diff --git a/circuits/BatchNormalization2D.circom b/circuits/BatchNormalization2D.circom
new file mode 100644
index 0000000..adedd36
--- /dev/null
+++ b/circuits/BatchNormalization2D.circom
@@ -0,0 +1,16 @@
+pragma circom 2.0.3;
+
+template BatchNormalization2D(nRows, nCols, nChannels) {
+    signal input in[nRows][nCols][nChannels];
+    signal input a[nChannels];
+    signal input b[nChannels];
+    signal output out[nRows][nCols][nChannels];
+
+    for (var i=0; i<nRows; i++) {
+        for (var j=0; j<nCols; j++) {
+            for (var k=0; k<nChannels; k++) {
+                out[i][j][k] <== a[k]*in[i][j][k]+b[k];
+            }
+        }
+    }
+}
\ No newline at end of file
diff --git a/models/batchNormalization.ipynb b/models/batchNormalization.ipynb
new file mode 100644
index 0000000..96107c5
--- /dev/null
+++ b/models/batchNormalization.ipynb
@@ -0,0 +1,842 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.layers import Input, BatchNormalization, Dense, Flatten\n",
+    "from tensorflow.keras import Model\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "inputs = Input(shape=(5,5,3))\n",
+    "out = BatchNormalization()(inputs)\n",
+    "out = Flatten()(out)\n",
+    "out = Dense(1)(out)\n",
+    "model = Model(inputs, out)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_1\"\n",
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "input_3 (InputLayer)         [(None, 5, 5, 3)]         0         \n",
+      "_________________________________________________________________\n",
+      "batch_normalization_2 (Batch (None, 5, 5, 3)           12        \n",
+      "_________________________________________________________________\n",
+      "flatten_1 (Flatten)          (None, 75)                0         \n",
+      "_________________________________________________________________\n",
+      "dense_1 (Dense)              (None, 1)                 76        \n",
+      "=================================================================\n",
+      "Total params: 88\n",
+      "Trainable params: 82\n",
+      "Non-trainable params: 6\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[0.19552316, 0.40459235, 0.65069767],\n",
+       "         [0.97024076, 0.81831547, 0.5177182 ],\n",
+       "         [0.05950918, 0.03350272, 0.9602491 ],\n",
+       "         [0.08329085, 0.30977063, 0.72642731],\n",
+       "         [0.54523607, 0.01088976, 0.82204523]],\n",
+       "\n",
+       "        [[0.54181334, 0.16416867, 0.26710512],\n",
+       "         [0.97727194, 0.83483966, 0.24119749],\n",
+       "         [0.31456203, 0.78914841, 0.65570909],\n",
+       "         [0.82076783, 0.80354282, 0.08698502],\n",
+       "         [0.15471074, 0.02844633, 0.95682819]],\n",
+       "\n",
+       "        [[0.98696698, 0.95562821, 0.93463443],\n",
+       "         [0.82143354, 0.56738999, 0.41847729],\n",
+       "         [0.23732813, 0.73090915, 0.52152988],\n",
+       "         [0.66008344, 0.19977572, 0.46451022],\n",
+       "         [0.65939235, 0.70715058, 0.12179132]],\n",
+       "\n",
+       "        [[0.89408023, 0.76606208, 0.89090992],\n",
+       "         [0.07624889, 0.99371095, 0.1459594 ],\n",
+       "         [0.93944538, 0.47966536, 0.06641057],\n",
+       "         [0.48141466, 0.88173281, 0.45451347],\n",
+       "         [0.68325521, 0.81211422, 0.6974409 ]],\n",
+       "\n",
+       "        [[0.45623391, 0.24889916, 0.48213288],\n",
+       "         [0.97917808, 0.44051506, 0.83544472],\n",
+       "         [0.81790414, 0.714095  , 0.71208781],\n",
+       "         [0.33263783, 0.20025252, 0.88928298],\n",
+       "         [0.51657685, 0.06946578, 0.24005295]]],\n",
+       "\n",
+       "\n",
+       "       [[[0.41251204, 0.08574465, 0.12044708],\n",
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+       "\n",
+       "        [[0.51329764, 0.23448758, 0.43125756],\n",
+       "         [0.35955841, 0.62410782, 0.69694018],\n",
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+       "         [0.4564451 , 0.23809092, 0.28503377],\n",
+       "         [0.89275057, 0.13423004, 0.33386003]],\n",
+       "\n",
+       "        [[0.79615467, 0.36188927, 0.55795231],\n",
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+       "         [0.62589457, 0.44257744, 0.2509827 ]],\n",
+       "\n",
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+       "\n",
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+       "         [0.49210416, 0.43450547, 0.13265603]]],\n",
+       "\n",
+       "\n",
+       "       [[[0.03283252, 0.82868096, 0.35256111],\n",
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+       "\n",
+       "        [[0.38891949, 0.29918601, 0.35197725],\n",
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+       "         [0.99480043, 0.90241281, 0.45853315],\n",
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+       "         [0.93221732, 0.75516309, 0.09468144]],\n",
+       "\n",
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+       "         [0.55167307, 0.98773022, 0.79994771]],\n",
+       "\n",
+       "        [[0.99263745, 0.82693783, 0.23376903],\n",
+       "         [0.07539697, 0.05538842, 0.85691306],\n",
+       "         [0.67048213, 0.76165171, 0.20386099],\n",
+       "         [0.73758635, 0.48078414, 0.35278882],\n",
+       "         [0.45800851, 0.42588808, 0.97806156]],\n",
+       "\n",
+       "        [[0.7585081 , 0.31273093, 0.0779818 ],\n",
+       "         [0.70683551, 0.78945448, 0.28707299],\n",
+       "         [0.16351077, 0.82045415, 0.63177139],\n",
+       "         [0.91288109, 0.16592425, 0.2684148 ],\n",
+       "         [0.04510453, 0.06687807, 0.47876222]]],\n",
+       "\n",
+       "\n",
+       "       ...,\n",
+       "\n",
+       "\n",
+       "       [[[0.80388499, 0.8306954 , 0.88533695],\n",
+       "         [0.2544695 , 0.42772413, 0.29397423],\n",
+       "         [0.30088549, 0.07140127, 0.08414232],\n",
+       "         [0.59652497, 0.61840432, 0.09003471],\n",
+       "         [0.30686121, 0.84965596, 0.55377544]],\n",
+       "\n",
+       "        [[0.64575651, 0.78031831, 0.03105214],\n",
+       "         [0.68957908, 0.84591631, 0.32222136],\n",
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+       "         [0.55281667, 0.50330466, 0.8140472 ],\n",
+       "         [0.59172028, 0.32120471, 0.06004827]],\n",
+       "\n",
+       "        [[0.46860565, 0.43696271, 0.91800029],\n",
+       "         [0.79865908, 0.19934814, 0.74944044],\n",
+       "         [0.4385724 , 0.84540006, 0.41441632],\n",
+       "         [0.12826746, 0.92421342, 0.04584626],\n",
+       "         [0.39343792, 0.58411404, 0.87618497]],\n",
+       "\n",
+       "        [[0.70076119, 0.08611802, 0.22301873],\n",
+       "         [0.31479409, 0.54422436, 0.85506774],\n",
+       "         [0.88506506, 0.33433081, 0.15683975],\n",
+       "         [0.54448916, 0.20975331, 0.25313311],\n",
+       "         [0.46810122, 0.50938211, 0.78746   ]],\n",
+       "\n",
+       "        [[0.17822056, 0.409899  , 0.58227314],\n",
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+       "         [0.58634465, 0.19243015, 0.23359699],\n",
+       "         [0.85772102, 0.47205961, 0.87255158]]],\n",
+       "\n",
+       "\n",
+       "       [[[0.54245385, 0.74207376, 0.54623721],\n",
+       "         [0.64837447, 0.39587349, 0.04896531],\n",
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+       "         [0.44277679, 0.83113755, 0.04340859]],\n",
+       "\n",
+       "        [[0.38069219, 0.90538212, 0.19320333],\n",
+       "         [0.01068953, 0.39745318, 0.27597242],\n",
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+       "         [0.88988958, 0.38178294, 0.18909108],\n",
+       "         [0.84478266, 0.83814902, 0.32351588]],\n",
+       "\n",
+       "        [[0.52913815, 0.99913557, 0.40826834],\n",
+       "         [0.36888234, 0.24461337, 0.30100798],\n",
+       "         [0.93609241, 0.91217449, 0.33189497],\n",
+       "         [0.7143837 , 0.75380449, 0.74337091],\n",
+       "         [0.39894018, 0.10044562, 0.85844636]],\n",
+       "\n",
+       "        [[0.79250446, 0.71461032, 0.54259891],\n",
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+       "         [0.27151432, 0.17484271, 0.03369879],\n",
+       "         [0.9199204 , 0.70766018, 0.36239686],\n",
+       "         [0.31023809, 0.67833763, 0.1695362 ]],\n",
+       "\n",
+       "        [[0.99527975, 0.56018757, 0.17711387],\n",
+       "         [0.3569703 , 0.28941656, 0.54529331],\n",
+       "         [0.67249171, 0.03973704, 0.21149269],\n",
+       "         [0.74074145, 0.28847081, 0.67414207],\n",
+       "         [0.33986564, 0.83839466, 0.91125865]]],\n",
+       "\n",
+       "\n",
+       "       [[[0.32102491, 0.84690145, 0.2979887 ],\n",
+       "         [0.55869975, 0.32024372, 0.80889414],\n",
+       "         [0.91025205, 0.69196448, 0.57958605],\n",
+       "         [0.83198663, 0.26188438, 0.76765691],\n",
+       "         [0.44824623, 0.90996026, 0.94743678]],\n",
+       "\n",
+       "        [[0.01253521, 0.42097529, 0.07818629],\n",
+       "         [0.68072037, 0.45587601, 0.50010411],\n",
+       "         [0.73692317, 0.50393005, 0.02089452],\n",
+       "         [0.84774507, 0.9384536 , 0.19898286],\n",
+       "         [0.17313704, 0.24205738, 0.61104063]],\n",
+       "\n",
+       "        [[0.19622547, 0.49347153, 0.74742077],\n",
+       "         [0.44855744, 0.26467912, 0.24315565],\n",
+       "         [0.91279984, 0.3891196 , 0.83430496],\n",
+       "         [0.15561309, 0.98367959, 0.91468684],\n",
+       "         [0.89776262, 0.81107386, 0.01643423]],\n",
+       "\n",
+       "        [[0.39498909, 0.26282065, 0.75595037],\n",
+       "         [0.77790085, 0.89858599, 0.43854238],\n",
+       "         [0.24432012, 0.29300183, 0.51955436],\n",
+       "         [0.96470115, 0.88156727, 0.75932967],\n",
+       "         [0.90743858, 0.24770177, 0.37398297]],\n",
+       "\n",
+       "        [[0.63992795, 0.70556042, 0.57476261],\n",
+       "         [0.83028084, 0.90006127, 0.37940215],\n",
+       "         [0.35120713, 0.20167522, 0.7885152 ],\n",
+       "         [0.76128603, 0.00147126, 0.33921825],\n",
+       "         [0.17960168, 0.73438349, 0.3825673 ]]]])"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train = np.random.rand(100,5,5,3)\n",
+    "X_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.41246058],\n",
+       "       [0.20653047],\n",
+       "       [0.89323387],\n",
+       "       [0.77751859],\n",
+       "       [0.71370253],\n",
+       "       [0.95423612],\n",
+       "       [0.73784913],\n",
+       "       [0.26439079],\n",
+       "       [0.98359435],\n",
+       "       [0.31472127],\n",
+       "       [0.93990584],\n",
+       "       [0.13038553],\n",
+       "       [0.66315094],\n",
+       "       [0.99092294],\n",
+       "       [0.00492079],\n",
+       "       [0.33818935],\n",
+       "       [0.81103904],\n",
+       "       [0.70299052],\n",
+       "       [0.98968483],\n",
+       "       [0.30019187],\n",
+       "       [0.07267281],\n",
+       "       [0.07848536],\n",
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+       "       [0.12276658],\n",
+       "       [0.55228728],\n",
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+       "       [0.0426659 ],\n",
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+       "       [0.3771928 ],\n",
+       "       [0.73206006],\n",
+       "       [0.24224496],\n",
+       "       [0.29938391],\n",
+       "       [0.89107997],\n",
+       "       [0.4720799 ],\n",
+       "       [0.29481523],\n",
+       "       [0.81441205],\n",
+       "       [0.1567971 ],\n",
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+       "       [0.35472884],\n",
+       "       [0.26632959],\n",
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+       "       [0.99142194],\n",
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+       "       [0.77496782],\n",
+       "       [0.262697  ],\n",
+       "       [0.08363569],\n",
+       "       [0.80047577],\n",
+       "       [0.57348913],\n",
+       "       [0.41349442],\n",
+       "       [0.96962925],\n",
+       "       [0.06607914],\n",
+       "       [0.29725151],\n",
+       "       [0.25198772],\n",
+       "       [0.00255906],\n",
+       "       [0.13953033],\n",
+       "       [0.2642294 ],\n",
+       "       [0.97552587],\n",
+       "       [0.28051008],\n",
+       "       [0.59269401],\n",
+       "       [0.58358134],\n",
+       "       [0.35289514],\n",
+       "       [0.45055523],\n",
+       "       [0.9325669 ],\n",
+       "       [0.25847543],\n",
+       "       [0.41978902],\n",
+       "       [0.79561462],\n",
+       "       [0.10705706],\n",
+       "       [0.22216489],\n",
+       "       [0.59989157],\n",
+       "       [0.88503714],\n",
+       "       [0.39226583],\n",
+       "       [0.88642095],\n",
+       "       [0.47593827],\n",
+       "       [0.51311448],\n",
+       "       [0.11776821],\n",
+       "       [0.91050219],\n",
+       "       [0.87378644],\n",
+       "       [0.23428406],\n",
+       "       [0.75091434],\n",
+       "       [0.6610274 ],\n",
+       "       [0.30534909],\n",
+       "       [0.53461862],\n",
+       "       [0.66292414],\n",
+       "       [0.56894196],\n",
+       "       [0.06660922]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_train = np.random.rand(100,1)\n",
+    "y_train"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "4/4 [==============================] - 0s 566us/step - loss: 1.7214\n",
+      "Epoch 2/10\n",
+      "4/4 [==============================] - 0s 660us/step - loss: 1.6521\n",
+      "Epoch 3/10\n",
+      "4/4 [==============================] - 0s 1ms/step - loss: 1.6740\n",
+      "Epoch 4/10\n",
+      "4/4 [==============================] - 0s 729us/step - loss: 1.6128\n",
+      "Epoch 5/10\n",
+      "4/4 [==============================] - 0s 1ms/step - loss: 1.4255\n",
+      "Epoch 6/10\n",
+      "4/4 [==============================] - 0s 585us/step - loss: 1.4899\n",
+      "Epoch 7/10\n",
+      "4/4 [==============================] - 0s 1ms/step - loss: 1.3811\n",
+      "Epoch 8/10\n",
+      "4/4 [==============================] - 0s 673us/step - loss: 1.3549\n",
+      "Epoch 9/10\n",
+      "4/4 [==============================] - 0s 653us/step - loss: 1.3313\n",
+      "Epoch 10/10\n",
+      "4/4 [==============================] - 0s 1ms/step - loss: 1.2854\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<tensorflow.python.keras.callbacks.History at 0x15461dd60>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.compile(\"adam\", \"mse\")\n",
+    "model.fit(X_train, y_train, epochs=10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<tf.Variable 'batch_normalization_2/gamma:0' shape=(3,) dtype=float32, numpy=array([0.9656307, 0.9650906, 0.9646477], dtype=float32)>,\n",
+       " <tf.Variable 'batch_normalization_2/beta:0' shape=(3,) dtype=float32, numpy=array([0.03126225, 0.03251555, 0.0313088 ], dtype=float32)>,\n",
+       " <tf.Variable 'batch_normalization_2/moving_mean:0' shape=(3,) dtype=float32, numpy=array([0.16415757, 0.16638677, 0.16154662], dtype=float32)>,\n",
+       " <tf.Variable 'batch_normalization_2/moving_variance:0' shape=(3,) dtype=float32, numpy=array([0.6964935 , 0.69624424, 0.6967338 ], dtype=float32)>]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.layers[1].weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[0.53603175, 0.16288177, 0.54647377],\n",
+       "         [0.58968269, 0.51633636, 0.09084824],\n",
+       "         [0.58954409, 0.60274177, 0.16628126],\n",
+       "         [0.03036366, 0.25776433, 0.97264483],\n",
+       "         [0.06753911, 0.4969747 , 0.02626603]],\n",
+       "\n",
+       "        [[0.92805506, 0.92962356, 0.94991846],\n",
+       "         [0.40984699, 0.57242913, 0.73624703],\n",
+       "         [0.27120968, 0.30428539, 0.6547197 ],\n",
+       "         [0.6895789 , 0.12203021, 0.56160566],\n",
+       "         [0.35853814, 0.61396961, 0.30326431]],\n",
+       "\n",
+       "        [[0.49895694, 0.26192641, 0.41918769],\n",
+       "         [0.56496371, 0.13934069, 0.77930897],\n",
+       "         [0.9652276 , 0.68000352, 0.59384582],\n",
+       "         [0.18267196, 0.26760574, 0.93864666],\n",
+       "         [0.49916607, 0.63215712, 0.38614211]],\n",
+       "\n",
+       "        [[0.46365438, 0.3845917 , 0.6604073 ],\n",
+       "         [0.59669509, 0.22802217, 0.62536791],\n",
+       "         [0.37852067, 0.51773501, 0.96948045],\n",
+       "         [0.46492378, 0.09701206, 0.90831063],\n",
+       "         [0.31265477, 0.43007139, 0.82608669]],\n",
+       "\n",
+       "        [[0.32252988, 0.28388506, 0.15159293],\n",
+       "         [0.54518128, 0.73664414, 0.27618411],\n",
+       "         [0.41446863, 0.45379391, 0.65724072],\n",
+       "         [0.1670575 , 0.82368301, 0.41525341],\n",
+       "         [0.05091919, 0.78432432, 0.29655634]]]])"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = np.random.rand(1,5,5,3)\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[ 0.4612311 ,  0.02846454,  0.47584018],\n",
+       "         [ 0.5232636 ,  0.4369807 , -0.05033689],\n",
+       "         [ 0.5231033 ,  0.5368464 ,  0.03677658],\n",
+       "         [-0.12343314,  0.13812803,  0.9680018 ],\n",
+       "         [-0.08045009,  0.41460282, -0.12491935]],\n",
+       "\n",
+       "        [[ 0.9144969 ,  0.9146502 ,  0.94175637],\n",
+       "         [ 0.31533363,  0.50181156,  0.69499886],\n",
+       "         [ 0.1550382 ,  0.19189616,  0.6008475 ],\n",
+       "         [ 0.63876563, -0.01875092,  0.4933151 ],\n",
+       "         [ 0.25600925,  0.54982334,  0.19497083]],\n",
+       "\n",
+       "        [[ 0.41836447,  0.14293846,  0.3288444 ],\n",
+       "         [ 0.49468288,  0.00125621,  0.7447288 ],\n",
+       "         [ 0.9574766 ,  0.62614405,  0.53054756],\n",
+       "         [ 0.05266898,  0.14950253,  0.92873925],\n",
+       "         [ 0.41860625,  0.57084405,  0.29068184]],\n",
+       "\n",
+       "        [[ 0.37754688,  0.28471267,  0.6074158 ],\n",
+       "         [ 0.53137136,  0.10375258,  0.5669507 ],\n",
+       "         [ 0.27911344,  0.43859717,  0.96434736],\n",
+       "         [ 0.37901458, -0.04766643,  0.8937058 ],\n",
+       "         [ 0.20295788,  0.33727726,  0.7987498 ]],\n",
+       "\n",
+       "        [[ 0.2143757 ,  0.16831785,  0.01981383],\n",
+       "         [ 0.47181004,  0.69160825,  0.16369738],\n",
+       "         [ 0.32067728,  0.3646953 ,  0.6037588 ],\n",
+       "         [ 0.03461521,  0.7922061 ,  0.32430092],\n",
+       "         [-0.0996664 ,  0.74671614,  0.18722416]]]], dtype=float32)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_model = Model(inputs, model.layers[1].output)\n",
+    "y = new_model.predict(X)\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma = model.layers[1].weights[0].numpy()\n",
+    "beta = model.layers[1].weights[1].numpy()\n",
+    "moving_mean = model.layers[1].weights[2].numpy()\n",
+    "moving_var = model.layers[1].weights[3].numpy()\n",
+    "epsilon = model.layers[1].epsilon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = gamma/(moving_var+epsilon)**.5\n",
+    "b = beta-gamma*moving_mean/(moving_var+epsilon)**.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[-2.03794953e-08,  5.91488761e-09,  1.45154126e-08],\n",
+       "         [ 7.98100520e-08,  1.98810133e-08,  7.91968675e-09],\n",
+       "         [ 4.81973802e-08, -2.20449419e-08, -1.88879183e-09],\n",
+       "         [ 2.64212180e-10,  1.55602272e-08, -7.21641903e-08],\n",
+       "         [-8.82741547e-10, -2.68981404e-08,  1.23617632e-08]],\n",
+       "\n",
+       "        [[ 2.51851505e-08, -1.65522146e-08, -4.00145346e-08],\n",
+       "         [ 2.10715177e-08, -7.90676278e-08, -8.24527382e-08],\n",
+       "         [ 5.33205935e-09, -1.58572262e-08,  1.61795255e-09],\n",
+       "         [ 4.38547865e-10,  3.73349793e-09, -5.56143572e-08],\n",
+       "         [ 3.41888787e-08,  3.81396181e-09,  5.69365846e-09]],\n",
+       "\n",
+       "        [[ 2.33330081e-08,  6.56881610e-09, -1.21710513e-08],\n",
+       "         [-2.89912733e-09,  4.82403046e-09, -1.72175791e-08],\n",
+       "         [ 6.43033263e-08, -4.13130072e-08, -1.50603818e-09],\n",
+       "         [ 7.43607972e-09,  4.41849135e-09,  2.61667081e-08],\n",
+       "         [ 1.46215137e-08, -6.68016564e-08, -4.45506274e-08]],\n",
+       "\n",
+       "        [[ 1.13439502e-08, -1.31488602e-08,  1.62490282e-08],\n",
+       "         [-3.02629793e-08,  4.91692291e-09, -2.13291591e-08],\n",
+       "         [-4.75657103e-09, -4.41433378e-08, -1.26229787e-07],\n",
+       "         [ 9.41011957e-09,  2.70651909e-09, -1.61074364e-08],\n",
+       "         [ 1.15060916e-09,  3.34245048e-09, -5.07417969e-08]],\n",
+       "\n",
+       "        [[ 4.33865668e-09,  4.46010895e-09, -6.16794888e-09],\n",
+       "         [ 3.34336615e-08, -4.74926964e-09, -1.07198432e-08],\n",
+       "         [ 2.74091595e-08,  1.35368101e-08, -5.05023202e-08],\n",
+       "         [ 4.70631774e-09, -2.86037326e-08, -1.74001409e-08],\n",
+       "         [-1.16552118e-08,  3.97495532e-08, -2.19518315e-09]]]])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y-(gamma*(X-moving_mean)/((moving_var+epsilon)**.5)+beta)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[[[-1.61475423e-08,  5.64923888e-09, -2.40047562e-08],\n",
+       "         [ 8.45288702e-08,  8.12395617e-09, -3.95743559e-09],\n",
+       "         [ 5.29149408e-08, -3.66111857e-08, -1.81769167e-08],\n",
+       "         [-9.26127786e-11,  1.22097854e-08, -1.35605033e-07],\n",
+       "         [-9.02211070e-10, -3.80257174e-08,  4.26113381e-09]],\n",
+       "\n",
+       "        [[ 3.29745908e-08, -4.17459400e-08, -1.02126435e-07],\n",
+       "         [ 2.41583839e-08, -9.26483562e-08, -1.32070041e-07],\n",
+       "         [ 7.16083581e-09, -2.07201465e-08, -4.32319784e-08],\n",
+       "         [ 6.06389250e-09,  4.79600271e-09, -9.50193745e-08],\n",
+       "         [ 3.68101333e-08, -1.11173180e-08, -1.86046535e-08]],\n",
+       "\n",
+       "        [[ 2.72285189e-08,  3.08305820e-09, -4.32480702e-08],\n",
+       "         [ 1.59537394e-09,  5.32374209e-09, -6.93529612e-08],\n",
+       "         [ 7.24300957e-08, -5.83911616e-08, -4.27963234e-08],\n",
+       "         [ 8.46140438e-09,  7.48088591e-10, -3.52860654e-08],\n",
+       "         [ 1.85189223e-08, -8.23242431e-08, -7.36952816e-08]],\n",
+       "\n",
+       "        [[ 1.49191015e-08, -2.06226749e-08, -2.89334895e-08],\n",
+       "         [-2.54805258e-08,  2.53344934e-09, -6.44627220e-08],\n",
+       "         [-1.95398131e-09, -5.59458676e-08, -1.89485590e-07],\n",
+       "         [ 1.29967902e-08,  4.58240647e-09, -7.57862879e-08],\n",
+       "         [ 3.35548667e-09, -5.60998636e-09, -1.05612541e-07]],\n",
+       "\n",
+       "        [[ 6.63314770e-09,  2.60438060e-10, -2.15971630e-08],\n",
+       "         [ 3.77486434e-08, -2.36689068e-08, -3.34346205e-08],\n",
+       "         [ 3.05379656e-08,  3.81311405e-09, -9.54996694e-08],\n",
+       "         [ 5.58994609e-09, -5.03531512e-08, -4.82471003e-08],\n",
+       "         [-1.18255018e-08,  1.92797525e-08, -2.61012420e-08]]]])"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y-(a*X+b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1.1562214, 1.1557811, 1.1548455], dtype=float32)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "in_json = {\n",
+    "    \"in\": (X*1000).round().astype(int).flatten().tolist(),\n",
+    "    \"a\": (a*1000).round().astype(int).flatten().tolist(),\n",
+    "    \"b\": (b*1000*1000).round().astype(int).flatten().tolist()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "out_json = {\n",
+    "    \"out\": (y*1000000).round().astype(int).flatten().tolist()\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"batchNormalization_input.json\", \"w\") as f:\n",
+    "    json.dump(in_json, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with open(\"batchNormalization_output.json\", \"w\") as f:\n",
+    "    json.dump(out_json, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 5, 5, 3)"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.6 ('tf24')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "11280bdb37aa6bc5d4cf1e4de756386eb1f9eecd8dcdefa77636dfac7be2370d"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/models/batchNormalization_input.json b/models/batchNormalization_input.json
new file mode 100644
index 0000000..9e9aa19
--- /dev/null
+++ b/models/batchNormalization_input.json
@@ -0,0 +1 @@
+{"in": [536, 163, 546, 590, 516, 91, 590, 603, 166, 30, 258, 973, 68, 497, 26, 928, 930, 950, 410, 572, 736, 271, 304, 655, 690, 122, 562, 359, 614, 303, 499, 262, 419, 565, 139, 779, 965, 680, 594, 183, 268, 939, 499, 632, 386, 464, 385, 660, 597, 228, 625, 379, 518, 969, 465, 97, 908, 313, 430, 826, 323, 284, 152, 545, 737, 276, 414, 454, 657, 167, 824, 415, 51, 784, 297], "a": [1156, 1156, 1155], "b": [-158540, -159791, -155253]}
\ No newline at end of file
diff --git a/models/batchNormalization_output.json b/models/batchNormalization_output.json
new file mode 100644
index 0000000..408a367
--- /dev/null
+++ b/models/batchNormalization_output.json
@@ -0,0 +1 @@
+{"out": [461231, 28465, 475840, 523264, 436981, -50337, 523103, 536846, 36777, -123433, 138128, 968002, -80450, 414603, -124919, 914497, 914650, 941756, 315334, 501812, 694999, 155038, 191896, 600847, 638766, -18751, 493315, 256009, 549823, 194971, 418364, 142938, 328844, 494683, 1256, 744729, 957477, 626144, 530548, 52669, 149503, 928739, 418606, 570844, 290682, 377547, 284713, 607416, 531371, 103753, 566951, 279113, 438597, 964347, 379015, -47666, 893706, 202958, 337277, 798750, 214376, 168318, 19814, 471810, 691608, 163697, 320677, 364695, 603759, 34615, 792206, 324301, -99666, 746716, 187224]}
\ No newline at end of file
diff --git a/test/BatchNormalization.js b/test/BatchNormalization.js
new file mode 100644
index 0000000..c149746
--- /dev/null
+++ b/test/BatchNormalization.js
@@ -0,0 +1,48 @@
+const chai = require("chai");
+const { Console } = require("console");
+const path = require("path");
+
+const wasm_tester = require("circom_tester").wasm;
+
+const F1Field = require("ffjavascript").F1Field;
+const Scalar = require("ffjavascript").Scalar;
+exports.p = Scalar.fromString("21888242871839275222246405745257275088548364400416034343698204186575808495617");
+const Fr = new F1Field(exports.p);
+
+const assert = chai.assert;
+
+
+
+describe("BatchNormalization layer test", function () {
+    this.timeout(100000000);
+
+    it("(5,5,3) -> (5,5,3)", async () => {
+        let json = require("../models/batchNormalization_input.json");
+        let OUTPUT = require("../models/batchNormalization_output.json");
+
+        const circuit = await wasm_tester(path.join(__dirname, "circuits", "batchNormalization_test.circom"));
+
+        const a = [];
+        const b = [];
+
+        for (var i=0; i<json.a.length; i++) {
+            a.push(Fr.e(json.a[i]));
+            b.push(Fr.e(json.b[i]));
+        }
+
+        const INPUT = {
+            "in": json.in,
+            "a": a,
+            "b": b
+        }
+
+        const witness = await circuit.calculateWitness(INPUT, true);
+
+        assert(Fr.eq(Fr.e(witness[0]),Fr.e(1)));
+
+        for (var i=0; i<5*5*3; i++) {
+            assert((witness[i+1]-Fr.e(OUTPUT.out[i]))<Fr.e(1000));
+            assert((Fr.e(OUTPUT.out[i])-witness[i+1])<Fr.e(1000));
+        }
+    });
+});
\ No newline at end of file
diff --git a/test/circuits/BatchNormalization_test.circom b/test/circuits/BatchNormalization_test.circom
new file mode 100644
index 0000000..482e1ce
--- /dev/null
+++ b/test/circuits/BatchNormalization_test.circom
@@ -0,0 +1,5 @@
+pragma circom 2.0.3;
+
+include "../../circuits/BatchNormalization2D.circom";
+
+component main = BatchNormalization2D(5,5,3);
\ No newline at end of file