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fix typo in instructions
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‎intro-to-pytorch/Part 2 - Neural Networks in PyTorch (Exercises).ipynb

Lines changed: 58 additions & 21 deletions
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@@ -12,7 +12,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Import necessary packages\n",
@@ -45,7 +47,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"### Run this cell\n",
@@ -79,7 +83,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"dataiter = iter(trainloader)\n",
@@ -99,7 +105,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"plt.imshow(images[1].numpy().squeeze(), cmap='Greys_r');"
@@ -121,7 +129,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"## Your solution\n",
@@ -153,7 +163,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"def softmax(x):\n",
@@ -180,7 +192,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from torch import nn"
@@ -189,7 +203,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class Network(nn.Module):\n",
@@ -231,7 +247,7 @@
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"self.hidden = nn.Linear(784, 256)\n",
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"```\n",
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"\n",
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"This line creates a module for a linear transformation, $x\\mathbf{W} + b$, with 784 inputs and 256 outputs and assigns it to `self.hidden`. The module automatically creates the weight and bias tensors which we'll use in the `forward` method. You can access the weight and bias tensors once the network once it's create at `net.hidden.weight` and `net.hidden.bias`.\n",
250+
"This line creates a module for a linear transformation, $x\\mathbf{W} + b$, with 784 inputs and 256 outputs and assigns it to `self.hidden`. The module automatically creates the weight and bias tensors which we'll use in the `forward` method. You can access the weight and bias tensors once the network (`net`) is created with `net.hidden.weight` and `net.hidden.bias`.\n",
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"\n",
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"```python\n",
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"self.output = nn.Linear(256, 10)\n",
@@ -267,7 +283,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Create the network and look at it's text representation\n",
@@ -285,7 +303,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import torch.nn.functional as F\n",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"scrolled": true
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},
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"outputs": [],
@@ -354,7 +375,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"print(model.fc1.weight)\n",
@@ -371,7 +394,9 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Set biases to all zeros\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# sample from random normal with standard dev = 0.01\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Grab some data \n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Hyperparameters for our network\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"print(model[0])\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from collections import OrderedDict\n",
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"print(model[0])\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.6.4"
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}
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},
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"nbformat": 4,

‎intro-to-pytorch/Part 2 - Neural Networks in PyTorch (Solution).ipynb

Lines changed: 21 additions & 9 deletions
Original file line numberDiff line numberDiff line change
@@ -12,7 +12,9 @@
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Import necessary packages\n",
@@ -45,7 +47,9 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"### Run this cell\n",
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"## Solution\n",
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from torch import nn"
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"class Network(nn.Module):\n",
@@ -288,7 +298,7 @@
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"self.hidden = nn.Linear(784, 256)\n",
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"```\n",
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"\n",
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"This line creates a module for a linear transformation, $x\\mathbf{W} + b$, with 784 inputs and 256 outputs and assigns it to `self.hidden`. The module automatically creates the weight and bias tensors which we'll use in the `forward` method. You can access the weight and bias tensors once the network once it's create at `net.hidden.weight` and `net.hidden.bias`.\n",
301+
"This line creates a module for a linear transformation, $x\\mathbf{W} + b$, with 784 inputs and 256 outputs and assigns it to `self.hidden`. The module automatically creates the weight and bias tensors which we'll use in the `forward` method. You can access the weight and bias tensors once the network (`net`) is created with `net.hidden.weight` and `net.hidden.bias`.\n",
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"\n",
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"```python\n",
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"self.output = nn.Linear(256, 10)\n",
@@ -358,7 +368,9 @@
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import torch.nn.functional as F\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.6"
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"version": "3.6.4"
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}
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},
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"nbformat": 4,

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