i in range(self._num_layers - 1):
@@ -25,11 +28,12 @@ def _model_def(self):
                                     name='layer{}'.format(i+1))
         # don't supply an activation for the final layer - the loss definition will
         # supply softmax activation. This defaults to a linear activation i.e. f(x) = x
-        logits = tf.layers.dense(input, 10, name='layer{}'.format(self._num_layers))
+        logits = tf.layers.dense(
+            input, 10, name='layer{}'.format(self._num_layers))
         # use softmax cross entropy with logits - no need to apply softmax activation to
         # logits
         self.loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits,
-                                                                             labels=self.labels))
+                                                                              labels=self.labels))
         # add the loss to the summary
         tf.summary.scalar('loss', self.loss)
         self._log_gradients(self._num_layers)
@@ -53,7 +57,8 @@ def _log_gradients(self, num_layers):
             mean = tf.reduce_mean(tf.abs(grad))
             tf.summary.scalar('mean_{}'.format(i + 1), mean)
             tf.summary.histogram('histogram_{}'.format(i + 1), grad)
-            tf.summary.histogram('hist_weights_{}'.format(i + 1), grad)
+            tf.summary.histogram('hist_weights_{}'.format(i + 1), weight)
+
 
 def run_training(model, mnist, sub_folder, iterations=2500, batch_size=30):
     with tf.Session() as sess:
@@ -71,6 +76,7 @@ def run_training(model, mnist, sub_folder, iterations=2500, batch_size=30):
                 print("Iteration {} of {}, loss: {:.3f}, train accuracy: "
                       "{:.2f}%".format(i, iterations, l, acc * 100))
 
+
 if __name__ == "__main__":
     scenarios = ["sigmoid", "relu", "leaky_relu"]
     act_funcs = [tf.sigmoid, tf.nn.relu, tf.nn.leaky_relu]
@@ -81,4 +87,4 @@ def run_training(model, mnist, sub_folder, iterations=2500, batch_size=30):
         tf.reset_default_graph()
         print("Running scenario: {}".format(scenarios[i]))
         model = Model(784, 10, act_funcs[i], 6, 10)
-        run_training(model, mnist, scenarios[i])
\ No newline at end of file
+        run_training(model, mnist, scenarios[i])