From 70a82a5647f5b6cd7097c3c8236b1f919f1773f0 Mon Sep 17 00:00:00 2001
From: Michele Alberti <michele.alberti@cea.fr>
Date: Thu, 15 Dec 2022 16:59:59 +0100
Subject: [PATCH] [examples][doc] Add example on MNIST dataset verification.

---
 doc/examples.rst                     |   1 +
 doc/mnist.rst                        |   6 ++
 examples/mnist/csv/mnist_test.csv    | 100 +++++++++++++++++++++++++++
 examples/mnist/csv/single_image.csv  |   1 +
 examples/mnist/nets/MNIST_256_2.onnx | Bin 0 -> 1077894 bytes
 examples/mnist/property.why          |  10 +++
 6 files changed, 118 insertions(+)
 create mode 100644 doc/mnist.rst
 create mode 100644 examples/mnist/csv/mnist_test.csv
 create mode 100644 examples/mnist/csv/single_image.csv
 create mode 100644 examples/mnist/nets/MNIST_256_2.onnx
 create mode 100644 examples/mnist/property.why

diff --git a/doc/examples.rst b/doc/examples.rst
index 028911d..cc4ae70 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -12,3 +12,4 @@ WhyML, and the CAISAR execution.
    :caption: Examples:
 
    acas_xu
+   mnist
diff --git a/doc/mnist.rst b/doc/mnist.rst
new file mode 100644
index 0000000..dcdef5f
--- /dev/null
+++ b/doc/mnist.rst
@@ -0,0 +1,6 @@
+.. _mnist:
+
+MNIST dataset in CSV format
+***************************
+
+bla bla
diff --git a/examples/mnist/csv/mnist_test.csv b/examples/mnist/csv/mnist_test.csv
new file mode 100644
index 0000000..3ea4d6f
--- /dev/null
+++ b/examples/mnist/csv/mnist_test.csv
@@ -0,0 +1,100 @@
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diff --git a/examples/mnist/csv/single_image.csv b/examples/mnist/csv/single_image.csv
new file mode 100644
index 0000000..879a801
--- /dev/null
+++ b/examples/mnist/csv/single_image.csv
@@ -0,0 +1 @@
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diff --git a/examples/mnist/property.why b/examples/mnist/property.why
new file mode 100644
index 0000000..f87266d
--- /dev/null
+++ b/examples/mnist/property.why
@@ -0,0 +1,10 @@
+theory MNIST
+  use MNIST_256_2.AsArray as N
+  use ieee_float.Float64
+  use caisar.DataSetClassification
+  use caisar.DataSetProps
+
+  goal P_0_1:
+   let normalized_dataset = min_max_scale true (0.0:t) (1.0:t) dataset in
+   robust N.model normalized_dataset (0.0100000000000000002081668171172168513294309377670288085937500000:t)
+end
-- 
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