diff --git a/doc/mnist.rst b/doc/mnist.rst
index 38e0e98a10aaefb6c30d5630020a1c88b6bab3b3..a703f0260b93aa84814a05e2eee7c8e85391f238 100644
--- a/doc/mnist.rst
+++ b/doc/mnist.rst
@@ -75,7 +75,7 @@ The CAISAR interpretation language `caisar.mlw
 We will import the relevant theories with the ``use`` keyword.
 As described in :ref:`interpretation`, the ``Vector`` theory provides
 a vector type, a getter (``[]``) operation and a ``valid_index`` predicate
-that determine whether the get operation is within the range of the vector length.
+that determines whether the get operation is within the range of the vector length.
 ``NeuralNetwork`` defines a type and an application function (``@@``).
 We will also need integers and floating point numbers
 to declare and define :math:`\epsilon`. 
diff --git a/examples/mnist/property.why b/examples/mnist/property.why
index 3a98618b79e94a702661efbe6dfe1650d3d0c77b..da1190ca4d67dd97e170b17140e7810df7840bfc 100644
--- a/examples/mnist/property.why
+++ b/examples/mnist/property.why
@@ -34,6 +34,6 @@ theory MNIST
   goal robustness:
     let nn = read_neural_network "nets/MNIST_256_2.onnx" ONNX in
     let dataset = read_dataset "csv/mnist_test.csv" CSV in
-    let eps = (0.375:t) in
+    let eps = (0.0100000000000000002081668171172168513294309377670288085937500000:t) in
     robust nn dataset eps
 end