pushtaev: part 1 solution

Author: VadimPushtaev

intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb

@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -119,11 +119,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.1595]])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## Calculate the output of this network using the weights and bias tensors"
+    "## Calculate the output of this network using the weights and bias tensors\n",
+    "\n",
+    "activation((features*weights).sum() + bias)"
    ]
   },
   {
@@ -162,11 +175,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.1595]])"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## Calculate the output of this network using matrix multiplication"
+    "## Calculate the output of this network using matrix multiplication\n",
+    "\n",
+    "activation(torch.mm(features, weights.view(5,1)) + bias)"
    ]
   },
   {
@@ -204,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -238,11 +264,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tensor([[0.3171]])"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "## Your solution here"
+    "## Your solution here\n",
+    "\n",
+    "H = activation(torch.mm(features, W1) + B1)\n",
+    "activation(torch.mm(H, W2) + B2)"
    ]
   },
   {
@@ -325,7 +365,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -339,7 +379,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.6"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,