Start working on model.py

3 files changed, 163 insertions, 334 deletions

diff --git a/.ipynb_checkpoints/quickstart_tutorial-checkpoint.ipynb b/.ipynb_checkpoints/quickstart_tutorial-checkpoint.ipynb
index 3ccf563..55d4021 100644
--- a/.ipynb_checkpoints/quickstart_tutorial-checkpoint.ipynb
+++ b/.ipynb_checkpoints/quickstart_tutorial-checkpoint.ipynb
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -88,112 +88,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47918efb82854fc7a269ce73230391b0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/26421880 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9abecd52d9144d53bd028f14a2cfd60b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/29515 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "df61f428b0c44a818d2ab0f64420d9b3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/4422102 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "418ca86b3df24c84979a54ca66cebe56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/5148 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n"
-     ]
-    },
-    {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/ta180m/.local/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /build/python-pytorch/src/pytorch-1.9.0-opt/torch/csrc/utils/tensor_numpy.cpp:174.)\n",
+      "/home/ta180m/git/PyTorch/.venv/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
       "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
      ]
     }
@@ -228,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -291,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -377,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -401,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -439,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -476,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -490,78 +388,78 @@
      "text": [
       "Epoch 1\n",
       "-------------------------------\n",
-      "loss: 1.758146  [    0/60000]\n",
-      "loss: 1.820034  [ 6400/60000]\n",
-      "loss: 1.846449  [12800/60000]\n",
-      "loss: 1.975245  [19200/60000]\n",
-      "loss: 1.612495  [25600/60000]\n",
-      "loss: 1.748993  [32000/60000]\n",
-      "loss: 1.628008  [38400/60000]\n",
-      "loss: 1.655061  [44800/60000]\n",
-      "loss: 1.770255  [51200/60000]\n",
-      "loss: 1.654287  [57600/60000]\n",
+      "loss: 2.300270  [    0/60000]\n",
+      "loss: 2.290948  [ 6400/60000]\n",
+      "loss: 2.280627  [12800/60000]\n",
+      "loss: 2.283042  [19200/60000]\n",
+      "loss: 2.268817  [25600/60000]\n",
+      "loss: 2.262104  [32000/60000]\n",
+      "loss: 2.248093  [38400/60000]\n",
+      "loss: 2.233517  [44800/60000]\n",
+      "loss: 2.234299  [51200/60000]\n",
+      "loss: 2.234841  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 37.7%, Avg loss: 1.749445 \n",
+      " Accuracy: 51.1%, Avg loss: 2.221716 \n",
       "\n",
       "Epoch 2\n",
       "-------------------------------\n",
-      "loss: 1.670408  [    0/60000]\n",
-      "loss: 1.743051  [ 6400/60000]\n",
-      "loss: 1.773547  [12800/60000]\n",
-      "loss: 1.924395  [19200/60000]\n",
-      "loss: 1.529726  [25600/60000]\n",
-      "loss: 1.692361  [32000/60000]\n",
-      "loss: 1.559834  [38400/60000]\n",
-      "loss: 1.593531  [44800/60000]\n",
-      "loss: 1.712157  [51200/60000]\n",
-      "loss: 1.605115  [57600/60000]\n",
+      "loss: 2.203925  [    0/60000]\n",
+      "loss: 2.200477  [ 6400/60000]\n",
+      "loss: 2.180246  [12800/60000]\n",
+      "loss: 2.213840  [19200/60000]\n",
+      "loss: 2.155768  [25600/60000]\n",
+      "loss: 2.154045  [32000/60000]\n",
+      "loss: 2.130811  [38400/60000]\n",
+      "loss: 2.101326  [44800/60000]\n",
+      "loss: 2.118105  [51200/60000]\n",
+      "loss: 2.116674  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 38.1%, Avg loss: 1.694516 \n",
+      " Accuracy: 51.0%, Avg loss: 2.093179 \n",
       "\n",
       "Epoch 3\n",
       "-------------------------------\n",
-      "loss: 1.607648  [    0/60000]\n",
-      "loss: 1.684907  [ 6400/60000]\n",
-      "loss: 1.716139  [12800/60000]\n",
-      "loss: 1.888849  [19200/60000]\n",
-      "loss: 1.474264  [25600/60000]\n",
-      "loss: 1.652733  [32000/60000]\n",
-      "loss: 1.514825  [38400/60000]\n",
-      "loss: 1.549373  [44800/60000]\n",
-      "loss: 1.670293  [51200/60000]\n",
-      "loss: 1.571395  [57600/60000]\n",
+      "loss: 2.054594  [    0/60000]\n",
+      "loss: 2.047193  [ 6400/60000]\n",
+      "loss: 2.009665  [12800/60000]\n",
+      "loss: 2.093936  [19200/60000]\n",
+      "loss: 1.965194  [25600/60000]\n",
+      "loss: 1.976750  [32000/60000]\n",
+      "loss: 1.938592  [38400/60000]\n",
+      "loss: 1.889513  [44800/60000]\n",
+      "loss: 1.942611  [51200/60000]\n",
+      "loss: 1.936963  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 39.0%, Avg loss: 1.653676 \n",
+      " Accuracy: 51.2%, Avg loss: 1.900150 \n",
       "\n",
       "Epoch 4\n",
       "-------------------------------\n",
-      "loss: 1.561757  [    0/60000]\n",
-      "loss: 1.640771  [ 6400/60000]\n",
-      "loss: 1.669458  [12800/60000]\n",
-      "loss: 1.862879  [19200/60000]\n",
-      "loss: 1.435348  [25600/60000]\n",
-      "loss: 1.623189  [32000/60000]\n",
-      "loss: 1.482370  [38400/60000]\n",
-      "loss: 1.515045  [44800/60000]\n",
-      "loss: 1.638349  [51200/60000]\n",
-      "loss: 1.545919  [57600/60000]\n",
+      "loss: 1.837173  [    0/60000]\n",
+      "loss: 1.822518  [ 6400/60000]\n",
+      "loss: 1.775139  [12800/60000]\n",
+      "loss: 1.925843  [19200/60000]\n",
+      "loss: 1.731390  [25600/60000]\n",
+      "loss: 1.778743  [32000/60000]\n",
+      "loss: 1.714922  [38400/60000]\n",
+      "loss: 1.670009  [44800/60000]\n",
+      "loss: 1.755909  [51200/60000]\n",
+      "loss: 1.763030  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 39.9%, Avg loss: 1.621615 \n",
+      " Accuracy: 52.5%, Avg loss: 1.711389 \n",
       "\n",
       "Epoch 5\n",
       "-------------------------------\n",
-      "loss: 1.525517  [    0/60000]\n",
-      "loss: 1.604991  [ 6400/60000]\n",
-      "loss: 1.630397  [12800/60000]\n",
-      "loss: 1.841878  [19200/60000]\n",
-      "loss: 1.406707  [25600/60000]\n",
-      "loss: 1.599460  [32000/60000]\n",
-      "loss: 1.456716  [38400/60000]\n",
-      "loss: 1.485950  [44800/60000]\n",
-      "loss: 1.612476  [51200/60000]\n",
-      "loss: 1.525381  [57600/60000]\n",
+      "loss: 1.621799  [    0/60000]\n",
+      "loss: 1.615258  [ 6400/60000]\n",
+      "loss: 1.567131  [12800/60000]\n",
+      "loss: 1.768921  [19200/60000]\n",
+      "loss: 1.539987  [25600/60000]\n",
+      "loss: 1.627408  [32000/60000]\n",
+      "loss: 1.533756  [38400/60000]\n",
+      "loss: 1.510703  [44800/60000]\n",
+      "loss: 1.603583  [51200/60000]\n",
+      "loss: 1.636089  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 40.7%, Avg loss: 1.595456 \n",
+      " Accuracy: 53.2%, Avg loss: 1.568043 \n",
       "\n",
       "Done!\n"
      ]
@@ -725,9 +623,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "PyTorch",
    "language": "python",
-   "name": "python3"
+   "name": "pytorch"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/model.py b/model.py
new file mode 100644
index 0000000..93cd47a
--- /dev/null
+++ b/model.py
@@ -0,0 +1,33 @@
+import torch
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor, Lambda, Compose
+import matplotlib.pyplot as plt
+
+# Download training data from open datasets.
+training_data = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor(),
+)
+
+# Download test data from open datasets.
+test_data = datasets.FashionMNIST(
+    root="data",
+    train=False,
+    download=True,
+    transform=ToTensor(),
+)
+
+batch_size = 64
+
+# Create data loaders.
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+for X, y in test_dataloader:
+    print("Shape of X [N, C, H, W]: ", X.shape)
+    print("Shape of y: ", y.shape, y.dtype)
+    break
\ No newline at end of file
diff --git a/quickstart_tutorial.ipynb b/quickstart_tutorial.ipynb
index 3ccf563..55d4021 100644
--- a/quickstart_tutorial.ipynb
+++ b/quickstart_tutorial.ipynb
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -88,112 +88,10 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to data/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "47918efb82854fc7a269ce73230391b0",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/26421880 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/train-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9abecd52d9144d53bd028f14a2cfd60b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/29515 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/train-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "df61f428b0c44a818d2ab0f64420d9b3",
-       "version_major": 2,
-       "version_minor": 0
-      },
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n",
-      "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "418ca86b3df24c84979a54ca66cebe56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting data/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to data/FashionMNIST/raw\n",
-      "\n"
-     ]
-    },
-    {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/ta180m/.local/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /build/python-pytorch/src/pytorch-1.9.0-opt/torch/csrc/utils/tensor_numpy.cpp:174.)\n",
+      "/home/ta180m/git/PyTorch/.venv/lib/python3.9/site-packages/torchvision/datasets/mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:180.)\n",
       "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"
      ]
     }
@@ -228,7 +126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -291,7 +189,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -377,7 +275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -401,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -439,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -476,7 +374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false,
     "jupyter": {
@@ -490,78 +388,78 @@
      "text": [
       "Epoch 1\n",
       "-------------------------------\n",
-      "loss: 1.758146  [    0/60000]\n",
-      "loss: 1.820034  [ 6400/60000]\n",
-      "loss: 1.846449  [12800/60000]\n",
-      "loss: 1.975245  [19200/60000]\n",
-      "loss: 1.612495  [25600/60000]\n",
-      "loss: 1.748993  [32000/60000]\n",
-      "loss: 1.628008  [38400/60000]\n",
-      "loss: 1.655061  [44800/60000]\n",
-      "loss: 1.770255  [51200/60000]\n",
-      "loss: 1.654287  [57600/60000]\n",
+      "loss: 2.300270  [    0/60000]\n",
+      "loss: 2.290948  [ 6400/60000]\n",
+      "loss: 2.280627  [12800/60000]\n",
+      "loss: 2.283042  [19200/60000]\n",
+      "loss: 2.268817  [25600/60000]\n",
+      "loss: 2.262104  [32000/60000]\n",
+      "loss: 2.248093  [38400/60000]\n",
+      "loss: 2.233517  [44800/60000]\n",
+      "loss: 2.234299  [51200/60000]\n",
+      "loss: 2.234841  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 37.7%, Avg loss: 1.749445 \n",
+      " Accuracy: 51.1%, Avg loss: 2.221716 \n",
       "\n",
       "Epoch 2\n",
       "-------------------------------\n",
-      "loss: 1.670408  [    0/60000]\n",
-      "loss: 1.743051  [ 6400/60000]\n",
-      "loss: 1.773547  [12800/60000]\n",
-      "loss: 1.924395  [19200/60000]\n",
-      "loss: 1.529726  [25600/60000]\n",
-      "loss: 1.692361  [32000/60000]\n",
-      "loss: 1.559834  [38400/60000]\n",
-      "loss: 1.593531  [44800/60000]\n",
-      "loss: 1.712157  [51200/60000]\n",
-      "loss: 1.605115  [57600/60000]\n",
+      "loss: 2.203925  [    0/60000]\n",
+      "loss: 2.200477  [ 6400/60000]\n",
+      "loss: 2.180246  [12800/60000]\n",
+      "loss: 2.213840  [19200/60000]\n",
+      "loss: 2.155768  [25600/60000]\n",
+      "loss: 2.154045  [32000/60000]\n",
+      "loss: 2.130811  [38400/60000]\n",
+      "loss: 2.101326  [44800/60000]\n",
+      "loss: 2.118105  [51200/60000]\n",
+      "loss: 2.116674  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 38.1%, Avg loss: 1.694516 \n",
+      " Accuracy: 51.0%, Avg loss: 2.093179 \n",
       "\n",
       "Epoch 3\n",
       "-------------------------------\n",
-      "loss: 1.607648  [    0/60000]\n",
-      "loss: 1.684907  [ 6400/60000]\n",
-      "loss: 1.716139  [12800/60000]\n",
-      "loss: 1.888849  [19200/60000]\n",
-      "loss: 1.474264  [25600/60000]\n",
-      "loss: 1.652733  [32000/60000]\n",
-      "loss: 1.514825  [38400/60000]\n",
-      "loss: 1.549373  [44800/60000]\n",
-      "loss: 1.670293  [51200/60000]\n",
-      "loss: 1.571395  [57600/60000]\n",
+      "loss: 2.054594  [    0/60000]\n",
+      "loss: 2.047193  [ 6400/60000]\n",
+      "loss: 2.009665  [12800/60000]\n",
+      "loss: 2.093936  [19200/60000]\n",
+      "loss: 1.965194  [25600/60000]\n",
+      "loss: 1.976750  [32000/60000]\n",
+      "loss: 1.938592  [38400/60000]\n",
+      "loss: 1.889513  [44800/60000]\n",
+      "loss: 1.942611  [51200/60000]\n",
+      "loss: 1.936963  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 39.0%, Avg loss: 1.653676 \n",
+      " Accuracy: 51.2%, Avg loss: 1.900150 \n",
       "\n",
       "Epoch 4\n",
       "-------------------------------\n",
-      "loss: 1.561757  [    0/60000]\n",
-      "loss: 1.640771  [ 6400/60000]\n",
-      "loss: 1.669458  [12800/60000]\n",
-      "loss: 1.862879  [19200/60000]\n",
-      "loss: 1.435348  [25600/60000]\n",
-      "loss: 1.623189  [32000/60000]\n",
-      "loss: 1.482370  [38400/60000]\n",
-      "loss: 1.515045  [44800/60000]\n",
-      "loss: 1.638349  [51200/60000]\n",
-      "loss: 1.545919  [57600/60000]\n",
+      "loss: 1.837173  [    0/60000]\n",
+      "loss: 1.822518  [ 6400/60000]\n",
+      "loss: 1.775139  [12800/60000]\n",
+      "loss: 1.925843  [19200/60000]\n",
+      "loss: 1.731390  [25600/60000]\n",
+      "loss: 1.778743  [32000/60000]\n",
+      "loss: 1.714922  [38400/60000]\n",
+      "loss: 1.670009  [44800/60000]\n",
+      "loss: 1.755909  [51200/60000]\n",
+      "loss: 1.763030  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 39.9%, Avg loss: 1.621615 \n",
+      " Accuracy: 52.5%, Avg loss: 1.711389 \n",
       "\n",
       "Epoch 5\n",
       "-------------------------------\n",
-      "loss: 1.525517  [    0/60000]\n",
-      "loss: 1.604991  [ 6400/60000]\n",
-      "loss: 1.630397  [12800/60000]\n",
-      "loss: 1.841878  [19200/60000]\n",
-      "loss: 1.406707  [25600/60000]\n",
-      "loss: 1.599460  [32000/60000]\n",
-      "loss: 1.456716  [38400/60000]\n",
-      "loss: 1.485950  [44800/60000]\n",
-      "loss: 1.612476  [51200/60000]\n",
-      "loss: 1.525381  [57600/60000]\n",
+      "loss: 1.621799  [    0/60000]\n",
+      "loss: 1.615258  [ 6400/60000]\n",
+      "loss: 1.567131  [12800/60000]\n",
+      "loss: 1.768921  [19200/60000]\n",
+      "loss: 1.539987  [25600/60000]\n",
+      "loss: 1.627408  [32000/60000]\n",
+      "loss: 1.533756  [38400/60000]\n",
+      "loss: 1.510703  [44800/60000]\n",
+      "loss: 1.603583  [51200/60000]\n",
+      "loss: 1.636089  [57600/60000]\n",
       "Test Error: \n",
-      " Accuracy: 40.7%, Avg loss: 1.595456 \n",
+      " Accuracy: 53.2%, Avg loss: 1.568043 \n",
       "\n",
       "Done!\n"
      ]
@@ -725,9 +623,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "PyTorch",
    "language": "python",
-   "name": "python3"
+   "name": "pytorch"
   },
   "language_info": {
    "codemirror_mode": {