diff --git a/main/deepfriedConvnetCifar10.py b/main/deepfriedConvnetCifar10.py
new file mode 100644
index 0000000000000000000000000000000000000000..747b4c13d3941fddb7ac0de7d78f2ce8503a3757
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+++ b/main/deepfriedConvnetCifar10.py
@@ -0,0 +1,77 @@
+import tensorflow as tf
+import numpy as np
+from sklearn.preprocessing import LabelBinarizer
+import skluc.mldatasets as dataset
+from fasfood_layer import fast_food
+import matplotlib.pyplot as plt
+
+IMAGE_SIZE = 24
+
+enc = LabelBinarizer()
+cifar = dataset.Cifar10Dataset()
+cifar_d = cifar.load()
+cifar_d = cifar.to_image()
+X_train, Y_train = cifar_d["train"]
+X_test, Y_test = cifar_d["test"]
+
+X_train = np.array(X_train / 255)
+enc.fit(Y_train)
+Y_train = np.array(enc.transform(Y_train))
+X_test = np.array(X_test / 255)
+Y_test = np.array(enc.transform(Y_test))
+
+X_train = X_train.astype(np.float32)
+permut = np.random.permutation(X_train.shape[0])
+val_size = 5000
+X_val = X_train[permut[:val_size]]
+X_train = X_train[permut[val_size:]]
+Y_val = Y_train[permut[:val_size]]
+Y_train = Y_train[permut[val_size:]]
+X_test = X_test.astype(np.float32)
+Y_train = Y_train.astype(np.float32)
+Y_test = Y_test.astype(np.float32)
+
+
+def distorded_inputs(image_tensor):
+    height = IMAGE_SIZE
+
+    width = IMAGE_SIZE
+
+    distorted_image = tf.random_crop(image_tensor, [height, width, 3])
+    distorted_image = tf.image.random_flip_left_right(distorted_image)
+    distorted_image = tf.image.random_brightness(distorted_image,
+                                                 max_delta=63)
+    distorted_image = tf.image.random_contrast(distorted_image,
+                                               lower=0.2, upper=1.8)
+    float_image = tf.image.per_image_standardization(distorted_image)
+    return float_image
+
+
+if __name__ == '__main__':
+    SIGMA = 5.0
+    print("Sigma = {}".format(SIGMA))
+
+    with tf.Graph().as_default():
+        output_dim = Y_train.shape[1]
+        input_dim = X_train.shape[1:]
+
+        x_image = tf.placeholder(tf.float32, shape=[None, *input_dim], name="x_image")
+        y_ = tf.placeholder(tf.float32, shape=[None, output_dim], name="labels")
+        tf.summary.image("cifarimage", x_image, max_outputs=10)
+
+        dist_x_images = distorded_inputs(x_image)
+        tf.summary.image("cifarimagedistorded", dist_x_images, max_outputs=10)
+
+        # out = fast_food(x_image, SIGMA)
+
+        merged_summary = tf.summary.merge_all()
+
+        init = tf.global_variables_initializer()
+        sess = tf.Session()
+
+        summary_writer = tf.summary.FileWriter("cifar")
+        feed_dict = {x_image: X_train[:10], y_: Y_train[:10]}
+        summary = sess.run([merged_summary], feed_dict=feed_dict)
+        summary_writer.add_summary(summary[0])
+        summary_writer.close()
+