diff --git a/Network/Model.py b/Network/Model.py
new file mode 100644
index 0000000..bd588e7
--- /dev/null
+++ b/Network/Model.py
@@ -0,0 +1,137 @@
+import torch
+import torch.nn as nn
+
+# Define 1D convolution with kernel size 5
+def conv1d_5(inplanes, outplanes, stride=1):
+    return nn.Conv1d(inplanes, outplanes, kernel_size=5, stride=stride,
+                     padding=2, bias=False)
+
+# Transformer-based self-attention module
+class TransformerLayer(nn.Module):
+    def __init__(self, embed_dim, num_heads, ff_dim, dropout=0.1):
+        super(TransformerLayer, self).__init__()
+        self.attention = nn.MultiheadAttention(embed_dim, num_heads, dropout=dropout, batch_first=True)
+        self.norm1 = nn.LayerNorm(embed_dim)
+        self.ffn = nn.Sequential(
+            nn.Linear(embed_dim, ff_dim),
+            nn.ReLU(),
+            nn.Linear(ff_dim, embed_dim)
+        )
+        self.norm2 = nn.LayerNorm(embed_dim)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # [B, C, L] -> [B, L, C]
+        attn_output, _ = self.attention(x, x, x)
+        x = self.norm1(x + self.dropout(attn_output))
+        
+        ffn_output = self.ffn(x)
+        x = self.norm2(x + self.dropout(ffn_output))
+        
+        x = x.permute(0, 2, 1)  # [B, L, C] -> [B, C, L]
+        return x
+
+# Downsampling Block using ResNet-style skip connections
+class Block(nn.Module):
+    def __init__(self, inplanes, planes, stride=1, downsample=None, bn=False):
+        super(Block, self).__init__()
+        self.bn = bn
+        self.conv1 = conv1d_5(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm1d(planes)
+        self.relu = nn.ReLU(inplace=False)
+        self.conv2 = conv1d_5(planes, planes)
+        self.bn2 = nn.BatchNorm1d(planes)
+        self.downsample = downsample
+
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        if self.bn:
+            out = self.bn1(out)
+        out = self.relu(out)
+        
+        out = self.conv2(out)
+        if self.bn:
+            out = self.bn2(out)
+        out = self.relu(out)
+        
+        if self.downsample is not None:
+            residual = self.downsample(x)
+        
+        out = out + residual
+        out = self.relu(out)
+        return out
+
+# Upsampling Block
+class Decoder_block(nn.Module):
+    def __init__(self, inplanes, outplanes, kernel_size=5, stride=5):
+        super(Decoder_block, self).__init__()
+        self.upsample = nn.ConvTranspose1d(inplanes, outplanes,
+                                           kernel_size=kernel_size, stride=stride, bias=False)
+        self.conv1 = conv1d_5(inplanes, outplanes)
+        self.relu = nn.ReLU(inplace=False)
+        self.conv2 = conv1d_5(outplanes, outplanes)
+
+    def forward(self, x1, x2):
+        x1 = self.upsample(x1)
+        out = torch.cat((x1, x2), dim=1)
+        out = self.conv1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.relu(out)
+        return out
+
+# Main Model
+class Model(nn.Module):
+    def __init__(self, inplanes=1, outplanes=2, layers=[2, 2, 2, 2]):
+        super(Model, self).__init__()
+        self.inplanes = inplanes
+        self.outplanes = outplanes
+        self.encoder1 = self._make_encoder(Block, 32, layers[0], 5)
+        self.encoder2 = self._make_encoder(Block, 64, layers[1], 5)
+        self.encoder3 = self._make_encoder(Block, 128, layers[2], 5)
+        self.encoder4 = self._make_encoder(Block, 256, layers[3], 4)
+        
+        # Self-Attention Layer between Encoder and Decoder
+        self.self_attention = TransformerLayer(embed_dim=256, num_heads=8, ff_dim=512)
+        
+        self.decoder3 = Decoder_block(256, 128, stride=4, kernel_size=4)
+        self.decoder2 = Decoder_block(128, 64)
+        self.decoder1 = Decoder_block(64, 32)
+        self.conv1x1 = nn.ConvTranspose1d(32, outplanes, kernel_size=5, stride=5, bias=False)
+
+    def _make_encoder(self, block, planes, blocks, stride=1):
+        downsample = None
+        if self.inplanes != planes or stride != 1:
+            downsample = nn.Conv1d(self.inplanes, planes, kernel_size=1, stride=stride, bias=False)
+        layers = [block(self.inplanes, planes, stride, downsample)]
+        self.inplanes = planes
+        for _ in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        down1 = self.encoder1(x)
+        down2 = self.encoder2(down1)
+        down3 = self.encoder3(down2)
+        down4 = self.encoder4(down3)
+        
+        # Apply self-attention layer
+        attention_out = self.self_attention(down4)
+        
+        up3 = self.decoder3(attention_out, down3)
+        up2 = self.decoder2(up3, down2)
+        up1 = self.decoder1(up2, down1)
+        out = self.conv1x1(up1)
+        return out
+
+# Test function to verify input-output compatibility
+if __name__ == "__main__":
+    model = Model(inplanes=1, outplanes=1, layers=[3, 3, 3, 3])
+    model.eval()
+    image = torch.randn(1, 1, 1000)
+    with torch.no_grad():
+        output = model(image)
+    print(output.size())
+
+
diff --git a/Network/MyDataset.py b/Network/MyDataset.py
new file mode 100644
index 0000000..2e129a7
--- /dev/null
+++ b/Network/MyDataset.py
@@ -0,0 +1,171 @@
+import os
+import sys
+import torch
+import numpy as np
+import matplotlib.pyplot as plt
+from torch.utils.data import Dataset
+from scipy.ndimage import zoom, gaussian_filter1d
+import torch.nn.functional as F
+
+# Add parent directory to path for config import
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
+from config import Network_train_Config as cfg
+
+data_length=cfg.data_length
+
+def convolve_signals(signal1, signal2):
+    """
+    Perform cyclic convolution using FFT. Output length is max(len(signal1), len(signal2)).
+    """
+    len_signal = max(len(signal1), len(signal2))
+    return np.fft.ifft(np.fft.fft(signal1, len_signal) * np.fft.fft(signal2, len_signal)).real
+
+
+def apply_exponential_gain(data, gain_factor):
+    """
+    Apply exponential gain to a signal.
+    """
+    data = np.asarray(data)
+    indices = np.arange(len(data))
+    gain = np.exp(gain_factor * indices)
+    return data * gain
+
+
+def shift_data_to_end(data, n):
+    """
+    Shift the first n elements of a 1D array to the end.
+    """
+    if n < 0 or n > len(data):
+        raise ValueError("Shift length n must be within data length range.")
+    return np.concatenate((data[n:], data[:n]))
+
+
+def shift_data_to_front(data, n):
+    """
+    Shift the last n elements of a 1D array to the front.
+    """
+    if n < 0 or n > len(data):
+        raise ValueError("Shift length n must be within data length range.")
+    return np.concatenate((data[-n:], data[:-n]))
+
+
+class MyDataset(Dataset):
+    def __init__(self, data_file, label_file, impulse_field_file, impulse_sim_file, mode='train',
+                 check=False, noise_coff=cfg.noise_coff, initial_params=None):
+        super(MyDataset, self).__init__()
+        self.mode = mode
+        self.check = check
+        self.noise_coff = noise_coff
+
+        self.data = np.delete(np.loadtxt(data_file, delimiter=","), [0], axis=0)
+        self.labels = np.delete(np.loadtxt(label_file, delimiter=","), [0], axis=0)
+
+        if self.mode in ['train', 'apply']:
+            self.impulse_field = np.loadtxt(impulse_field_file, delimiter=",")
+            self.impulse_sim = np.loadtxt(impulse_sim_file, delimiter=",")
+        else:
+            raise ValueError("Mode must be either 'train' or 'apply'")
+
+        if self.mode == 'apply':
+            self.initial_model = self.generate_initial_model(*initial_params, total_length=data_length)
+
+        self.data = self.data.T
+        self.labels = self.labels.T
+
+    def __len__(self):
+        return self.data.shape[0]
+
+    def __getitem__(self, index):
+        data_raw = self.data[index]
+        label_data = self.labels[index]
+        impulse_field = self.impulse_field
+        impulse_sim = self.impulse_sim
+
+        data_raw = zoom(data_raw, data_length / len(data_raw))
+        label_data = zoom(label_data, data_length / len(label_data))
+        impulse_field = zoom(impulse_field, data_length / len(impulse_field))
+        impulse_sim = zoom(impulse_sim, data_length / len(impulse_sim))
+
+        if self.mode == 'train':
+            data_gained = apply_exponential_gain(data_raw, 0.00)
+            data_noise1 = np.random.normal(0, self.noise_coff * np.max(abs(data_gained)), size=data_gained.shape)
+            data_noise2 = np.random.normal(0, self.noise_coff * np.max(abs(data_gained)), size=data_gained.shape)
+
+            data_noise1 = convolve_signals(impulse_field, data_noise1)
+            data_noise1 = convolve_signals(impulse_sim, data_noise1)
+
+            data_noise2 = convolve_signals(impulse_sim, data_noise2)
+
+            data_gained = convolve_signals(impulse_field, data_gained)
+            data_gained = shift_data_to_end(data_gained, cfg.shift_distance)
+
+            data_noised = data_gained + self.noise_coff * data_noise1 + self.noise_coff * data_noise2
+            data_data = data_noised
+
+        elif self.mode == 'apply':
+            data_meta = data_raw
+            data_data = convolve_signals(impulse_sim, data_meta)
+            data_data = shift_data_to_end(data_data, cfg.shift_distance)
+            data_data = data_data / np.max(abs(data_data))
+
+        # Construct initial model
+        if self.mode == 'train':
+            initial_model = np.full(data_length, label_data[1])
+        elif self.mode == 'apply':
+            initial_model = self.initial_model
+            initial_model = zoom(initial_model, data_length / len(initial_model))
+            initial_model = gaussian_filter1d(initial_model, sigma=data_length / 5)
+
+        data_data = data_data / np.max(abs(data_data)) * 0.5
+
+        if self.check:
+            plt.figure(figsize=(10, 7))
+            titles = [
+                ('label_data', label_data),
+                ('initial_model', initial_model),
+                ('data_raw', data_raw),
+                ('data_noise1', data_noise1),
+                ('data_noise2', data_noise2),
+                ('impulse_field', impulse_field),
+                ('impulse_sim', impulse_sim),
+                ('data_without_noise', data_gained),
+                ('data_noised', data_data),
+            ]
+            for i, (title, signal) in enumerate(titles):
+                plt.subplot(9, 1, i + 1)
+                plt.plot(signal, label=title, color='blue')
+                plt.grid(alpha=0.3)
+                plt.legend()
+            plt.show()
+
+        # Convert to PyTorch tensors
+        data_data = torch.from_numpy(data_data).float().unsqueeze(0)
+        label_data = torch.from_numpy(label_data).float().unsqueeze(0)
+        initial_model = torch.from_numpy(initial_model).float().unsqueeze(0)
+
+        data_data = data_data + 0.5
+        label_data = label_data / cfg.max_permittivity
+        initial_model = initial_model / cfg.max_permittivity
+
+        input_data = torch.cat((data_data, initial_model), dim=0)
+
+        return input_data, label_data
+
+    def generate_initial_model(self, epsilon, thickness, total_length=data_length):
+        """
+        Generate an initial layered model.
+
+        Parameters:
+        - epsilon: List of permittivity values for each layer.
+        - thickness: Corresponding layer thicknesses.
+        - total_length: Total model length.
+
+        Returns:
+        - A 1D numpy array of length total_length.
+        """
+        layer_points = (np.array(thickness) / np.sum(thickness) * total_length).astype(int)
+        diff = total_length - np.sum(layer_points)
+        layer_points[0] += diff  # Adjust rounding difference
+
+        initial_model = np.concatenate([np.full(points, eps) for eps, points in zip(epsilon, layer_points)])
+        return initial_model
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