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config.json

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+{
+  "architectures": [
+    "ArabiCharModelForImageClassification"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_arabichar.ArabiCharModelConfig",
+    "AutoModelForImageClassification": "modeling_arabichar.ArabiCharModelForImageClassification"
+  },
+  "conv1_channels": 32,
+  "conv2_channels": 64,
+  "dropout_prob": 0.4,
+  "fc1_units": 128,
+  "num_classes": 28,
+  "torch_dtype": "float32",
+  "transformers_version": "4.32.1"
+}

configuration_arabichar.py

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+from transformers import PretrainedConfig
+
+class ArabiCharModelConfig(PretrainedConfig):
+    def __init__(
+        self,
+        num_classes=28,
+        conv1_channels=32,
+        conv2_channels=64,
+        fc1_units=128,
+        dropout_prob=0.4,
+        **kwargs
+    ):
+        super().__init__(**kwargs)
+        self.num_classes = num_classes
+        self.conv1_channels = conv1_channels
+        self.conv2_channels = conv2_channels
+        self.fc1_units = fc1_units
+        self.dropout_prob = dropout_prob

modeling_arabichar.py

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+from typing import Any, Mapping
+from .configuration_arabichar import ArabiCharModelConfig
+from transformers import PreTrainedModel
+import torch
+import torch.nn as nn
+
+class ArabiCharModel(nn.Module):
+    def __init__(self, config):
+        super(ArabiCharModel, self).__init__()
+
+        self.conv1 = nn.Conv2d(1, config.conv1_channels, kernel_size=5, padding=4)
+        self.conv2 = nn.Conv2d(config.conv1_channels, config.conv1_channels, kernel_size=5)
+        self.conv3 = nn.Conv2d(config.conv1_channels, config.conv1_channels, kernel_size=5)
+        self.pool1 = nn.MaxPool2d(2)
+        self.bn1 = nn.BatchNorm2d(config.conv1_channels)
+
+        self.conv4 = nn.Conv2d(config.conv1_channels, config.conv2_channels, kernel_size=5, padding=4)
+        self.conv5 = nn.Conv2d(config.conv2_channels, config.conv2_channels, kernel_size=5)
+        self.conv6 = nn.Conv2d(config.conv2_channels, config.conv2_channels, kernel_size=5)
+        self.pool2 = nn.MaxPool2d(2)
+        self.bn2 = nn.BatchNorm2d(config.conv2_channels)
+
+        self.fc1 = nn.Linear(config.conv2_channels * 5 * 5, config.fc1_units)
+        self.fc2 = nn.Linear(config.fc1_units, config.fc1_units)
+        self.dropout = nn.Dropout(config.dropout_prob)
+        self.fc3 = nn.Linear(config.fc1_units, config.num_classes)
+
+    def forward(self, x):
+        x = torch.relu(self.conv1(x))
+        x = torch.relu(self.conv2(x))
+        x = torch.relu(self.conv3(x))
+        x = self.pool1(x)
+        x = self.bn1(x)
+
+        x = torch.relu(self.conv4(x))
+        x = torch.relu(self.conv5(x))
+        x = torch.relu(self.conv6(x))
+        x = self.pool2(x)
+        x = self.bn2(x)
+
+        x = x.view(x.size(0), -1)
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.dropout(x)
+        return torch.softmax(self.fc3(x), dim=1)
+
+class ArabiCharModelForImageClassification(PreTrainedModel):
+    config_class = ArabiCharModelConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ArabiCharModel(config)
+
+    def forward(self, tensor, labels=None):
+        logits = self.model(tensor)
+        if labels is not None:
+            loss = torch.nn.cross_entropy(logits, labels)
+            return {"loss": loss, "logits": logits}
+        
+        return {"logits": logits}
+
+    def load_state_dict(self, model_name):
+        self.model.load_state_dict(torch.load(model_name))
+        

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14729bd70797b50e42399038f0782bae18f0b8fbe732660df74036e1e7cab4cc
+size 2143045