diff --git a/examples/train.ipynb b/examples/train.ipynb
index 67d7c2d..b29a29d 100644
--- a/examples/train.ipynb
+++ b/examples/train.ipynb
@@ -26,7 +26,64 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Download train-images-idx3-ubyte.gz from http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to .data/mnist/train-images-idx3-ubyte.gz\n",
+      "Downloading... 100% | [==================================================] | Done !\n",
+      ".data/mnist/mnist-data-py\n",
+      "Extracting... | NOTE: gzip files are not extracted, and moved to .data/mnist/mnist-data-py | Done !\n",
+      "Download train-labels-idx1-ubyte.gz from http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to .data/mnist/train-labels-idx1-ubyte.gz\n",
+      "Downloading... 100% | [==================================================] | Done !\n",
+      ".data/mnist/mnist-data-py\n",
+      "Extracting... | NOTE: gzip files are not extracted, and moved to .data/mnist/mnist-data-py | Done !\n",
+      "Download t10k-labels-idx1-ubyte.gz from http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to .data/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading... 100% | [==================================================] | Done !\n",
+      ".data/mnist/mnist-data-py\n",
+      "Extracting... | NOTE: gzip files are not extracted, and moved to .data/mnist/mnist-data-py | Done !\n",
+      "Download t10k-images-idx3-ubyte.gz from http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to .data/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Downloading... 100% | [==================================================] | Done !\n",
+      ".data/mnist/mnist-data-py\n",
+      "Extracting... | NOTE: gzip files are not extracted, and moved to .data/mnist/mnist-data-py | Done !\n"
+     ]
+    },
+    {
+     "ename": "BadGzipFile",
+     "evalue": "Not a gzipped file (b'<!')",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mBadGzipFile\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[2], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m reshape \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m x: x\u001b[38;5;241m.\u001b[39mreshape(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m784\u001b[39m)\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m# Download the training and testing data\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m train_data, test_data \u001b[38;5;241m=\u001b[39m \u001b[43mnorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mMNIST\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msplits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtransform\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mreshape\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documentos/recreate_pytorch/PyNorch/norch/datasets/mnist.py:75\u001b[0m, in \u001b[0;36mMNIST.splits\u001b[0;34m(cls, root, train_data, train_label, test_data, test_label, **kwargs)\u001b[0m\n\u001b[1;32m     73\u001b[0m test_data \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(path, test_data)\n\u001b[1;32m     74\u001b[0m test_label \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(path, test_label)\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mMNIST\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_label\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m, MNIST(test_data, test_label, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[0;32m~/Documentos/recreate_pytorch/PyNorch/norch/datasets/mnist.py:39\u001b[0m, in \u001b[0;36mMNIST.__init__\u001b[0;34m(self, path_data, path_label, transform)\u001b[0m\n\u001b[1;32m     38\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, path_data, path_label, transform\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[0;32m---> 39\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_load_mnist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheader_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m16\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mreshape((\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m28\u001b[39m, \u001b[38;5;241m28\u001b[39m))\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m transform \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m     41\u001b[0m         data \u001b[38;5;241m=\u001b[39m transform(data)\n",
+      "File \u001b[0;32m~/Documentos/recreate_pytorch/PyNorch/norch/datasets/mnist.py:47\u001b[0m, in \u001b[0;36mMNIST._load_mnist\u001b[0;34m(self, path, header_size)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_load_mnist\u001b[39m(\u001b[38;5;28mself\u001b[39m, path, header_size):\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m gzip\u001b[38;5;241m.\u001b[39mopen(path, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m---> 47\u001b[0m         \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseek\u001b[49m\u001b[43m(\u001b[49m\u001b[43mheader_size\u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Skip the header\u001b[39;00m\n\u001b[1;32m     48\u001b[0m         data \u001b[38;5;241m=\u001b[39m f\u001b[38;5;241m.\u001b[39mread()\n\u001b[1;32m     50\u001b[0m     data_list \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(data)\n",
+      "File \u001b[0;32m/usr/lib/python3.8/gzip.py:384\u001b[0m, in \u001b[0;36mGzipFile.seek\u001b[0;34m(self, offset, whence)\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmode \u001b[38;5;241m==\u001b[39m READ:\n\u001b[1;32m    383\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_not_closed()\n\u001b[0;32m--> 384\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_buffer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mseek\u001b[49m\u001b[43m(\u001b[49m\u001b[43moffset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwhence\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    386\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moffset\n",
+      "File \u001b[0;32m/usr/lib/python3.8/_compression.py:143\u001b[0m, in \u001b[0;36mDecompressReader.seek\u001b[0;34m(self, offset, whence)\u001b[0m\n\u001b[1;32m    141\u001b[0m \u001b[38;5;66;03m# Read and discard data until we reach the desired position.\u001b[39;00m\n\u001b[1;32m    142\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m offset \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 143\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mmin\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDEFAULT_BUFFER_SIZE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moffset\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    144\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m data:\n\u001b[1;32m    145\u001b[0m         \u001b[38;5;28;01mbreak\u001b[39;00m\n",
+      "File \u001b[0;32m/usr/lib/python3.8/gzip.py:479\u001b[0m, in \u001b[0;36m_GzipReader.read\u001b[0;34m(self, size)\u001b[0m\n\u001b[1;32m    475\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_member:\n\u001b[1;32m    476\u001b[0m     \u001b[38;5;66;03m# If the _new_member flag is set, we have to\u001b[39;00m\n\u001b[1;32m    477\u001b[0m     \u001b[38;5;66;03m# jump to the next member, if there is one.\u001b[39;00m\n\u001b[1;32m    478\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_init_read()\n\u001b[0;32m--> 479\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_read_gzip_header\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m    480\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pos\n\u001b[1;32m    481\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "File \u001b[0;32m/usr/lib/python3.8/gzip.py:427\u001b[0m, in \u001b[0;36m_GzipReader._read_gzip_header\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    424\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    426\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m magic \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124mb\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;130;01m\\037\u001b[39;00m\u001b[38;5;130;01m\\213\u001b[39;00m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m--> 427\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m BadGzipFile(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mNot a gzipped file (\u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m%\u001b[39m magic)\n\u001b[1;32m    429\u001b[0m (method, flag,\n\u001b[1;32m    430\u001b[0m  \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_last_mtime) \u001b[38;5;241m=\u001b[39m struct\u001b[38;5;241m.\u001b[39munpack(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m<BBIxx\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_read_exact(\u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m    431\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m method \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m8\u001b[39m:\n",
+      "\u001b[0;31mBadGzipFile\u001b[0m: Not a gzipped file (b'<!')"
+     ]
+    }
+   ],
+   "source": [
+    "import norch\n",
+    "\n",
+    "to_tensor = lambda x: norch.Tensor(x)\n",
+    "reshape = lambda x: x.reshape([-1, 784])\n",
+    "transform = lambda x: reshape(to_tensor(x))\n",
+    "\n",
+    "# Download the training and testing data\n",
+    "train_data, test_data = norch.datasets.MNIST.splits(transform = reshape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -44,18 +101,18 @@
       "[0.8905413746833801,],\n",
       "[0.8028548955917358,],\n",
       "[-0.9388200044631958,]], device=\"cpu\", requires_grad=True)\n",
-      "f1 grad_antes tensor([[0.7763990759849548,],\n",
-      "[-0.07534197717905045,],\n",
-      "[-0.7497915625572205,],\n",
-      "[0.1710733026266098,],\n",
-      "[-0.05041608214378357,],\n",
-      "[-0.020194964483380318,],\n",
-      "[-0.14156067371368408,],\n",
-      "[-0.1368660032749176,],\n",
-      "[-0.027204060927033424,],\n",
-      "[-0.0021885898895561695,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[0.7763985395431519,],\n",
+      "[-0.07534199953079224,],\n",
+      "[-0.7497910857200623,],\n",
+      "[0.17107321321964264,],\n",
+      "[-0.05041635036468506,],\n",
+      "[-0.020194709300994873,],\n",
+      "[-0.1415593922138214,],\n",
+      "[-0.1368662416934967,],\n",
+      "[-0.02720409631729126,],\n",
+      "[-0.002188590820878744,]], device=\"cpu\", requires_grad=True)\n",
       "f2 antes tensor([[-0.5338311195373535,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-3.655086040496826,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-3.6550822257995605,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "f1 depois tensor([[0.6707537770271301,],\n",
@@ -72,7 +129,7 @@
       "\n",
       "\n",
       "\n",
-      "loss_antes tensor([3.549180507659912,], device=\"cpu\", requires_grad=True)\n",
+      "loss_antes tensor([3.5491833686828613,], device=\"cpu\", requires_grad=True)\n",
       "f1 antes tensor([[0.6707537770271301,],\n",
       "[-0.13439051806926727,],\n",
       "[0.5253099203109741,],\n",
@@ -83,25 +140,25 @@
       "[0.8906782269477844,],\n",
       "[0.8028820753097534,],\n",
       "[-0.9388177990913391,]], device=\"cpu\", requires_grad=True)\n",
-      "f1 grad_antes tensor([[0.8605965375900269,],\n",
-      "[-0.0792151615023613,],\n",
-      "[-0.7264857888221741,],\n",
-      "[0.1592811942100525,],\n",
-      "[-0.034485332667827606,],\n",
-      "[-0.014619439840316772,],\n",
-      "[-0.09272521734237671,],\n",
-      "[-0.16714394092559814,],\n",
-      "[-0.01336900144815445,],\n",
-      "[-0.0027314303442835808,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[0.8605958223342896,],\n",
+      "[-0.07921524345874786,],\n",
+      "[-0.7264853715896606,],\n",
+      "[0.15928134322166443,],\n",
+      "[-0.034484878182411194,],\n",
+      "[-0.014619462192058563,],\n",
+      "[-0.09272399544715881,],\n",
+      "[-0.1671433448791504,],\n",
+      "[-0.013369085267186165,],\n",
+      "[-0.0027314182370901108,]], device=\"cpu\", requires_grad=True)\n",
       "f2 antes tensor([[-0.530176043510437,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-3.7678537368774414,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-3.7678496837615967,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "f1 depois tensor([[0.669893205165863,],\n",
       "[-0.13431130349636078,],\n",
       "[0.5260363817214966,],\n",
       "[-0.9961182475090027,],\n",
-      "[-0.10914070159196854,],\n",
+      "[-0.10914070904254913,],\n",
       "[0.44311490654945374,],\n",
       "[-0.5422412753105164,],\n",
       "[0.8908453583717346,],\n",
@@ -111,36 +168,36 @@
       "\n",
       "\n",
       "\n",
-      "loss_antes tensor([4.6171770095825195,], device=\"cpu\", requires_grad=True)\n",
+      "loss_antes tensor([4.617180824279785,], device=\"cpu\", requires_grad=True)\n",
       "f1 antes tensor([[0.669893205165863,],\n",
       "[-0.13431130349636078,],\n",
       "[0.5260363817214966,],\n",
       "[-0.9961182475090027,],\n",
-      "[-0.10914070159196854,],\n",
+      "[-0.10914070904254913,],\n",
       "[0.44311490654945374,],\n",
       "[-0.5422412753105164,],\n",
       "[0.8908453583717346,],\n",
       "[0.8028954267501831,],\n",
       "[-0.9388150572776794,]], device=\"cpu\", requires_grad=True)\n",
-      "f1 grad_antes tensor([[1.01288902759552,],\n",
-      "[-0.08897353708744049,],\n",
-      "[-0.7638412714004517,],\n",
-      "[0.16191577911376953,],\n",
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       "f2 antes tensor([[-0.5264081954956055,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-4.297523498535156,]], device=\"cpu\", requires_grad=True)\n",
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       "\n",
       "\n",
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@@ -150,36 +207,36 @@
       "\n",
       "\n",
       "\n",
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       "f1 antes tensor([[0.6688803434371948,],\n",
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-      "f1 grad_antes tensor([[1.119826316833496,],\n",
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       "f2 antes tensor([[-0.5221107006072998,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-4.806644439697266,]], device=\"cpu\", requires_grad=True)\n",
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       "\n",
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@@ -189,36 +246,36 @@
       "\n",
       "\n",
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       "f1 antes tensor([[0.6677604913711548,],\n",
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-      "f2 grad_antes tensor([[-4.872036933898926,]], device=\"cpu\", requires_grad=True)\n",
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       "\n",
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@@ -228,36 +285,36 @@
       "\n",
       "\n",
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       "f1 antes tensor([[0.6666858196258545,],\n",
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       "f2 antes tensor([[-0.5124320387840271,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-4.344778060913086,]], device=\"cpu\", requires_grad=True)\n",
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       "\n",
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       "f1 depois tensor([[0.6658152341842651,],\n",
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@@ -267,36 +324,36 @@
       "\n",
       "\n",
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       "f1 antes tensor([[0.6658152341842651,],\n",
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       "\n",
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@@ -306,36 +363,36 @@
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       "\n",
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       "\n",
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@@ -384,36 +441,36 @@
       "\n",
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       "f2 antes tensor([[-0.4997139871120453,]], device=\"cpu\", requires_grad=True)\n",
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       "\n",
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@@ -423,36 +480,36 @@
       "\n",
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       "\n",
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@@ -462,36 +519,36 @@
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@@ -501,223 +558,223 @@
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@@ -1286,743 +1343,18653 @@
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       "\n",
       "\n",
       "\n",
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       "f1 antes tensor([[0.6632210612297058,],\n",
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-      "f2 grad_antes tensor([[-1.0008103847503662,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[1.443894575459126e-06,],\n",
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       "\n",
       "\n",
       "f1 depois tensor([[0.6632210612297058,],\n",
       "[-0.1337345540523529,],\n",
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-      "f2 depois tensor([[-0.3995830714702606,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.399583101272583,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "\n",
-      "loss_antes tensor([0.3715560734272003,], device=\"cpu\", requires_grad=True)\n",
+      "loss_antes tensor([0.3715556859970093,], device=\"cpu\", requires_grad=True)\n",
       "f1 antes tensor([[0.6632210612297058,],\n",
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-      "f2 antes tensor([[-0.3995830714702606,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-1.219107985496521,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[1.3045458899796358e-06,],\n",
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+      "f2 grad_antes tensor([[-1.2191067934036255,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "f1 depois tensor([[0.6632210612297058,],\n",
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+      "f2 depois tensor([[-0.39836400747299194,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "\n",
-      "loss_antes tensor([0.9010469317436218,], device=\"cpu\", requires_grad=True)\n",
+      "loss_antes tensor([0.9010468125343323,], device=\"cpu\", requires_grad=True)\n",
       "f1 antes tensor([[0.6632210612297058,],\n",
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-      "f1 grad_antes tensor([[1.5068005723151146e-06,],\n",
-      "[-5.306197863319539e-07,],\n",
-      "[-2.8542974177980796e-05,],\n",
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-      "f2 antes tensor([[-0.39836397767066956,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-1.8984699249267578,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[1.506796706962632e-06,],\n",
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+      "f2 grad_antes tensor([[-1.898468255996704,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "f1 depois tensor([[0.6632210612297058,],\n",
       "[-0.1337345540523529,],\n",
       "[0.5307624936103821,],\n",
       "[-0.9970796704292297,],\n",
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-      "f2 depois tensor([[-0.3964655101299286,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.396465539932251,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "\n",
-      "loss_antes tensor([1.7061519622802734,], device=\"cpu\", requires_grad=True)\n",
+      "loss_antes tensor([1.706151008605957,], device=\"cpu\", requires_grad=True)\n",
       "f1 antes tensor([[0.6632210612297058,],\n",
       "[-0.1337345540523529,],\n",
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       "[0.4453928470611572,],\n",
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       "[-0.9386110305786133,]], device=\"cpu\", requires_grad=True)\n",
-      "f1 grad_antes tensor([[1.5378873285953887e-06,],\n",
-      "[-5.577430783887394e-07,],\n",
-      "[-3.1709354516351596e-05,],\n",
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-      "[-0.4221835136413574,],\n",
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-      "f2 antes tensor([[-0.3964655101299286,]], device=\"cpu\", requires_grad=True)\n",
-      "f2 grad_antes tensor([[-2.6123950481414795,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[1.5378866464743624e-06,],\n",
+      "[-5.577424531111319e-07,],\n",
+      "[-3.170934724039398e-05,],\n",
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+      "[-0.7774237394332886,],\n",
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+      "f2 antes tensor([[-0.396465539932251,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.6123976707458496,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "f1 depois tensor([[0.6632210612297058,],\n",
       "[-0.1337345540523529,],\n",
       "[0.5307625532150269,],\n",
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       "f2 depois tensor([[-0.3938531279563904,]], device=\"cpu\", requires_grad=True)\n",
       "\n",
       "\n",
       "\n",
-      "Epoch [1/1], Loss: 1.7062\n"
+      "Epoch [1/10], Loss: 1.7062\n",
+      "loss_antes tensor([1.7912184000015259,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6632210612297058,],\n",
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+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6626602411270142,],\n",
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+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.8513489961624146,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6626602411270142,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.3884551227092743,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([2.537475824356079,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6620467901229858,],\n",
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+      "f2 grad_antes tensor([[-3.185892105102539,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "f2 grad_antes tensor([[-3.6347692012786865,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.660474419593811,],\n",
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+      "f2 depois tensor([[-0.3816344439983368,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
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+      "f2 antes tensor([[-0.32083117961883545,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-1.1966784000396729,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6577598452568054,],\n",
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+      "f2 depois tensor([[-0.31963449716567993,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.0469222217798233,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6577598452568054,],\n",
+      "[-0.13257884979248047,],\n",
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+      "f1 grad_antes tensor([[6.992023031671124e-07,],\n",
+      "[-3.6159499927634897e-07,],\n",
+      "[-1.146448357758345e-05,],\n",
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+      "f2 antes tensor([[-0.31963449716567993,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.43323057889938354,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.31920126080513,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.0008811806910671294,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6577598452568054,],\n",
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+      "[-3.753726574018401e-08,],\n",
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+      "f2 antes tensor([[-0.31920126080513,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.05936930328607559,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.3191418945789337,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.021754954010248184,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6577598452568054,],\n",
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+      "f1 grad_antes tensor([[2.5998755859291123e-07,],\n",
+      "[-1.410977006344183e-07,],\n",
+      "[-5.038877588958712e-06,],\n",
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+      "f2 antes tensor([[-0.3191418945789337,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.2949911057949066,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6577598452568054,],\n",
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+      "f2 depois tensor([[-0.3188469111919403,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.24520684778690338,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6577598452568054,],\n",
+      "[-0.13257884979248047,],\n",
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+      "f1 grad_antes tensor([[6.450158025472774e-07,],\n",
+      "[-3.58664038913048e-07,],\n",
+      "[-1.3592059985967353e-05,],\n",
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+      "f2 antes tensor([[-0.3188469111919403,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.99036705493927,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6577598452568054,],\n",
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+      "f2 depois tensor([[-0.3178565502166748,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.7371494174003601,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6577598452568054,],\n",
+      "[-0.13257884979248047,],\n",
+      "[0.5354554653167725,],\n",
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+      "f1 grad_antes tensor([[8.264420330306166e-07,],\n",
+      "[-4.720220090348448e-07,],\n",
+      "[-1.894695196824614e-05,],\n",
+      "[7.3678170338098425e-06,],\n",
+      "[-0.4677564203739166,],\n",
+      "[-0.06307730078697205,],\n",
+      "[-0.6389914751052856,],\n",
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+      "f2 antes tensor([[-0.3178565502166748,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-1.7171493768692017,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6577598452568054,],\n",
+      "[-0.13257884979248047,],\n",
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+      "f2 depois tensor([[-0.3161394000053406,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [2/10], Loss: 0.7371\n",
+      "loss_antes tensor([1.1547526121139526,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6577598452568054,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.31399020552635193,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.1643296480178833,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.657313883304596,],\n",
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+      "f2 antes tensor([[-0.31399020552635193,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.1580827236175537,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6568319797515869,],\n",
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+      "f2 depois tensor([[-0.31183212995529175,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.6755621433258057,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6568319797515869,],\n",
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+      "f1 grad_antes tensor([[0.5952900052070618,],\n",
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+      "f2 grad_antes tensor([[-2.5888686180114746,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6562367081642151,],\n",
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+      "f2 depois tensor([[-0.30924326181411743,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([2.2598841190338135,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6562367081642151,],\n",
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+      "f2 antes tensor([[-0.30924326181411743,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-3.0065791606903076,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6555567383766174,],\n",
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+      "f2 depois tensor([[-0.3062366843223572,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([2.234950304031372,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6555567383766174,],\n",
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+      "f1 grad_antes tensor([[0.6354647874832153,],\n",
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+      "f2 antes tensor([[-0.3062366843223572,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.9899518489837646,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.654921293258667,],\n",
+      "[-0.1317436546087265,],\n",
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+      "f1 grad_antes tensor([[-2.712539242111234e-07,],\n",
+      "[1.752526372911234e-07,],\n",
+      "[4.7066314436960965e-06,],\n",
+      "[-1.7390131006322918e-06,],\n",
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+      "f2 antes tensor([[-0.2746489942073822,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.18991079926490784,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6537677645683289,],\n",
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+      "f2 depois tensor([[-0.2748388946056366,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.07597973197698593,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6537677645683289,],\n",
+      "[-0.13144570589065552,],\n",
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+      "f1 grad_antes tensor([[-5.805014779980411e-07,],\n",
+      "[3.8430411564149836e-07,],\n",
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+      "[-4.141878889640793e-06,],\n",
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+      "f2 antes tensor([[-0.2748388946056366,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.5512891411781311,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6537677645683289,],\n",
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+      "f2 depois tensor([[-0.2753901779651642,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.022419605404138565,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6537677645683289,],\n",
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+      "[1.5751494686355727e-07,],\n",
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+      "[-1.8459696775607881e-06,],\n",
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+      "f2 antes tensor([[-0.2753901779651642,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.2994636297225952,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6537677645683289,],\n",
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+      "f2 depois tensor([[-0.27568963170051575,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.041900161653757095,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6537677645683289,],\n",
+      "[-0.13144570589065552,],\n",
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+      "[-0.9985430240631104,],\n",
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+      "[-0.9385664463043213,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[2.342912495123528e-07,],\n",
+      "[-1.62608998266478e-07,],\n",
+      "[-5.21960282640066e-06,],\n",
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+      "[-0.7025344371795654,],\n",
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+      "f2 antes tensor([[-0.27568963170051575,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.4093908369541168,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6537677645683289,],\n",
+      "[-0.13144570589065552,],\n",
+      "[0.5391386151313782,],\n",
+      "[-0.9985430240631104,],\n",
+      "[-0.057779353111982346,],\n",
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+      "[-0.45599600672721863,],\n",
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+      "[-0.9385672807693481,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.275280237197876,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.32609570026397705,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6537677645683289,],\n",
+      "[-0.13144570589065552,],\n",
+      "[0.5391386151313782,],\n",
+      "[-0.9985430240631104,],\n",
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+      "[-0.9385672807693481,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[4.818560341846023e-07,],\n",
+      "[-3.4333146459175623e-07,],\n",
+      "[-1.1675449059111997e-05,],\n",
+      "[4.739212272397708e-06,],\n",
+      "[-0.47193965315818787,],\n",
+      "[-0.051527950912714005,],\n",
+      "[-0.5948068499565125,],\n",
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+      "f2 antes tensor([[-0.275280237197876,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-1.1420964002609253,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6537677645683289,],\n",
+      "[-0.13144570589065552,],\n",
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+      "[-0.9985430240631104,],\n",
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+      "f2 depois tensor([[-0.2741381525993347,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [3/10], Loss: 0.3261\n",
+      "loss_antes tensor([0.8737258315086365,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6537677645683289,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.272268682718277,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.8657096028327942,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6533827185630798,],\n",
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+      "f2 grad_antes tensor([[-1.8608695268630981,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.2704078257083893,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.2942181825637817,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6529702544212341,],\n",
+      "[-0.13115152716636658,],\n",
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+      "f1 grad_antes tensor([[0.5189365148544312,],\n",
+      "[-0.1730600893497467,],\n",
+      "[-0.5054467916488647,],\n",
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+      "f2 antes tensor([[-0.2704078257083893,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.2752740383148193,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6524513363838196,],\n",
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+      "f2 depois tensor([[-0.2681325376033783,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.7930351495742798,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6524513363838196,],\n",
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+      "f1 grad_antes tensor([[0.5998929738998413,],\n",
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+      "f2 antes tensor([[-0.2681325376033783,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.6780848503112793,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6518514156341553,],\n",
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+      "f2 depois tensor([[-0.2654544413089752,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.7533721923828125,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6518514156341553,],\n",
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+      "f1 grad_antes tensor([[0.5563762187957764,],\n",
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+      "f2 antes tensor([[-0.2654544413089752,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.648298978805542,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6512950658798218,],\n",
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+      "f2 depois tensor([[-0.26280614733695984,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.038375735282898,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6512950658798218,],\n",
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+      "f1 grad_antes tensor([[0.3844916522502899,],\n",
+      "[-0.11285840719938278,],\n",
+      "[-0.3099977374076843,],\n",
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+      "f2 depois tensor([[-0.2504063546657562,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.1857844889163971,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6505272388458252,],\n",
+      "[-0.13039615750312805,],\n",
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+      "f1 grad_antes tensor([[-8.282784165203338e-07,],\n",
+      "[6.213047072378686e-07,],\n",
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+      "[-6.319565727608278e-06,],\n",
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+      "f2 antes tensor([[-0.2504063546657562,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.862054705619812,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6505272388458252,],\n",
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+      "f2 depois tensor([[-0.2512684166431427,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.08998353779315948,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6505272388458252,],\n",
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+      "f2 antes tensor([[-0.2512684166431427,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.5999447107315063,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.2518683671951294,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.003489461727440357,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6505272388458252,],\n",
+      "[-0.13039615750312805,],\n",
+      "[0.5422416925430298,],\n",
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+      "f1 grad_antes tensor([[6.148190578869617e-08,],\n",
+      "[-4.838430456288734e-08,],\n",
+      "[-1.416049030922295e-06,],\n",
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+      "f2 antes tensor([[-0.2518683671951294,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.11814315617084503,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6505272388458252,],\n",
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+      "f2 depois tensor([[-0.25175023078918457,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.18360266089439392,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6505272388458252,],\n",
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+      "f1 grad_antes tensor([[3.2821387208059605e-07,],\n",
+      "[-2.6523520091359387e-07,],\n",
+      "[-8.220880772569217e-06,],\n",
+      "[3.4672391393542057e-06,],\n",
+      "[-0.46908363699913025,],\n",
+      "[-0.0424647331237793,],\n",
+      "[-0.6101729273796082,],\n",
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+      "f2 antes tensor([[-0.25175023078918457,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.8569777607917786,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6505272388458252,],\n",
+      "[-0.13039615750312805,],\n",
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+      "f2 depois tensor([[-0.2508932650089264,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [4/10], Loss: 0.1836\n",
+      "loss_antes tensor([0.7371393442153931,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6505272388458252,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.24917612969875336,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.723792314529419,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6501758098602295,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.24747461080551147,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.1125712394714355,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6498010754585266,],\n",
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+      "f1 grad_antes tensor([[0.47777873277664185,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.24536503851413727,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.5711164474487305,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6493232846260071,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.2428581565618515,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.5283877849578857,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.648766279220581,],\n",
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+      "f1 grad_antes tensor([[0.514426589012146,],\n",
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+      "f2 antes tensor([[-0.2428581565618515,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.472560167312622,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.24038559198379517,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.8637006878852844,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6482518315315247,],\n",
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+      "f1 grad_antes tensor([[0.34661489725112915,],\n",
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+      "f2 antes tensor([[-0.24038559198379517,]], device=\"cpu\", requires_grad=True)\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "loss_antes tensor([0.19525720179080963,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6479052305221558,],\n",
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+      "f1 depois tensor([[0.6476705074310303,],\n",
+      "[-0.12940824031829834,],\n",
+      "[0.5450354218482971,],\n",
+      "[-0.9994741082191467,],\n",
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+      "[-0.938912570476532,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.23620453476905823,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.13052301108837128,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6476705074310303,],\n",
+      "[-0.12940824031829834,],\n",
+      "[0.5450354218482971,],\n",
+      "[-0.9994741082191467,],\n",
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+      "f1 grad_antes tensor([[-4.7308716943916806e-07,],\n",
+      "[3.954105238790362e-07,],\n",
+      "[1.0264103366353083e-05,],\n",
+      "[-4.2548599594738334e-06,],\n",
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+      "f2 antes tensor([[-0.23620453476905823,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.7225592732429504,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6476705074310303,],\n",
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+      "f2 depois tensor([[-0.2369270920753479,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([2.7471611474538804e-07,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6476705074310303,],\n",
+      "[-0.12940824031829834,],\n",
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+      "f1 grad_antes tensor([[-5.04402020151673e-10,],\n",
+      "[4.3226255908024314e-10,],\n",
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+      "f2 antes tensor([[-0.2369270920753479,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.0010482671204954386,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6476705074310303,],\n",
+      "[-0.12940824031829834,],\n",
+      "[0.5450354218482971,],\n",
+      "[-0.9994741082191467,],\n",
+      "[-0.013187147676944733,],\n",
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+      "[-0.3864831328392029,],\n",
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+      "f2 depois tensor([[-0.23692813515663147,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.1373223513364792,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6476705074310303,],\n",
+      "[-0.12940824031829834,],\n",
+      "[0.5450354218482971,],\n",
+      "[-0.9994741082191467,],\n",
+      "[-0.013187147676944733,],\n",
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+      "[0.8236567378044128,],\n",
+      "[-0.9389217495918274,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[2.620860755087051e-07,],\n",
+      "[-2.3077440403085347e-07,],\n",
+      "[-6.721117642882746e-06,],\n",
+      "[2.935278871518676e-06,],\n",
+      "[-0.46442094445228577,],\n",
+      "[-0.03493456542491913,],\n",
+      "[-0.6232568025588989,],\n",
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+      "f2 antes tensor([[-0.23692813515663147,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.7411404848098755,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6476705074310303,],\n",
+      "[-0.12940824031829834,],\n",
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+      "f2 depois tensor([[-0.23618699610233307,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [5/10], Loss: 0.1373\n",
+      "loss_antes tensor([0.6579055786132812,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6476705074310303,],\n",
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+      "f1 grad_antes tensor([[0.33010154962539673,],\n",
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+      "f2 grad_antes tensor([[-1.6222267150878906,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.23456476628780365,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.6434317231178284,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6473404169082642,],\n",
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+      "f1 grad_antes tensor([[0.35126182436943054,],\n",
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+      "f2 antes tensor([[-0.23456476628780365,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-1.6042829751968384,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.23296047747135162,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.0104846954345703,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6469891667366028,],\n",
+      "[-0.12913130223751068,],\n",
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+      "f1 grad_antes tensor([[0.45241039991378784,],\n",
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+      "f2 antes tensor([[-0.23296047747135162,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.01045560836792,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6465367674827576,],\n",
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+      "f2 depois tensor([[-0.2309500277042389,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.4477484226226807,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6465367674827576,],\n",
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+      "f1 grad_antes tensor([[0.530709981918335,],\n",
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+      "f2 antes tensor([[-0.2309500277042389,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.40645170211792,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6460060477256775,],\n",
+      "[-0.12878061830997467,],\n",
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+      "f2 depois tensor([[-0.22854357957839966,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.4058836698532104,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6460060477256775,],\n",
+      "[-0.12878061830997467,],\n",
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+      "f1 grad_antes tensor([[0.4890628159046173,],\n",
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+      "f2 antes tensor([[-0.22854357957839966,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-2.37139892578125,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6455169916152954,],\n",
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+      "f2 depois tensor([[-0.22617217898368835,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.7721152305603027,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6455169916152954,],\n",
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+      "f1 grad_antes tensor([[0.3243308663368225,],\n",
+      "[-0.10512252897024155,],\n",
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+      "f2 antes tensor([[-0.22617217898368835,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-1.75740385055542,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6451926827430725,],\n",
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+      "f2 depois tensor([[-0.22441478073596954,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.1531953662633896,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6451926827430725,],\n",
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+      "f1 grad_antes tensor([[0.12429937720298767,],\n",
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+      "f2 grad_antes tensor([[-0.7828041315078735,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
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+      "f1 depois tensor([[0.6450684070587158,],\n",
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+      "f2 depois tensor([[-0.22363197803497314,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
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+      "f2 antes tensor([[-0.22525587677955627,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.7667660117149353,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.2260226458311081,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.0005024892743676901,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6450346112251282,],\n",
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+      "f1 grad_antes tensor([[-2.0126366351291836e-08,],\n",
+      "[1.8438569782119885e-08,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.22606748342514038,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.12207801640033722,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6450346112251282,],\n",
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+      "f1 grad_antes tensor([[2.3026274220683263e-07,],\n",
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+      "[-6.018398380547296e-06,],\n",
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+      "[-0.6307750940322876,],\n",
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+      "f2 antes tensor([[-0.22606748342514038,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.698793351650238,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.2253686934709549,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [6/10], Loss: 0.1221\n",
+      "loss_antes tensor([0.6033365726470947,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6450346112251282,],\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "f1 antes tensor([[0.6443859338760376,],\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "loss_antes tensor([1.326645016670227,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.643439769744873,],\n",
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+      "\n",
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+      "f2 depois tensor([[-0.21569719910621643,]], device=\"cpu\", requires_grad=True)\n",
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+      "\n",
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+      "f1 antes tensor([[0.6429685950279236,],\n",
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+      "f1 grad_antes tensor([[-2.4917271801427887e-08,],\n",
+      "[2.416898681190105e-08,],\n",
+      "[6.087056476644648e-07,],\n",
+      "[-2.7536094648894505e-07,],\n",
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+      "f2 antes tensor([[-0.21709378063678741,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.05915902182459831,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6425473093986511,],\n",
+      "[-0.12754473090171814,],\n",
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+      "[-0.31475508213043213,],\n",
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+      "f2 depois tensor([[-0.217152938246727,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.11730096489191055,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6425473093986511,],\n",
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+      "f1 grad_antes tensor([[2.1153007878638164e-07,],\n",
+      "[-2.1110943748681166e-07,],\n",
+      "[-5.620816864393419e-06,],\n",
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+      "[-0.45417192578315735,],\n",
+      "[-0.02233201079070568,],\n",
+      "[-0.6364914774894714,],\n",
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+      "f2 antes tensor([[-0.217152938246727,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.6849842667579651,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6425473093986511,],\n",
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+      "f2 depois tensor([[-0.216467946767807,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [7/10], Loss: 0.1173\n",
+      "loss_antes tensor([0.5608254075050354,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6425473093986511,],\n",
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+      "\n",
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+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.547595739364624,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6422457695007324,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.21349018812179565,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.8901832103729248,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6419252157211304,],\n",
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+      "f1 grad_antes tensor([[0.4195728302001953,],\n",
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+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.21160319447517395,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
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+      "\n",
+      "loss_antes tensor([1.3047960996627808,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6415056586265564,],\n",
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+      "\n",
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+      "f2 depois tensor([[-0.2093186378479004,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([1.2677077054977417,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.641008734703064,],\n",
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+      "\n",
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+      "f2 depois tensor([[-0.20706678926944733,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.6731455326080322,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.640551745891571,],\n",
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+      "f1 grad_antes tensor([[0.2971467971801758,],\n",
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+      "\n",
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+      "\n",
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+      "f1 grad_antes tensor([[1.9790178384937462e-07,],\n",
+      "[-2.0802022504540219e-07,],\n",
+      "[-5.337563834473258e-06,],\n",
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+      "f2 grad_antes tensor([[-0.6810067892074585,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.20864929258823395,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [8/10], Loss: 0.1159\n",
+      "loss_antes tensor([0.5252150893211365,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6401737332344055,],\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "f1 antes tensor([[0.6378946304321289,],\n",
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+      "\n",
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+      "\n",
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+      "f2 depois tensor([[-0.20138061046600342,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
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+      "f1 antes tensor([[0.6378946304321289,],\n",
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+      "f2 grad_antes tensor([[0.7822472453117371,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
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+      "f2 depois tensor([[-0.20216286182403564,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.0010424922220408916,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6378946304321289,],\n",
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+      "f1 grad_antes tensor([[-2.4213999694211452e-08,],\n",
+      "[2.5909811185442777e-08,],\n",
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+      "f2 grad_antes tensor([[0.06457527726888657,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6378946304321289,],\n",
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+      "[-0.9406284093856812,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.20222744345664978,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.11544226855039597,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6378946304321289,],\n",
+      "[-0.12578056752681732,],\n",
+      "[0.554867148399353,],\n",
+      "[-1.0009452104568481,],\n",
+      "[0.07564683258533478,],\n",
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+      "[-0.24286840856075287,],\n",
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+      "[0.8356553912162781,],\n",
+      "[-0.9406284093856812,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[1.8643919474925497e-07,],\n",
+      "[-2.0553028434733278e-07,],\n",
+      "[-5.097626399219735e-06,],\n",
+      "[2.5081571948248893e-06,],\n",
+      "[-0.44419458508491516,],\n",
+      "[-0.011491646990180016,],\n",
+      "[-0.6479165554046631,],\n",
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+      "f2 antes tensor([[-0.20222744345664978,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.6795352101325989,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6378946304321289,],\n",
+      "[-0.12578056752681732,],\n",
+      "[0.554867148399353,],\n",
+      "[-1.0009452104568481,],\n",
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+      "[-0.24222049117088318,],\n",
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+      "[-0.9406394362449646,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.20154790580272675,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [9/10], Loss: 0.1154\n",
+      "loss_antes tensor([0.49423569440841675,], device=\"cpu\", requires_grad=True)\n",
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+      "\n",
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+      "\n",
+      "loss_antes tensor([0.07114461064338684,], device=\"cpu\", requires_grad=True)\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
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+      "\n",
+      "loss_antes tensor([0.13713476061820984,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
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+      "f2 grad_antes tensor([[-0.7406349778175354,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
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+      "f2 depois tensor([[-0.19315429031848907,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.0008557140245102346,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
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+      "f1 grad_antes tensor([[7.280428349076828e-08,],\n",
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+      "f2 antes tensor([[-0.19315429031848907,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.05850522965192795,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6356977224349976,],\n",
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+      "f2 depois tensor([[-0.19309578835964203,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.11809159070253372,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
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+      "[-1.001272201538086,],\n",
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+      "f1 grad_antes tensor([[-6.251980835259019e-07,],\n",
+      "[6.4199883809124e-07,],\n",
+      "[1.2378923202049918e-05,],\n",
+      "[-5.536154276342131e-06,],\n",
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+      "f2 antes tensor([[-0.19309578835964203,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.6872891783714294,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
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+      "[-1.001272201538086,],\n",
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+      "f2 depois tensor([[-0.19378307461738586,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.2703823745250702,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
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+      "[-1.001272201538086,],\n",
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+      "f1 grad_antes tensor([[-6.91533898589114e-07,],\n",
+      "[7.307918963306292e-07,],\n",
+      "[1.4889592421241105e-05,],\n",
+      "[-6.865194336569402e-06,],\n",
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+      "f2 antes tensor([[-0.19378307461738586,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[1.0399656295776367,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
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+      "[-0.9412215948104858,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.19482304155826569,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.15285517275333405,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
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+      "[0.8379193544387817,],\n",
+      "[-0.9412215948104858,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[-3.8008357705621165e-07,],\n",
+      "[4.1295672303931497e-07,],\n",
+      "[8.896585313777905e-06,],\n",
+      "[-4.230267677485244e-06,],\n",
+      "[-0.45896172523498535,],\n",
+      "[-0.007265132386237383,],\n",
+      "[-0.8248808979988098,],\n",
+      "[-0.24633106589317322,],\n",
+      "[-0.04836709797382355,],\n",
+      "[0.012506773695349693,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 antes tensor([[-0.19482304155826569,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.7819349765777588,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
+      "[0.09701727330684662,],\n",
+      "[0.45901572704315186,],\n",
+      "[-0.20772269368171692,],\n",
+      "[1.036684513092041,],\n",
+      "[0.8379676938056946,],\n",
+      "[-0.9412341117858887,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.19560497999191284,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.001087940763682127,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
+      "[0.09701727330684662,],\n",
+      "[0.45901572704315186,],\n",
+      "[-0.20772269368171692,],\n",
+      "[1.036684513092041,],\n",
+      "[0.8379676938056946,],\n",
+      "[-0.9412341117858887,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[-2.343993088516072e-08,],\n",
+      "[2.6201785630064478e-08,],\n",
+      "[5.965808327346167e-07,],\n",
+      "[-2.924781483670813e-07,],\n",
+      "[-0.4487413763999939,],\n",
+      "[-0.0069082132540643215,],\n",
+      "[-0.7385655641555786,],\n",
+      "[-0.2471114993095398,],\n",
+      "[-0.04836709797382355,],\n",
+      "[0.012506773695349693,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 antes tensor([[-0.19560497999191284,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[0.06596784293651581,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
+      "[0.09746601432561874,],\n",
+      "[0.4590226411819458,],\n",
+      "[-0.20698413252830505,],\n",
+      "[1.0369316339492798,],\n",
+      "[0.8380160331726074,],\n",
+      "[-0.9412466287612915,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.195670947432518,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "loss_antes tensor([0.11488907784223557,], device=\"cpu\", requires_grad=True)\n",
+      "f1 antes tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
+      "[0.09746601432561874,],\n",
+      "[0.4590226411819458,],\n",
+      "[-0.20698413252830505,],\n",
+      "[1.0369316339492798,],\n",
+      "[0.8380160331726074,],\n",
+      "[-0.9412466287612915,]], device=\"cpu\", requires_grad=True)\n",
+      "f1 grad_antes tensor([[1.7607962377041986e-07,],\n",
+      "[-2.029000540915149e-07,],\n",
+      "[-4.875874310528161e-06,],\n",
+      "[2.463153350618086e-06,],\n",
+      "[-0.439397394657135,],\n",
+      "[-0.006496804300695658,],\n",
+      "[-0.6538427472114563,],\n",
+      "[-0.24717746675014496,],\n",
+      "[-0.04836709797382355,],\n",
+      "[0.012506773695349693,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 antes tensor([[-0.195670947432518,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 grad_antes tensor([[-0.6779057383537292,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "f1 depois tensor([[0.6356977224349976,],\n",
+      "[-0.12492786347866058,],\n",
+      "[0.5571243166923523,],\n",
+      "[-1.001272201538086,],\n",
+      "[0.09790541231632233,],\n",
+      "[0.4590291380882263,],\n",
+      "[-0.2063302844762802,],\n",
+      "[1.0371787548065186,],\n",
+      "[0.8380643725395203,],\n",
+      "[-0.9412591457366943,]], device=\"cpu\", requires_grad=True)\n",
+      "f2 depois tensor([[-0.1949930489063263,]], device=\"cpu\", requires_grad=True)\n",
+      "\n",
+      "\n",
+      "\n",
+      "Epoch [10/10], Loss: 0.1149\n"
      ]
     }
    ],
@@ -2050,7 +20017,7 @@
     "        return out\n",
     "\n",
     "device = \"cpu\"\n",
-    "epochs = 1\n",
+    "epochs = 10\n",
     "\n",
     "model = MyModel().to(device)\n",
     "criterion = nn.MSELoss()\n",
@@ -2067,7 +20034,7 @@
     "for x in x_values:\n",
     "    y_true.append(math.pow(math.sin(x), 2))\n",
     "\n",
-    "batch_size = 1000\n",
+    "batch_size = 100\n",
     "\n",
     "\n",
     "for epoch in range(epochs):\n",
@@ -2178,7 +20145,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
diff --git a/norch/__init__.py b/norch/__init__.py
index ca7be0b..3eb0b7f 100644
--- a/norch/__init__.py
+++ b/norch/__init__.py
@@ -2,6 +2,7 @@ from norch.tensor import Tensor
 from .nn import *
 from .optim import *
 from .utils import *
+from .datasets import *
 
 __version__ = "0.0.1"
 __author__ = 'Lucas de Lima Nogueira'
diff --git a/norch/__pycache__/__init__.cpython-38.pyc b/norch/__pycache__/__init__.cpython-38.pyc
index d172276..8202d1d 100644
Binary files a/norch/__pycache__/__init__.cpython-38.pyc and b/norch/__pycache__/__init__.cpython-38.pyc differ
diff --git a/norch/__pycache__/tensor.cpython-38.pyc b/norch/__pycache__/tensor.cpython-38.pyc
index cc2bfbb..9764715 100644
Binary files a/norch/__pycache__/tensor.cpython-38.pyc and b/norch/__pycache__/tensor.cpython-38.pyc differ
diff --git a/norch/autograd/__pycache__/functions.cpython-38.pyc b/norch/autograd/__pycache__/functions.cpython-38.pyc
index d56a797..0a7fe5b 100644
Binary files a/norch/autograd/__pycache__/functions.cpython-38.pyc and b/norch/autograd/__pycache__/functions.cpython-38.pyc differ
diff --git a/norch/datasets/__init__.py b/norch/datasets/__init__.py
new file mode 100644
index 0000000..fe34b11
--- /dev/null
+++ b/norch/datasets/__init__.py
@@ -0,0 +1 @@
+from .mnist import *
\ No newline at end of file
diff --git a/norch/datasets/mnist.py b/norch/datasets/mnist.py
new file mode 100644
index 0000000..ff11edf
--- /dev/null
+++ b/norch/datasets/mnist.py
@@ -0,0 +1,90 @@
+import gzip
+import os
+import norch
+from norch.utils.data import Dataset
+import numpy as np
+
+class MNIST(Dataset):
+    """
+    Loads training, validation, and test partitions of the mnist dataset
+    (http://yann.lecun.com/exdb/mnist/). If the data is not already contained in data_dir, it will
+    try to download it.
+
+    This dataset contains 60000 training examples, and 10000 test examples of handwritten digits
+    in {0, ..., 9} and corresponding labels. Each handwritten image has an "original" dimension of
+    28x28x1, and is stored row-wise as a string of 784x1 bytes. Pixel values are in range 0 to 255
+    (inclusive).
+
+    Args:
+        data_dir: String. Relative or absolute path of the dataset.
+        devel_size: Integer. Size of the development (validation) dataset partition.
+
+    Returns:
+        X_train: float64 numpy array with shape [784, 60000-devel_size] with values in [0, 1].
+        Y_train: uint8 numpy array with shape [60000-devel_size]. Labels.
+        X_devel: float64 numpy array with shape [784, devel_size] with values in [0, 1].
+        Y_devel: uint8 numpy array with shape [devel_size]. Labels.
+        X_test: float64 numpy array with shape [784, 10000] with values in [0, 1].
+        Y_test: uint8 numpy array with shape [10000]. Labels.
+    """
+
+    urls = ['https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz',
+            'https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz',
+            'https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz',
+            'https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz',]
+    name = 'mnist-data-py'
+    dirname = 'mnist'
+
+    def __init__(self, path_data, path_label, transform=None, target_transform=None):
+        self.data = self._load_mnist(path_data, header_size=16).reshape((-1, 28, 28))
+        self.labels = self._load_mnist(path_label, header_size=8)
+        self.transform = transform
+        self.target_transform = target_transform
+
+    def _load_mnist(self, path, header_size):
+        with gzip.open(path, 'rb') as f:
+            data = np.frombuffer(f.read(), np.uint8, offset=header_size)
+        return np.asarray(data, dtype=np.uint8)
+
+
+    @classmethod
+    def splits(cls, root='.data', train_data='train-images-idx3-ubyte.gz', train_label='train-labels-idx1-ubyte.gz',
+               test_data='t10k-images-idx3-ubyte.gz', test_label='t10k-labels-idx1-ubyte.gz', **kwargs):
+        r"""
+        Loads training and test partitions of the [mnist dataset](https://www.cs.toronto.edu/~kriz/cifar.html). If
+        the data is not already contained in the ``root`` folder, it will download it.
+
+        Args:
+            root (str): relative or absolute path of the dataset.
+
+        Returns:
+            tuple(Dataset): training and testing datasets
+        """
+        path = os.path.join(root, cls.dirname, cls.name)
+        if not os.path.isdir(path):
+            path = cls.download(root)
+        train_data = os.path.join(path, train_data)
+        train_label = os.path.join(path, train_label)
+        test_data = os.path.join(path, test_data)
+        test_label = os.path.join(path, test_label)
+        return MNIST(train_data, train_label, **kwargs), MNIST(test_data, test_label, **kwargs)
+
+    def __getitem__(self, item):
+        data = self.data[item].tolist()
+        label = self.labels[item].tolist()
+
+        if self.transform is not None:
+            data = self.transform(data)
+        
+        if self.target_transform is not None:
+            label = self.target_transform(label)
+            
+        
+
+        return data, label
+
+    def __setitem__(self, key, value):
+        self.data[key], self.labels[key] = value
+
+    def __len__(self):
+        return len(self.data)
diff --git a/norch/tensor.py b/norch/tensor.py
index 807c290..8f3ae12 100644
--- a/norch/tensor.py
+++ b/norch/tensor.py
@@ -19,13 +19,18 @@ class Tensor:
     def __init__(self, data=None, device="cpu", requires_grad=False):
 
         if data != None:
+            if isinstance(data, (float, int)):
+                data = [data]
+
             data, shape = self.flatten(data)
-            self.data_ctype = (ctypes.c_float * len(data))(*data)
-            self.shape_ctype = (ctypes.c_int * len(shape))(*shape)
+            
+            self.shape = shape.copy()
+            
+            self.data_ctype = (ctypes.c_float * len(data))(*data.copy())
+            self.shape_ctype = (ctypes.c_int * len(shape))(*shape.copy())
             self.ndim_ctype = ctypes.c_int(len(shape))
             self.device_ctype = device.encode('utf-8')
 
-            self.shape = shape
             self.ndim = len(shape)
             self.device = device
 
@@ -109,6 +114,25 @@ class Tensor:
         return result_data
     
     def reshape(self, new_shape):
+        # Calculate the total number of elements in the tensor
+        total_elements = self.numel
+        
+        # Check for the presence of -1 in new_shape
+        if new_shape.count(-1) > 1:
+            raise ValueError("Only one dimension can be inferred (set to -1).")
+        
+        inferred_dim = None
+        known_dims_product = 1
+        for dim in new_shape:
+            if dim == -1:
+                inferred_dim = dim
+            else:
+                known_dims_product *= dim
+        
+        # Calculate the inferred dimension if -1 is present
+        if inferred_dim == -1:
+            inferred_dim_size = total_elements // known_dims_product
+            new_shape = [inferred_dim_size if dim == -1 else dim for dim in new_shape]
 
         new_shape_ctype = (ctypes.c_int * len(new_shape))(*new_shape)
         new_ndim_ctype = ctypes.c_int(len(new_shape))
diff --git a/norch/utils/__init__.py b/norch/utils/__init__.py
index 90f60fd..c6adfd1 100644
--- a/norch/utils/__init__.py
+++ b/norch/utils/__init__.py
@@ -1 +1,2 @@
-from .utils import *
\ No newline at end of file
+from .utils import *
+from .data import *
\ No newline at end of file
diff --git a/norch/utils/__pycache__/utils.cpython-38.pyc b/norch/utils/__pycache__/utils.cpython-38.pyc
index 95d3b45..d78c80a 100644
Binary files a/norch/utils/__pycache__/utils.cpython-38.pyc and b/norch/utils/__pycache__/utils.cpython-38.pyc differ
diff --git a/norch/utils/data/__init__.py b/norch/utils/data/__init__.py
new file mode 100644
index 0000000..2397554
--- /dev/null
+++ b/norch/utils/data/__init__.py
@@ -0,0 +1,4 @@
+from .dataset import *
+from .example import *
+from .dataloader import *
+from .batch import *
\ No newline at end of file
diff --git a/norch/utils/data/batch.py b/norch/utils/data/batch.py
new file mode 100644
index 0000000..3c9b92c
--- /dev/null
+++ b/norch/utils/data/batch.py
@@ -0,0 +1,13 @@
+class Batch(object):
+    """
+    A ``Batch``depends on a ``Dataset`` and is made of ``batch_size`` ``Example``.
+    """
+    def __init__(self, example, batch_size):
+        self.example = example
+        self.batch_size = batch_size
+        
+    def __getitem__(self, item):
+        return self.example[item]
+
+    def __len__(self):
+        return self.batch_size
\ No newline at end of file
diff --git a/norch/utils/data/dataloader.py b/norch/utils/data/dataloader.py
new file mode 100644
index 0000000..01bd41e
--- /dev/null
+++ b/norch/utils/data/dataloader.py
@@ -0,0 +1,20 @@
+import numpy as np
+from .batch import Batch
+
+
+class Dataloader(object):
+
+    def __init__(self, dataset, batch_size=32):
+        self.dataset = dataset
+        self.batch_size = batch_size
+
+    def __iter__(self):
+        starts = np.arange(0, len(self.dataset), self.batch_size)
+
+        for start in starts:
+            end = start + self.batch_size
+            batch_size = min(end, len(self.dataset)) - start
+            yield Batch(self.dataset[start:end], batch_size)
+
+    def __len__(self):
+        return len(self.dataset) // self.batch_size
diff --git a/norch/utils/data/dataset.py b/norch/utils/data/dataset.py
new file mode 100644
index 0000000..d38c1ea
--- /dev/null
+++ b/norch/utils/data/dataset.py
@@ -0,0 +1,74 @@
+from abc import ABC, abstractmethod
+import os
+from norch.utils import extract_to_dir, download_from_url
+from .example import Example
+import norch
+
+
+class Dataset(ABC):
+    r"""
+    Abstract Dataset class. All dataset for machine learning purposes can inherits from this architecture,
+    for convenience.
+    """
+    urls = []
+    name = ''
+    dirname = ''
+
+    def __init__(self, examples, fields):
+        self.examples = examples
+        self.fields = fields
+
+    @classmethod
+    def splits(cls, train=None, test=None, valid=None, root='.'):
+        raise NotImplementedError
+
+    @classmethod
+    def download(cls, root):
+        r"""Download and unzip a web archive (.zip, .gz, or .tgz).
+
+        Args:
+            root (str): Folder to download data to.
+
+        Returns:
+            string: Path to extracted dataset.
+        """
+        path_dirname = os.path.join(root, cls.dirname)
+        path_name = os.path.join(path_dirname, cls.name)
+        if not os.path.isdir(path_dirname):
+            for url in cls.urls:
+                filename = os.path.basename(url)
+                zpath = os.path.join(path_dirname, filename)
+                if not os.path.isfile(zpath):
+                    if not os.path.exists(os.path.dirname(zpath)):
+                        os.makedirs(os.path.dirname(zpath))
+                    print(f'Download {filename} from {url} to {zpath}')
+                    download_from_url(url, zpath)
+                extract_to_dir(zpath, path_name)
+
+        return path_name
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        string = f"Dataset {name}("
+        tab = "   "
+        for (key, value) in self.__dict__.items():
+            if key[0] != "_":
+                if isinstance(value, Example):
+                    fields = self.fields
+                    for (name, field) in fields:
+                        string += f"\n{tab}({name}): {field.__class__.__name__}" \
+                                  f"(transform={True if field.transform is not None else None}, dtype={field.dtype})"
+                elif isinstance(value, norch.Tensor):
+                    string += f"\n{tab}({key}): {value.__class__.__name__}(shape={value.shape}, dtype={value.dtype})"
+                else:
+                    string += f"\n{tab}({key}): {value.__class__.__name__}"
+        return f'{string}\n)'
+
+    def __getitem__(self, item):
+        return self.examples[item]
+
+    def __setitem__(self, key, value):
+        self.examples[key] = value
+
+    def __len__(self):
+        return len(self.examples)
diff --git a/norch/utils/data/example.py b/norch/utils/data/example.py
new file mode 100644
index 0000000..a4f4ebf
--- /dev/null
+++ b/norch/utils/data/example.py
@@ -0,0 +1,65 @@
+# File: example.py
+# Creation: Wednesday August 19th 2020
+# Author: Arthur Dujardin
+# Contact: arthur.dujardin@ensg.eu
+#          arthurd@ifi.uio.no
+# --------
+# Copyright (c) 2020 Arthur Dujardin
+
+
+class Field(object):
+    r"""
+    A ``Field`` defines the data to process from a raw dataset. It will convert the data into a tensor. The data can
+    be a string, integers, float etc. The data is meant to be preprocessed and the attribute ``transform`` handles
+    the way the user want to process the raw data.
+    """
+
+    def __init__(self, transform=None, dtype=None):
+        self.transform = transform
+        self.dtype = dtype
+
+    def process(self, value):
+        """Applies the transformation and changes the data's type if necessary.
+
+        Args:
+            value (Tensor):
+
+        Returns:
+
+        """
+        if self.transform is not None:
+            value = self.transform(value)
+        if self.dtype is not None:
+            value = value.astype(self.dtype)
+        return value
+
+
+class Example(object):
+    r"""
+    Store a single training / testing example, and store it as an attribute.
+    Highly inspired from PyTorch `Example <https://github.com/pytorch/text/blob/master/torchtext/data/example.py>`__.
+    
+    """
+    @classmethod
+    def fromlist(cls, values, fields):
+        """
+        Add an example from a list of data with respect to the fields.
+
+        Args:
+            values: raw data
+            fields (tuple(string, Field)): fields to preprocess the data on
+
+        Returns:
+            None
+        """
+        example = cls()
+        for (value, field) in zip(values, fields):
+            assert len(field) == 2, f"expected a field template similar to \
+                                    ('name_field', Field()) but got {format(field)}"
+            assert isinstance(field[1], Field), f"expected a field template similar to \
+                                                ('name_field', Field()) but got {format(field)}"
+            name = field[0]
+            value = field[1].process(value)
+            setattr(example, name, value)
+
+        return example
diff --git a/norch/utils/utils.py b/norch/utils/utils.py
index 1652e16..f8b9b30 100644
--- a/norch/utils/utils.py
+++ b/norch/utils/utils.py
@@ -1,4 +1,9 @@
 import random
+import requests
+import tarfile
+import zipfile
+import shutil
+import os
 
 def generate_random_list(shape):
     """
@@ -11,4 +16,105 @@ def generate_random_list(shape):
         if len(inner_shape) == 0:
             return [random.uniform(-1, 1) for _ in range(shape[0])]
         else:
-            return [generate_random_list(inner_shape) for _ in range(shape[0])]
\ No newline at end of file
+            return [generate_random_list(inner_shape) for _ in range(shape[0])]
+
+
+def download_from_url(url, save_path, chunk_size=128):
+    """Download a file from an URL.
+
+    Original answer from https://stackoverflow.com/questions/9419162/download-returned-zip-file-from-url
+
+    Args:
+        url (str): path to the URL.
+        save_path (str): path to the saving directory.
+        chunk_size (int): download chunk.
+
+    Returns:
+        None
+    """
+    response = requests.get(url, stream=True)
+    total = response.headers.get('content-length')
+    with open(save_path, 'wb') as f:
+        if total is None:
+            f.write(response.content)
+        else:
+            downloaded = 0
+            total = int(total)
+            for data in response.iter_content(chunk_size=max(int(total / 1000), 1024 * 1024)):
+                downloaded += len(data)
+                f.write(data)
+                progress_bar(downloaded, total, "Downloading...")
+
+
+def extract_to_dir(filename, dirpath='.'):
+    # Does not create folder twice with the same name
+    name, ext = os.path.splitext(filename)
+    # if os.path.basename(name) == os.path.basename(dirpath):
+    #     dirpath = '.'
+    # Extract
+    print(dirpath)
+    print("Extracting...", end="")
+    if tarfile.is_tarfile(filename):
+        tarfile.open(filename, 'r').extractall(dirpath)
+    elif zipfile.is_zipfile(filename):
+        zipfile.ZipFile(filename, 'r').extractall(dirpath)
+    elif ext == '.gz':
+        if not os.path.exists(dirpath):
+            os.mkdir(dirpath)
+        shutil.move(filename, os.path.join(dirpath, os.path.basename(filename)))
+        print(f" | NOTE: gzip files are not extracted, and moved to {dirpath}", end="")
+    # Return the path where the file was extracted
+    print(" | Done !")
+    return os.path.abspath(dirpath)
+
+def progress_bar(current_index, max_index, prefix=None, suffix=None, start_time=None):
+    """Display a progress bar and duration.
+
+    Args:
+        current_index (int): current state index (or epoch number).
+        max_index (int): maximal numbers of state.
+        prefix (str, optional): prefix of the progress bar. The default is None.
+        suffix (str, optional): suffix of the progress bar. The default is None.
+        start_time (float, optional): starting time of the progress bar. If not None, it will display the time
+            spent from the beginning to the current state. The default is None.
+
+    Returns:
+        None. Display the progress bar in the console.
+    """
+    # Add a prefix to the progress bar
+    prefix = "" if prefix is None else str(prefix) + " "
+
+    # Get the percentage
+    percentage = current_index * 100 // max_index
+    loading = "[" + "=" * (percentage // 2) + " " * (50 - percentage // 2) + "]"
+    progress_display = "\r{0}{1:3d}% | {2}".format(prefix, percentage, loading)
+
+    # Add a suffix to the progress bar
+    progress_display += "" if suffix is None else " | " + str(suffix)
+
+    # Add a timer
+    if start_time is not None:
+        time_min, time_sec = get_time(start_time, time.time())
+        time_display = " | Time: {0}m {1}s".format(time_min, time_sec)
+        progress_display += time_display
+
+    # Print the progress bar
+    # TODO: return a string instead
+    print(progress_display, end="{}".format("" if current_index < max_index else " | Done !\n"))
+
+def get_time(start_time, end_time):
+    """Get ellapsed time in minutes and seconds.
+
+    Args:
+        start_time (float): strarting time
+        end_time (float): ending time
+
+    Returns:
+        elapsed_mins (float): elapsed time in minutes
+        elapsed_secs (float): elapsed time in seconds.
+    """
+    elapsed_time = end_time - start_time
+    elapsed_mins = int(elapsed_time / 60)
+    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))
+
+    return elapsed_mins, elapsed_secs
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..dfe3aae
--- /dev/null
+++ b/test.py
@@ -0,0 +1,75 @@
+import norch
+from norch.utils.data.dataloader import Dataloader
+import norch
+import norch.nn as nn
+import norch.optim as optim
+import random
+random.seed(1)
+
+to_tensor = lambda x: norch.Tensor(x)
+reshape = lambda x: x.reshape([-1, 784])
+transform = lambda x: reshape(to_tensor(x))
+target_transform = lambda x: to_tensor(x)
+
+train_data, test_data = norch.datasets.MNIST.splits(transform=transform, target_transform=target_transform)
+sample, _ = train_data[0]
+
+BATCH_SIZE = 100
+
+train_loader = Dataloader(train_data, batch_size = BATCH_SIZE)
+
+class MyModel(nn.Module):
+    def __init__(self):
+        super(MyModel, self).__init__()
+        self.fc1 = nn.Linear(784, 10)
+        self.sigmoid = nn.Sigmoid()
+        self.fc2 = nn.Linear(10, 1)
+
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.sigmoid(out)
+        out = self.fc2(out)
+        
+        return out
+
+device = "cpu"
+epochs = 10
+
+model = MyModel().to(device)
+criterion = nn.MSELoss()
+optimizer = optim.SGD(model.parameters(), lr=0.001)
+loss_list = []
+
+
+for epoch in range(epochs):    
+    for idx, batch in enumerate(train_loader):
+        x, target = batch
+
+        x = x.T
+        target = target.T 
+
+        x = x.to(device)
+        target = target.to(device)
+
+        outputs = model(x)
+
+        loss = criterion(outputs, target)
+        
+        optimizer.zero_grad()
+        loss.backward()
+        print('loss_antes', loss)
+
+        print('f1 antes', model.fc1.bias)
+        print('f1 grad_antes', model.fc1.bias.grad)
+        print('f2 antes', model.fc2.bias)
+        print('f2 grad_antes', model.fc2.bias.grad)
+
+        optimizer.step()
+        print('\n')
+
+        print('f1 depois', model.fc1.bias)
+        print('f2 depois', model.fc2.bias)
+        print('\n\n')
+
+    print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss[0]:.4f}')
+    loss_list.append(loss[0])
\ No newline at end of file