diff --git a/README.md b/README.md
new file mode 100644
index 0000000..8dd9259
--- /dev/null
+++ b/README.md
@@ -0,0 +1,53 @@
+# ReliableAI 2022 Course Project
+
+This is the project for Reliable and Trustworthy Artificial Intelligence course at ETH Zurich.
+
+## Folder structure
+In the directory `code` you can find several files. 
+File `networks.py` and `resnet.py` contain encodings of fully connected, convolutional and residual neural network architectures as PyTorch classes.
+The architectures extend `nn.Module` object and consist of standard PyTorch layers (e.g. `Linear`, `Flatten`, `ReLU`, `Conv2d`). Please note that first layer of each network performs normalization of the input image. 
+File `verifier.py` contains a template of verifier. Loading of the stored networks and test cases is already implemented in `main` function. If you decide to modify `main` function, please ensure that parsing of the test cases works correctly. Your task is to modify `analyze` function by building upon DeepPoly convex relaxation. Note that provided verifier template is guaranteed to achieve **0** points (by always outputting `not verified`).
+
+In folder `nets` you can find 10 neural networks (3 fully connected, 4 convolutional, and 3 residual). These networks are loaded using PyTorch in `verifier.py`.
+You can find architectures of these networks in `networks.py`.
+Note that for ResNet we prepend Normalization layer after loading the network (see `get_net` function in `verifier.py`).
+Name of each network contains the dataset the network is trained used on, e.g. `net3_cifar10_fc3.pt` is network which receives CIFAR-10 images as inputs.
+In folder `examples` you can find 10 subfolders. Each subfolder is associated with one of the 10 networks. In a subfolder corresponding to a network, you can find 2 example test cases for this network. 
+As explained in the lecture, these test cases **are not** part of the set of test cases which we will use for the final evaluation, and they are only here for you to develop your verifier. 
+
+## Setup instructions
+
+We recommend you to install Python virtual environment to ensure dependencies are same as the ones we will use for evaluation.
+To evaluate your solution, we are going to use Python 3.7.
+You can create virtual environment and install the dependencies using the following commands:
+
+```bash
+$ virtualenv venv --python=python3.7
+$ source venv/bin/activate
+$ pip install -r requirements.txt
+```
+
+## Running the verifier
+
+We will run your verifier from `code` directory using the command:
+
+```bash
+$ python verifier.py --net {net} --spec ../examples/{net}/img{test_idx}_{eps}.txt
+```
+
+In this command, `{net}` is equal to one of the following values (each representing one of the networks we want to verify): `net1, net2, net3, net4, net5, net6, net7, net8, net9, net10`.
+`test_idx` is an integer representing index of the test case, while `eps` is perturbation that verifier should certify in this test case.
+
+To test your verifier, you can run for example:
+
+```bash
+$ python verifier.py --net net1 --spec ../examples/net1/img1_0.0500.txt
+```
+
+To evaluate the verifier on all networks and sample test cases, we provide the evaluation script.
+You can run this script using the following commands:
+
+```bash
+chmod +x evaluate
+./evaluate ../examples
+```
diff --git a/code/evaluate b/code/evaluate
new file mode 100755
index 0000000..34f1e28
--- /dev/null
+++ b/code/evaluate
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+rm $1/res.txt
+for net in {1..10}
+do
+	echo Evaluating network net${net}...
+	for spec in `ls $1/net${net}/`
+	do
+		echo ${spec}
+		res=$(python verifier.py --net net${net} --spec $1/net${net}/${spec})
+		echo net${k}_${net},$spec,$res >> $1/res.txt
+	done
+done
diff --git a/code/networks.py b/code/networks.py
new file mode 100644
index 0000000..8ea6ac9
--- /dev/null
+++ b/code/networks.py
@@ -0,0 +1,129 @@
+import torch
+import torch.nn as nn
+from resnet import ResNet, BasicBlock
+
+class Normalization(nn.Module):
+
+    def __init__(self, device, dataset):
+        super(Normalization, self).__init__()
+        if dataset == 'mnist':
+            self.mean = torch.FloatTensor([0.1307]).view((1, 1, 1, 1)).to(device)
+            self.sigma = torch.FloatTensor([0.3081]).view((1, 1, 1, 1)).to(device)
+        elif dataset == 'cifar10':
+            self.mean = torch.FloatTensor([0.4914, 0.4822, 0.4465]).view((1, 3, 1, 1)).to(device)
+            self.sigma = torch.FloatTensor([0.2023, 0.1994, 0.201]).view((1, 3, 1, 1)).to(device)
+        else:
+            assert False
+
+    def forward(self, x):
+        return (x - self.mean) / self.sigma
+
+
+class FullyConnected(nn.Module):
+
+    def __init__(self, device, dataset, input_size, input_channels, fc_layers, act='relu'):
+        super(FullyConnected, self).__init__()
+
+        layers = [Normalization(device, dataset), nn.Flatten()]
+        prev_fc_size = input_size * input_size * input_channels
+        for i, fc_size in enumerate(fc_layers):
+            layers += [nn.Linear(prev_fc_size, fc_size)]
+            if i + 1 < len(fc_layers):
+                if act == 'relu':
+                    layers += [nn.ReLU()]
+                else:
+                    assert False
+            prev_fc_size = fc_size
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class Conv(nn.Module):
+
+    def __init__(self, device, dataset, input_size, input_channels, conv_layers, fc_layers, n_class=10):
+        super(Conv, self).__init__()
+
+        self.input_size = input_size
+        self.n_class = n_class
+
+        layers = [Normalization(device, dataset)]
+        prev_channels = input_channels
+        img_dim = input_size
+
+        for n_channels, kernel_size, stride, padding in conv_layers:
+            layers += [
+                nn.Conv2d(prev_channels, n_channels, kernel_size, stride=stride, padding=padding),
+                nn.ReLU(),
+            ]
+            prev_channels = n_channels
+            img_dim = img_dim // stride
+        layers += [nn.Flatten()]
+
+        prev_fc_size = prev_channels * img_dim * img_dim
+        for i, fc_size in enumerate(fc_layers):
+            layers += [nn.Linear(prev_fc_size, fc_size)]
+            if i + 1 < len(fc_layers):
+                layers += [nn.ReLU()]
+            prev_fc_size = fc_size
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class NormalizedResnet(nn.Module):
+
+    def __init__(self, device, resnet):
+        super(NormalizedResnet, self).__init__()
+        
+        self.normalization = Normalization(device, 'cifar10')
+        self.resnet = resnet
+
+    def forward(self, x):
+        x = self.normalization(x)
+        x = self.resnet(x)
+        return x
+
+
+def get_net_name(net):
+    net_names = {
+        'net1': 'net1_mnist_fc1.pt',
+        'net2': 'net2_mnist_fc2.pt',
+        'net3': 'net3_cifar10_fc3.pt',
+        'net4': 'net4_mnist_conv1.pt',
+        'net5': 'net5_mnist_conv2.pt',
+        'net6': 'net6_cifar10_conv2.pt',
+        'net7': 'net7_mnist_conv3.pt',
+        'net8': 'net8_cifar10_resnet_2b.pt',
+        'net9': 'net9_cifar10_resnet_2b2_bn.pt',
+        'net10': 'net10_cifar10_resnet_4b.pt',
+    }
+    return net_names[net]
+    
+def get_network(device, net):
+    if net == 'net1':
+        return FullyConnected(device, 'mnist', 28, 1, [50, 10])
+    elif net == 'net2':
+        return FullyConnected(device, 'mnist', 28, 1, [100, 50, 10])
+    elif net == 'net3':
+        return FullyConnected(device, 'cifar10', 32, 3, [100, 100, 10])
+    elif net == 'net4':
+        return Conv(device, 'mnist', 28, 1, [(16, 3, 2, 1)], [100, 10], 10)
+    elif net == 'net5':
+        return Conv(device, 'mnist', 28, 1, [(16, 4, 2, 1), (32, 4, 2, 1)], [100, 10], 10)
+    elif net == 'net6':
+        return Conv(device, 'cifar10', 32, 3, [(16, 4, 2, 1), (32, 4, 2, 1)], [100, 10], 10)
+    elif net == 'net7':
+        return Conv(device, 'mnist', 28, 1, [(16, 4, 2, 1), (64, 4, 2, 1)], [100, 100, 10], 10)
+    elif net == 'net8':
+        return ResNet(BasicBlock, num_stages=1, num_blocks=2, in_planes=8, bn=False, last_layer="dense")
+    elif net == 'net9':
+        return ResNet(
+            BasicBlock, num_stages=2, num_blocks=1, in_planes=16,
+            bn=True, last_layer="dense", stride=[2, 2, 2])
+    elif net == 'net10':
+        return ResNet(BasicBlock, num_stages=2, num_blocks=2, in_planes=8, bn=False, last_layer="dense")
+    assert False
+
diff --git a/code/resnet.py b/code/resnet.py
new file mode 100644
index 0000000..d1c9041
--- /dev/null
+++ b/code/resnet.py
@@ -0,0 +1,222 @@
+import torch
+import torch.nn as nn
+from torch import Tensor
+from typing import List, Optional, Sequence, Tuple, Type
+
+
+class ResidualBlock(nn.Module):
+    def __init__(
+        self,
+        path_a: nn.Sequential,
+        path_b: nn.Sequential,
+    ) -> None:
+        super(ResidualBlock, self).__init__()
+        self.path_a = path_a
+        self.path_b = path_b
+
+    def forward(self, x: Tensor) -> Tensor:
+        out = self.path_a(x) + self.path_b(x)
+        return out
+
+
+class BasicBlock(ResidualBlock, nn.Module):
+    expansion = 1
+
+    in_planes: int
+    planes: int
+    stride: int
+    bn: bool
+    kernel: int
+
+    out_dim: int
+
+    def __init__(
+        self,
+        in_planes: int,
+        planes: int,
+        stride: int = 1,
+        bn: bool = True,
+        kernel: int = 3,
+        in_dim: int = -1,
+    ) -> None:
+        self.in_planes = in_planes
+        self.planes = planes
+        self.stride = stride
+        self.bn = bn
+        self.kernel = kernel
+
+        kernel_size = kernel
+        assert kernel_size in [1, 2, 3], "kernel not supported!"
+        p_1 = 1 if kernel_size > 1 else 0
+        p_2 = 1 if kernel_size > 2 else 0
+
+        layers_b: List[nn.Module] = []
+        layers_b.append(
+            nn.Conv2d(
+                in_planes,
+                planes,
+                kernel_size=kernel_size,
+                stride=stride,
+                padding=p_1,
+                bias=(not bn),
+            )
+        )
+        _, _, in_dim = self._getShapeConv(
+            (in_planes, in_dim, in_dim),
+            (self.in_planes, kernel_size, kernel_size),
+            stride=stride,
+            padding=p_1,
+        )
+
+        if bn:
+            layers_b.append(nn.BatchNorm2d(planes))
+        layers_b.append(nn.ReLU())
+        layers_b.append(
+            nn.Conv2d(
+                planes,
+                self.expansion * planes,
+                kernel_size=kernel_size,
+                stride=1,
+                padding=p_2,
+                bias=(not bn),
+            )
+        )
+        _, _, in_dim = self._getShapeConv(
+            (planes, in_dim, in_dim),
+            (self.in_planes, kernel_size, kernel_size),
+            stride=1,
+            padding=p_2,
+        )
+        if bn:
+            layers_b.append(nn.BatchNorm2d(self.expansion * planes))
+        path_b = nn.Sequential(*layers_b)
+
+        layers_a: List[nn.Module] = [torch.nn.Identity()]
+        if stride != 1 or in_planes != self.expansion * planes:
+            layers_a.append(
+                nn.Conv2d(
+                    in_planes,
+                    self.expansion * planes,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=(not bn),
+                )
+            )
+            if bn:
+                layers_a.append(nn.BatchNorm2d(self.expansion * planes))
+        path_a = nn.Sequential(*layers_a)
+        self.out_dim = in_dim
+        super(BasicBlock, self).__init__(path_a, path_b)
+
+    def _getShapeConv(
+        self,
+        in_shape: Tuple[int, int, int],
+        conv_shape: Tuple[int, ...],
+        stride: int = 1,
+        padding: int = 0,
+    ) -> Tuple[int, int, int]:
+        inChan, inH, inW = in_shape
+        outChan, kH, kW = conv_shape[:3]
+
+        outH = 1 + int((2 * padding + inH - kH) / stride)
+        outW = 1 + int((2 * padding + inW - kW) / stride)
+        return (outChan, outH, outW)
+
+def getShapeConv(
+    in_shape: Tuple[int, int, int],
+    conv_shape: Tuple[int, ...],
+    stride: int = 1,
+    padding: int = 0,
+) -> Tuple[int, int, int]:
+    inChan, inH, inW = in_shape
+    outChan, kH, kW = conv_shape[:3]
+
+    outH = 1 + int((2 * padding + inH - kH) / stride)
+    outW = 1 + int((2 * padding + inW - kW) / stride)
+    return (outChan, outH, outW)
+
+
+class ResNet(nn.Sequential):
+    def __init__(
+        self,
+        block: Type[BasicBlock],
+        in_ch: int = 3,
+        num_stages: int = 1,
+        num_blocks: int = 2,
+        num_classes: int = 10,
+        in_planes: int = 64,
+        bn: bool = True,
+        last_layer: str = "dense",
+        in_dim: int = 32,
+        stride: Optional[Sequence[int]] = None,
+    ):
+        layers: List[nn.Module] = []
+        self.in_planes = in_planes
+        if stride is None:
+            stride = (num_stages + 1) * [2]
+
+        layers.append(
+            nn.Conv2d(
+                in_ch,
+                self.in_planes,
+                kernel_size=3,
+                stride=stride[0],
+                padding=1,
+                bias=not bn,
+            )
+        )
+
+        _, _, in_dim = getShapeConv(
+            (in_ch, in_dim, in_dim), (self.in_planes, 3, 3), stride=stride[0], padding=1
+        )
+
+        if bn:
+            layers.append(nn.BatchNorm2d(self.in_planes))
+
+        layers.append(nn.ReLU())
+
+        for s in stride[1:]:
+            block_layers, in_dim = self._make_layer(
+                block,
+                self.in_planes * 2,
+                num_blocks,
+                stride=s,
+                bn=bn,
+                kernel=3,
+                in_dim=in_dim,
+            )
+            layers.append(block_layers)
+
+        if last_layer == "dense":
+            layers.append(nn.Flatten())
+            layers.append(
+                nn.Linear(self.in_planes * block.expansion * in_dim**2, 100)
+            )
+            layers.append(nn.ReLU())
+            layers.append(nn.Linear(100, num_classes))
+        else:
+            exit("last_layer type not supported!")
+
+        super(ResNet, self).__init__(*layers)
+
+    def _make_layer(
+        self,
+        block: Type[BasicBlock],
+        planes: int,
+        num_layers: int,
+        stride: int,
+        bn: bool,
+        kernel: int,
+        in_dim: int,
+    ) -> Tuple[nn.Sequential, int]:
+        strides = [stride] + [1] * (num_layers - 1)
+        cur_dim: int = in_dim
+        layers: List[nn.Module] = []
+        for stride in strides:
+            layer = block(self.in_planes, planes, stride, bn, kernel, in_dim=cur_dim)
+            layers.append(layer)
+            cur_dim = layer.out_dim
+            layers.append(nn.ReLU())
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers), cur_dim
+
diff --git a/code/verifier.py b/code/verifier.py
new file mode 100644
index 0000000..1d720d8
--- /dev/null
+++ b/code/verifier.py
@@ -0,0 +1,78 @@
+import argparse
+import csv
+import torch
+import torch.nn.functional as F
+from networks import get_network, get_net_name, NormalizedResnet
+
+
+DEVICE = 'cpu'
+DTYPE = torch.float32
+
+def transform_image(pixel_values, input_dim):
+    normalized_pixel_values = torch.tensor([float(p) / 255.0 for p in pixel_values])
+    if len(input_dim) > 1:
+        input_dim_in_hwc = (input_dim[1], input_dim[2], input_dim[0])
+        image_in_hwc = normalized_pixel_values.view(input_dim_in_hwc)
+        image_in_chw = image_in_hwc.permute(2, 0, 1)
+        image = image_in_chw
+    else:
+        image = normalized_pixel_values
+
+    assert (image >= 0).all()
+    assert (image <= 1).all()
+    return image
+
+def get_spec(spec, dataset):
+    input_dim = [1, 28, 28] if dataset == 'mnist' else [3, 32, 32]
+    eps = float(spec[:-4].split('/')[-1].split('_')[-1])
+    test_file = open(spec, "r")
+    test_instances = csv.reader(test_file, delimiter=",")
+    for i, (label, *pixel_values) in enumerate(test_instances):
+        inputs = transform_image(pixel_values, input_dim)
+        inputs = inputs.to(DEVICE).to(dtype=DTYPE)
+        true_label = int(label)
+    inputs = inputs.unsqueeze(0)
+    return inputs, true_label, eps
+
+
+def get_net(net, net_name):
+    net = get_network(DEVICE, net)
+    state_dict = torch.load('../nets/%s' % net_name, map_location=torch.device(DEVICE))
+    if "state_dict" in state_dict.keys():
+        state_dict = state_dict["state_dict"]
+    net.load_state_dict(state_dict)
+    net = net.to(dtype=DTYPE)
+    net.eval()
+    if 'resnet' in net_name:
+        net = NormalizedResnet(DEVICE, net)
+    return net
+
+
+def analyze(net, inputs, eps, true_label):
+    return 0
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Neural network verification using DeepPoly relaxation')
+    parser.add_argument('--net', type=str, required=True, help='Neural network architecture to be verified.')
+    parser.add_argument('--spec', type=str, required=True, help='Test case to verify.')
+    args = parser.parse_args()
+
+    net_name = get_net_name(args.net)
+    dataset = 'mnist' if 'mnist' in net_name else 'cifar10'
+    
+    inputs, true_label, eps = get_spec(args.spec, dataset)
+    net = get_net(args.net, net_name)
+
+    outs = net(inputs)
+    pred_label = outs.max(dim=1)[1].item()
+    assert pred_label == true_label
+
+    if analyze(net, inputs, eps, true_label):
+        print('verified')
+    else:
+        print('not verified')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/examples/gt.txt b/examples/gt.txt
new file mode 100644
index 0000000..7db2b6d
--- /dev/null
+++ b/examples/gt.txt
@@ -0,0 +1,20 @@
+net_1,img1_0.0500.txt,verified
+net_1,img2_0.0550.txt,verified
+net_2,img1_0.2400.txt,verified
+net_2,img2_0.2360.txt,verified
+net_3,img1_0.0065.txt,not verified
+net_3,img2_0.0081.txt,verified
+net_4,img1_0.2240.txt,verified
+net_4,img2_0.2290.txt,not verified
+net_5,img1_0.0260.txt,verified
+net_5,img2_0.0470.txt,verified
+net_6,img1_0.0140.txt,verified
+net_6,img2_0.0147.txt,not verified
+net_7,img1_0.0540.txt,verified
+net_7,img2_0.1440.txt,not verified
+net_8,img1_0.0096.txt,verified
+net_8,img2_0.0100.txt,verified
+net_9,img1_0.0040.txt,verified
+net_9,img2_0.0040.txt,verified
+net_10,img1_0.0014.txt,verified
+net_10,img2_0.0015.txt,not verified
diff --git a/examples/net1/img1_0.0500.txt b/examples/net1/img1_0.0500.txt
new file mode 100644
index 0000000..7de9913
--- /dev/null
+++ b/examples/net1/img1_0.0500.txt
@@ -0,0 +1 @@
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diff --git a/examples/net1/img2_0.0550.txt b/examples/net1/img2_0.0550.txt
new file mode 100644
index 0000000..65fb44f
--- /dev/null
+++ b/examples/net1/img2_0.0550.txt
@@ -0,0 +1 @@
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diff --git a/examples/net10/img1_0.0014.txt b/examples/net10/img1_0.0014.txt
new file mode 100644
index 0000000..29da4c2
--- /dev/null
+++ b/examples/net10/img1_0.0014.txt
@@ -0,0 +1 @@
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diff --git a/examples/net10/img2_0.0015.txt b/examples/net10/img2_0.0015.txt
new file mode 100644
index 0000000..ecf91be
--- /dev/null
+++ b/examples/net10/img2_0.0015.txt
@@ -0,0 +1 @@
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diff --git a/examples/net2/img1_0.2400.txt b/examples/net2/img1_0.2400.txt
new file mode 100644
index 0000000..488b405
--- /dev/null
+++ b/examples/net2/img1_0.2400.txt
@@ -0,0 +1 @@
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diff --git a/examples/net2/img2_0.2360.txt b/examples/net2/img2_0.2360.txt
new file mode 100644
index 0000000..53fa490
--- /dev/null
+++ b/examples/net2/img2_0.2360.txt
@@ -0,0 +1 @@
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diff --git a/examples/net3/img1_0.0065.txt b/examples/net3/img1_0.0065.txt
new file mode 100644
index 0000000..9d70112
--- /dev/null
+++ b/examples/net3/img1_0.0065.txt
@@ -0,0 +1 @@
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diff --git a/examples/net3/img2_0.0081.txt b/examples/net3/img2_0.0081.txt
new file mode 100644
index 0000000..6d44f49
--- /dev/null
+++ b/examples/net3/img2_0.0081.txt
@@ -0,0 +1 @@
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diff --git a/examples/net4/img1_0.2240.txt b/examples/net4/img1_0.2240.txt
new file mode 100644
index 0000000..c7c6c42
--- /dev/null
+++ b/examples/net4/img1_0.2240.txt
@@ -0,0 +1 @@
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diff --git a/examples/net4/img2_0.2290.txt b/examples/net4/img2_0.2290.txt
new file mode 100644
index 0000000..694853f
--- /dev/null
+++ b/examples/net4/img2_0.2290.txt
@@ -0,0 +1 @@
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diff --git a/examples/net5/img1_0.0260.txt b/examples/net5/img1_0.0260.txt
new file mode 100644
index 0000000..cc99051
--- /dev/null
+++ b/examples/net5/img1_0.0260.txt
@@ -0,0 +1 @@
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diff --git a/examples/net5/img2_0.0470.txt b/examples/net5/img2_0.0470.txt
new file mode 100644
index 0000000..cc99051
--- /dev/null
+++ b/examples/net5/img2_0.0470.txt
@@ -0,0 +1 @@
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diff --git a/examples/net6/img1_0.0140.txt b/examples/net6/img1_0.0140.txt
new file mode 100644
index 0000000..9629dd0
--- /dev/null
+++ b/examples/net6/img1_0.0140.txt
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diff --git a/examples/net6/img2_0.0147.txt b/examples/net6/img2_0.0147.txt
new file mode 100644
index 0000000..7721929
--- /dev/null
+++ b/examples/net6/img2_0.0147.txt
@@ -0,0 +1 @@
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diff --git a/examples/net7/img1_0.0540.txt b/examples/net7/img1_0.0540.txt
new file mode 100644
index 0000000..61074ab
--- /dev/null
+++ b/examples/net7/img1_0.0540.txt
@@ -0,0 +1 @@
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diff --git a/examples/net7/img2_0.1440.txt b/examples/net7/img2_0.1440.txt
new file mode 100644
index 0000000..4c5f319
--- /dev/null
+++ b/examples/net7/img2_0.1440.txt
@@ -0,0 +1 @@
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diff --git a/examples/net8/img1_0.0096.txt b/examples/net8/img1_0.0096.txt
new file mode 100644
index 0000000..5eb037e
--- /dev/null
+++ b/examples/net8/img1_0.0096.txt
@@ -0,0 +1 @@
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diff --git a/examples/net8/img2_0.0100.txt b/examples/net8/img2_0.0100.txt
new file mode 100644
index 0000000..cbeecc6
--- /dev/null
+++ b/examples/net8/img2_0.0100.txt
@@ -0,0 +1 @@
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diff --git a/examples/net9/img1_0.0040.txt b/examples/net9/img1_0.0040.txt
new file mode 100644
index 0000000..0843b8d
--- /dev/null
+++ b/examples/net9/img1_0.0040.txt
@@ -0,0 +1 @@
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diff --git a/examples/net9/img2_0.0040.txt b/examples/net9/img2_0.0040.txt
new file mode 100644
index 0000000..7d508b5
--- /dev/null
+++ b/examples/net9/img2_0.0040.txt
@@ -0,0 +1 @@
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diff --git a/nets/net10_cifar10_resnet_4b.pt b/nets/net10_cifar10_resnet_4b.pt
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diff --git a/nets/net3_cifar10_fc3.pt b/nets/net3_cifar10_fc3.pt
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diff --git a/nets/net4_mnist_conv1.pt b/nets/net4_mnist_conv1.pt
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diff --git a/nets/net5_mnist_conv2.pt b/nets/net5_mnist_conv2.pt
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diff --git a/nets/net6_cifar10_conv2.pt b/nets/net6_cifar10_conv2.pt
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diff --git a/nets/net7_mnist_conv3.pt b/nets/net7_mnist_conv3.pt
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diff --git a/nets/net8_cifar10_resnet_2b.pt b/nets/net8_cifar10_resnet_2b.pt
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diff --git a/nets/net9_cifar10_resnet_2b2_bn.pt b/nets/net9_cifar10_resnet_2b2_bn.pt
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diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..51653e6
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+numpy==1.19.5
+torch==1.10.0
+torchvision==0.11.1