update

README.md

@@ -1,45 +1,22 @@
 # Intestelligence: A Pharmacological Neural Network Using Intestine Data
-The source code accompanying the archive article ["Intestelligence: A pharmacological neural network using intestine data" (https://www.biorxiv.org/content/10.1101/2023.04.15.537044v1)](https://www.biorxiv.org/content/10.1101/2023.04.15.537044v1).
-
-#### Description
-This repository contains the implementation of PerceptronOrINN, a three-layer neural network model designed to operate both as a Perceptron and as an Intestine-based Neural Network (INN). The model can be used for various classification tasks and has been specifically optimized for pharmacological applications.
-
-#### Features
-- Customizable activation functions
-- Supports both Perceptron and INN modes
-- Resampling activation functions based on given parameters
-- Demonstrated on MNIST dataset
+This repogitory includes the source code accompanying the article ["Intestelligence: A pharmacological neural network using intestine data" (https://www.biorxiv.org/content/10.1101/2023.04.15.537044v1)](https://www.biorxiv.org/content/10.1101/2023.04.15.537044v1).
 
 #### Installation and Usage
 ``` bash
 # Clone the repository
-$ git clone git@github.com:ywatanabe1989/intestelligence.git
-
-# Navigate to the directory
-$ cd intestelligence
+$ git clone git@gin.g-node.org:/intestelligence/intestelligence.git && cd intestelligence
 
 # Create a Python virtual environment
-$ python -m venv env
-
-# Activate the virtual environment
-$ source env/bin/activate
-
-# Install the required packages
-$ pip install ipython numpy torch torchvision
+$ python -m venv env && source env/bin/activate && pip install -r requirements.txt
 
 # Run the MNIST training script
-$ python train_MNIST.py
+$ python ./scripts/train_MNIST.py --activation_function intestine_simulated # INN
+$ python ./scripts/train_MNIST.py --activation_function sigmoid # Perceptron
 ```
 
 #### File Structure
-PerceptronOrINN.py: Contains the implementation of the PerceptronOrINN model.
-train_MNIST.py: Script for training the PerceptronOrINN model on the MNIST dataset.
-
-#### Dependencies
-Python 3.x
-NumPy
-PyTorch
-torchvision
+- [`./scripts/PerceptronOrINN.py`](./scripts/PerceptronOrINN.py): Contains the definitions of the INN and Perceptron models.
+- [`./scripts/train_MNIST.py`](./scripts/train_MNIST.py): Trains and evaluates INN or Perceptron model on the MNIST dataset.
 
 #### Additional Notes
-To switch between Perceptron and INN modes, you can update the act_str parameter in the model_config dictionary within the train_MNIST.py script.
+

requirements.txt

@@ -0,0 +1,3 @@
+numpy
+torch
+torchvision

PerceptronOrINN.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
-# Time-stamp: "2023-10-16 10:28:24 (ywatanabe)"
+# Time-stamp: "2024-05-03 10:29:37 (ywatanabe)"
 
 import numpy as np
 import torch
@@ -23,6 +23,11 @@ class PerceptronOrINN(nn.Module):
         super().__init__()
         self.config = config
 
+        assert config["act_str"] in [
+            "intestine_simulated",
+            "sigmoid",
+        ], "Error: Unsupported activation function. Only 'intestine_simulated' or 'sigmoid' are available."
+
         # the 1st layer
         self.fc1 = nn.Linear(config["n_fc_in"], config["n_fc_1"])
         self.act_layer_1 = Activation(
@@ -35,7 +40,7 @@ class PerceptronOrINN(nn.Module):
         )
         self.dropout_layer_1 = nn.Dropout(config["d_ratio_1"])
 
-        # the 2nd layer        
+        # the 2nd layer
         self.fc2 = nn.Linear(config["n_fc_1"], config["n_fc_2"])
         self.act_layer_2 = Activation(
             (config["n_fc_2"],),
@@ -45,7 +50,7 @@ class PerceptronOrINN(nn.Module):
             self.config[f"{self.config['act_str']}_beta_1_mean"],
             self.config[f"{self.config['act_str']}_beta_1_var"],
         )
-        
+
         # the last layer
         self.fc3 = nn.Linear(config["n_fc_2"], len(config["LABELS"]))
 
@@ -92,18 +97,26 @@ class Activation(nn.Module):
                 std=self.beta_0_std,
                 size=self.shape,
             ),
-            torch.normal(mean=-self.beta_0_mean, std=self.beta_0_std, size=self.shape),
+            torch.normal(
+                mean=-self.beta_0_mean, std=self.beta_0_std, size=self.shape
+            ),
         )
         self.beta_1 = torch.where(
             mix,
-            torch.normal(mean=self.beta_1_mean, std=self.beta_1_std, size=self.shape),
-            torch.normal(mean=-self.beta_1_mean, std=self.beta_1_std, size=self.shape),
+            torch.normal(
+                mean=self.beta_1_mean, std=self.beta_1_std, size=self.shape
+            ),
+            torch.normal(
+                mean=-self.beta_1_mean, std=self.beta_1_std, size=self.shape
+            ),
         )
 
     def forward(self, x):
         if self.do_resample_act_funcs:
             self.resample()
-        return 1 / (1 + torch.exp(-self.beta_0.type_as(x) * x + self.beta_1.type_as(x)))
+        return 1 / (
+            1 + torch.exp(-self.beta_0.type_as(x) * x + self.beta_1.type_as(x))
+        )
 
 
 if __name__ == "__main__":
@@ -111,7 +124,7 @@ if __name__ == "__main__":
     Xb = torch.rand(batch_size, n_in).cuda()
 
     model_config = {
-        "act_str": "intestine_simulated",  # "sigmoid"
+        "act_str": "intestine_simulated",  # or "sigmoid"
         "do_resample_act_funcs": False,
         "bs": batch_size,
         "n_fc_in": n_in,

scripts/__init__.py

@@ -0,0 +1,3 @@
+#!./env/bin/python3
+# -*- coding: utf-8 -*-
+# Time-stamp: "2024-05-03 10:30:47 (ywatanabe)"

scripts/train_MNIST.py

@@ -0,0 +1,221 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Time-stamp: "2024-05-03 10:33:30 (ywatanabe)"
+
+import sys
+
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+from torchvision import datasets, transforms
+
+sys.path.append(".")
+from scripts.PerceptronOrINN import PerceptronOrINN
+
+
+def set_random_seeds(seed=42):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
+    torch.backends.cudnn.benchmark = False
+    torch.backends.cudnn.deterministic = True
+
+
+def main(act_str):
+    # Initialize config
+    model_config = {
+        "act_str": act_str,
+        "do_resample_act_funcs": False,
+        "bs": 64,
+        "n_fc_in": 784,
+        "n_fc_1": 1000,
+        "n_fc_2": 1000,
+        "d_ratio_1": 0.5,
+        "sigmoid_beta_0_mean": 1,
+        "sigmoid_beta_0_var": 0,
+        "sigmoid_beta_1_mean": 0,
+        "sigmoid_beta_1_var": 0,
+        "intestine_simulated_beta_0_mean": 3.06,
+        "intestine_simulated_beta_0_var": 1.38,
+        "intestine_simulated_beta_1_mean": 0,
+        "intestine_simulated_beta_1_var": 3.23,
+        "LABELS": list(range(10)),
+    }
+
+    # Initialize the model, optimizer, and loss function
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    model = PerceptronOrINN(model_config).to(device)
+    optimizer = optim.SGD(model.parameters(), lr=0.001)
+    criterion = torch.nn.CrossEntropyLoss()
+
+    # Load MNIST Data
+    transform = transforms.Compose(
+        [transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]
+    )
+
+    train_dataset = datasets.MNIST(
+        "./data", train=True, download=True, transform=transform
+    )
+    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
+
+    test_dataset = datasets.MNIST(
+        "./data", train=False, download=True, transform=transform
+    )
+    test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
+
+    # Training Loop
+    for epoch in range(10):
+        for batch_idx, (data, target) in enumerate(train_loader):
+            data, target = data.to(device), target.to(device)
+
+            data = data.view(data.size(0), -1)
+
+            optimizer.zero_grad()
+            output = model(data)
+
+            loss = criterion(output, target)
+            loss.backward()
+
+            optimizer.step()
+
+            if batch_idx % 100 == 0:
+                print(f"Epoch: {epoch} Batch: {batch_idx} Loss: {loss.item()}")
+
+    # Test Loop
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            data = data.view(data.size(0), -1)
+            output = model(data)
+            _, predicted = torch.max(output.data, 1)
+            total += target.size(0)
+            correct += (predicted == target).sum().item()
+
+    print(f"Accuracy: {(100 * correct / total):.2f}%")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(
+        description="Switcher for Intestine-derived Neural Network (INN) or a perceptron"
+    )
+    parser.add_argument(
+        "--activation_function",
+        type=str,
+        default="intestine_simulated",
+        choices=["intestine_simulated", "sigmoid"],
+        help="The type of activation function to use in the model",
+    )
+    args = parser.parse_args()
+
+    # Set the random seed for reproducibility
+    set_random_seeds()
+
+    main(args.activation_function)
+
+    """
+    Epoch: 0 Batch: 0 Loss: 2.337707996368408
+    Epoch: 0 Batch: 100 Loss: 2.3034439086914062
+    Epoch: 0 Batch: 200 Loss: 2.308676242828369
+    Epoch: 0 Batch: 300 Loss: 2.291069269180298
+    Epoch: 0 Batch: 400 Loss: 2.2898669242858887
+    Epoch: 0 Batch: 500 Loss: 2.259193181991577
+    Epoch: 0 Batch: 600 Loss: 2.2262508869171143
+    Epoch: 0 Batch: 700 Loss: 2.2591052055358887
+    Epoch: 0 Batch: 800 Loss: 2.227748155593872
+    Epoch: 0 Batch: 900 Loss: 2.214931011199951
+    Epoch: 1 Batch: 0 Loss: 2.205514669418335
+    Epoch: 1 Batch: 100 Loss: 2.1726245880126953
+    Epoch: 1 Batch: 200 Loss: 2.179299831390381
+    Epoch: 1 Batch: 300 Loss: 2.1493451595306396
+    Epoch: 1 Batch: 400 Loss: 2.181206703186035
+    Epoch: 1 Batch: 500 Loss: 2.165060043334961
+    Epoch: 1 Batch: 600 Loss: 2.1518664360046387
+    Epoch: 1 Batch: 700 Loss: 2.107090950012207
+    Epoch: 1 Batch: 800 Loss: 2.0922796726226807
+    Epoch: 1 Batch: 900 Loss: 2.0883922576904297
+    Epoch: 2 Batch: 0 Loss: 2.067126750946045
+    Epoch: 2 Batch: 100 Loss: 2.0309972763061523
+    Epoch: 2 Batch: 200 Loss: 2.0337514877319336
+    Epoch: 2 Batch: 300 Loss: 2.038759231567383
+    Epoch: 2 Batch: 400 Loss: 1.9987781047821045
+    Epoch: 2 Batch: 500 Loss: 2.0336506366729736
+    Epoch: 2 Batch: 600 Loss: 1.9891915321350098
+    Epoch: 2 Batch: 700 Loss: 1.9620522260665894
+    Epoch: 2 Batch: 800 Loss: 1.9142768383026123
+    Epoch: 2 Batch: 900 Loss: 1.885387659072876
+    Epoch: 3 Batch: 0 Loss: 1.8634549379348755
+    Epoch: 3 Batch: 100 Loss: 1.922452449798584
+    Epoch: 3 Batch: 200 Loss: 1.843092918395996
+    Epoch: 3 Batch: 300 Loss: 1.8004738092422485
+    Epoch: 3 Batch: 400 Loss: 1.781233549118042
+    Epoch: 3 Batch: 500 Loss: 1.7627004384994507
+    Epoch: 3 Batch: 600 Loss: 1.6683433055877686
+    Epoch: 3 Batch: 700 Loss: 1.7323194742202759
+    Epoch: 3 Batch: 800 Loss: 1.638037919998169
+    Epoch: 3 Batch: 900 Loss: 1.6239663362503052
+    Epoch: 4 Batch: 0 Loss: 1.5985149145126343
+    Epoch: 4 Batch: 100 Loss: 1.5736466646194458
+    Epoch: 4 Batch: 200 Loss: 1.5068103075027466
+    Epoch: 4 Batch: 300 Loss: 1.3710484504699707
+    Epoch: 4 Batch: 400 Loss: 1.3616474866867065
+    Epoch: 4 Batch: 500 Loss: 1.401007890701294
+    Epoch: 4 Batch: 600 Loss: 1.426381230354309
+    Epoch: 4 Batch: 700 Loss: 1.313161849975586
+    Epoch: 4 Batch: 800 Loss: 1.3480956554412842
+    Epoch: 4 Batch: 900 Loss: 1.3751581907272339
+    Epoch: 5 Batch: 0 Loss: 1.3140125274658203
+    Epoch: 5 Batch: 100 Loss: 1.1920424699783325
+    Epoch: 5 Batch: 200 Loss: 1.2809444665908813
+    Epoch: 5 Batch: 300 Loss: 1.317400574684143
+    Epoch: 5 Batch: 400 Loss: 1.1676445007324219
+    Epoch: 5 Batch: 500 Loss: 1.2212748527526855
+    Epoch: 5 Batch: 600 Loss: 1.1691396236419678
+    Epoch: 5 Batch: 700 Loss: 1.1782811880111694
+    Epoch: 5 Batch: 800 Loss: 1.243850827217102
+    Epoch: 5 Batch: 900 Loss: 1.0820790529251099
+    Epoch: 6 Batch: 0 Loss: 1.176945686340332
+    Epoch: 6 Batch: 100 Loss: 1.1030327081680298
+    Epoch: 6 Batch: 200 Loss: 1.183580756187439
+    Epoch: 6 Batch: 300 Loss: 1.0883508920669556
+    Epoch: 6 Batch: 400 Loss: 0.9526631832122803
+    Epoch: 6 Batch: 500 Loss: 0.922423243522644
+    Epoch: 6 Batch: 600 Loss: 1.0819437503814697
+    Epoch: 6 Batch: 700 Loss: 0.939717173576355
+    Epoch: 6 Batch: 800 Loss: 1.0133917331695557
+    Epoch: 6 Batch: 900 Loss: 0.9692772030830383
+    Epoch: 7 Batch: 0 Loss: 0.9215019941329956
+    Epoch: 7 Batch: 100 Loss: 0.963954746723175
+    Epoch: 7 Batch: 200 Loss: 0.9186135530471802
+    Epoch: 7 Batch: 300 Loss: 0.8597159385681152
+    Epoch: 7 Batch: 400 Loss: 1.0357908010482788
+    Epoch: 7 Batch: 500 Loss: 0.9571436047554016
+    Epoch: 7 Batch: 600 Loss: 0.9383936524391174
+    Epoch: 7 Batch: 700 Loss: 0.8021243810653687
+    Epoch: 7 Batch: 800 Loss: 0.8582736849784851
+    Epoch: 7 Batch: 900 Loss: 0.8480632901191711
+    Epoch: 8 Batch: 0 Loss: 0.752300500869751
+    Epoch: 8 Batch: 100 Loss: 0.9244712591171265
+    Epoch: 8 Batch: 200 Loss: 0.8200180530548096
+    Epoch: 8 Batch: 300 Loss: 0.8038215041160583
+    Epoch: 8 Batch: 400 Loss: 0.8257690668106079
+    Epoch: 8 Batch: 500 Loss: 0.7846906185150146
+    Epoch: 8 Batch: 600 Loss: 0.6740760207176208
+    Epoch: 8 Batch: 700 Loss: 0.6744505763053894
+    Epoch: 8 Batch: 800 Loss: 0.6676566004753113
+    Epoch: 8 Batch: 900 Loss: 0.6961811184883118
+    Epoch: 9 Batch: 0 Loss: 0.8140008449554443
+    Epoch: 9 Batch: 100 Loss: 0.6493793725967407
+    Epoch: 9 Batch: 200 Loss: 0.6754528880119324
+    Epoch: 9 Batch: 300 Loss: 0.7368165254592896
+    Epoch: 9 Batch: 400 Loss: 0.7969047427177429
+    Epoch: 9 Batch: 500 Loss: 0.6278544664382935
+    Epoch: 9 Batch: 600 Loss: 0.6742461323738098
+    Epoch: 9 Batch: 700 Loss: 0.6166834235191345
+    Epoch: 9 Batch: 800 Loss: 0.7250018119812012
+    Epoch: 9 Batch: 900 Loss: 0.717006266117096
+    Accuracy: 83.51%
+    """

train_MNIST.py

@@ -1,190 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Time-stamp: "2023-10-16 10:43:22 (ywatanabe)"
-
-import torch
-import torch.optim as optim
-from torchvision import datasets, transforms
-from torch.utils.data import DataLoader
-
-def set_random_seeds(seed=42):
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)  # if you are using multi-GPU.
-    torch.backends.cudnn.benchmark = False
-    torch.backends.cudnn.deterministic = True
-
-# Set the random seed for reproducibility
-set_random_seeds()
-
-from PerceptronOrINN import PerceptronOrINN
-
-# Initialize config
-model_config = {
-    "act_str": "intestine_simulated",
-    "do_resample_act_funcs": False,
-    "bs": 64,
-    "n_fc_in": 784,
-    "n_fc_1": 1000,
-    "n_fc_2": 1000,
-    "d_ratio_1": 0.5,
-    "sigmoid_beta_0_mean": 1,
-    "sigmoid_beta_0_var": 0,
-    "sigmoid_beta_1_mean": 0,
-    "sigmoid_beta_1_var": 0,
-    "intestine_simulated_beta_0_mean": 3.06,
-    "intestine_simulated_beta_0_var": 1.38,
-    "intestine_simulated_beta_1_mean": 0,
-    "intestine_simulated_beta_1_var": 3.23,
-    "LABELS": list(range(10))
-}
-
-# Initialize the model, optimizer, and loss function
-model = PerceptronOrINN(model_config).cuda()
-optimizer = optim.SGD(model.parameters(), lr=0.001)
-criterion = torch.nn.CrossEntropyLoss()
-
-# Load MNIST Data
-transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])
-
-train_dataset = datasets.MNIST('./data', train=True, download=True, transform=transform)
-train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
-
-test_dataset = datasets.MNIST('./data', train=False, download=True, transform=transform)
-test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
-
-# Training Loop
-for epoch in range(10):
-    for batch_idx, (data, target) in enumerate(train_loader):
-        data, target = data.cuda(), target.cuda()
-        
-        data = data.view(data.size(0), -1)
-        
-        optimizer.zero_grad()
-        output = model(data)
-        
-        loss = criterion(output, target)
-        loss.backward()
-        
-        optimizer.step()
-        
-        if batch_idx % 100 == 0:
-            print(f"Epoch: {epoch} Batch: {batch_idx} Loss: {loss.item()}")
-
-# Test Loop
-correct = 0
-total = 0
-with torch.no_grad():
-    for data, target in test_loader:
-        data, target = data.cuda(), target.cuda()
-        data = data.view(data.size(0), -1)
-        output = model(data)
-        _, predicted = torch.max(output.data, 1)
-        total += target.size(0)
-        correct += (predicted == target).sum().item()
-
-print(f"Accuracy: {(100 * correct / total):.2f}%")
-
-"""
-Epoch: 0 Batch: 0 Loss: 2.337707996368408
-Epoch: 0 Batch: 100 Loss: 2.3034439086914062
-Epoch: 0 Batch: 200 Loss: 2.308676242828369
-Epoch: 0 Batch: 300 Loss: 2.291069269180298
-Epoch: 0 Batch: 400 Loss: 2.2898669242858887
-Epoch: 0 Batch: 500 Loss: 2.259193181991577
-Epoch: 0 Batch: 600 Loss: 2.2262508869171143
-Epoch: 0 Batch: 700 Loss: 2.2591052055358887
-Epoch: 0 Batch: 800 Loss: 2.227748155593872
-Epoch: 0 Batch: 900 Loss: 2.214931011199951
-Epoch: 1 Batch: 0 Loss: 2.205514669418335
-Epoch: 1 Batch: 100 Loss: 2.1726245880126953
-Epoch: 1 Batch: 200 Loss: 2.179299831390381
-Epoch: 1 Batch: 300 Loss: 2.1493451595306396
-Epoch: 1 Batch: 400 Loss: 2.181206703186035
-Epoch: 1 Batch: 500 Loss: 2.165060043334961
-Epoch: 1 Batch: 600 Loss: 2.1518664360046387
-Epoch: 1 Batch: 700 Loss: 2.107090950012207
-Epoch: 1 Batch: 800 Loss: 2.0922796726226807
-Epoch: 1 Batch: 900 Loss: 2.0883922576904297
-Epoch: 2 Batch: 0 Loss: 2.067126750946045
-Epoch: 2 Batch: 100 Loss: 2.0309972763061523
-Epoch: 2 Batch: 200 Loss: 2.0337514877319336
-Epoch: 2 Batch: 300 Loss: 2.038759231567383
-Epoch: 2 Batch: 400 Loss: 1.9987781047821045
-Epoch: 2 Batch: 500 Loss: 2.0336506366729736
-Epoch: 2 Batch: 600 Loss: 1.9891915321350098
-Epoch: 2 Batch: 700 Loss: 1.9620522260665894
-Epoch: 2 Batch: 800 Loss: 1.9142768383026123
-Epoch: 2 Batch: 900 Loss: 1.885387659072876
-Epoch: 3 Batch: 0 Loss: 1.8634549379348755
-Epoch: 3 Batch: 100 Loss: 1.922452449798584
-Epoch: 3 Batch: 200 Loss: 1.843092918395996
-Epoch: 3 Batch: 300 Loss: 1.8004738092422485
-Epoch: 3 Batch: 400 Loss: 1.781233549118042
-Epoch: 3 Batch: 500 Loss: 1.7627004384994507
-Epoch: 3 Batch: 600 Loss: 1.6683433055877686
-Epoch: 3 Batch: 700 Loss: 1.7323194742202759
-Epoch: 3 Batch: 800 Loss: 1.638037919998169
-Epoch: 3 Batch: 900 Loss: 1.6239663362503052
-Epoch: 4 Batch: 0 Loss: 1.5985149145126343
-Epoch: 4 Batch: 100 Loss: 1.5736466646194458
-Epoch: 4 Batch: 200 Loss: 1.5068103075027466
-Epoch: 4 Batch: 300 Loss: 1.3710484504699707
-Epoch: 4 Batch: 400 Loss: 1.3616474866867065
-Epoch: 4 Batch: 500 Loss: 1.401007890701294
-Epoch: 4 Batch: 600 Loss: 1.426381230354309
-Epoch: 4 Batch: 700 Loss: 1.313161849975586
-Epoch: 4 Batch: 800 Loss: 1.3480956554412842
-Epoch: 4 Batch: 900 Loss: 1.3751581907272339
-Epoch: 5 Batch: 0 Loss: 1.3140125274658203
-Epoch: 5 Batch: 100 Loss: 1.1920424699783325
-Epoch: 5 Batch: 200 Loss: 1.2809444665908813
-Epoch: 5 Batch: 300 Loss: 1.317400574684143
-Epoch: 5 Batch: 400 Loss: 1.1676445007324219
-Epoch: 5 Batch: 500 Loss: 1.2212748527526855
-Epoch: 5 Batch: 600 Loss: 1.1691396236419678
-Epoch: 5 Batch: 700 Loss: 1.1782811880111694
-Epoch: 5 Batch: 800 Loss: 1.243850827217102
-Epoch: 5 Batch: 900 Loss: 1.0820790529251099
-Epoch: 6 Batch: 0 Loss: 1.176945686340332
-Epoch: 6 Batch: 100 Loss: 1.1030327081680298
-Epoch: 6 Batch: 200 Loss: 1.183580756187439
-Epoch: 6 Batch: 300 Loss: 1.0883508920669556
-Epoch: 6 Batch: 400 Loss: 0.9526631832122803
-Epoch: 6 Batch: 500 Loss: 0.922423243522644
-Epoch: 6 Batch: 600 Loss: 1.0819437503814697
-Epoch: 6 Batch: 700 Loss: 0.939717173576355
-Epoch: 6 Batch: 800 Loss: 1.0133917331695557
-Epoch: 6 Batch: 900 Loss: 0.9692772030830383
-Epoch: 7 Batch: 0 Loss: 0.9215019941329956
-Epoch: 7 Batch: 100 Loss: 0.963954746723175
-Epoch: 7 Batch: 200 Loss: 0.9186135530471802
-Epoch: 7 Batch: 300 Loss: 0.8597159385681152
-Epoch: 7 Batch: 400 Loss: 1.0357908010482788
-Epoch: 7 Batch: 500 Loss: 0.9571436047554016
-Epoch: 7 Batch: 600 Loss: 0.9383936524391174
-Epoch: 7 Batch: 700 Loss: 0.8021243810653687
-Epoch: 7 Batch: 800 Loss: 0.8582736849784851
-Epoch: 7 Batch: 900 Loss: 0.8480632901191711
-Epoch: 8 Batch: 0 Loss: 0.752300500869751
-Epoch: 8 Batch: 100 Loss: 0.9244712591171265
-Epoch: 8 Batch: 200 Loss: 0.8200180530548096
-Epoch: 8 Batch: 300 Loss: 0.8038215041160583
-Epoch: 8 Batch: 400 Loss: 0.8257690668106079
-Epoch: 8 Batch: 500 Loss: 0.7846906185150146
-Epoch: 8 Batch: 600 Loss: 0.6740760207176208
-Epoch: 8 Batch: 700 Loss: 0.6744505763053894
-Epoch: 8 Batch: 800 Loss: 0.6676566004753113
-Epoch: 8 Batch: 900 Loss: 0.6961811184883118
-Epoch: 9 Batch: 0 Loss: 0.8140008449554443
-Epoch: 9 Batch: 100 Loss: 0.6493793725967407
-Epoch: 9 Batch: 200 Loss: 0.6754528880119324
-Epoch: 9 Batch: 300 Loss: 0.7368165254592896
-Epoch: 9 Batch: 400 Loss: 0.7969047427177429
-Epoch: 9 Batch: 500 Loss: 0.6278544664382935
-Epoch: 9 Batch: 600 Loss: 0.6742461323738098
-Epoch: 9 Batch: 700 Loss: 0.6166834235191345
-Epoch: 9 Batch: 800 Loss: 0.7250018119812012
-Epoch: 9 Batch: 900 Loss: 0.717006266117096
-Accuracy: 83.51%
-"""