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change text on solution and epochs to 3
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wassname committed Nov 3, 2020
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108 changes: 70 additions & 38 deletions notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.ipynb
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Expand Up @@ -1559,6 +1559,8 @@
},
"outputs": [],
"source": [
"# We generally want out input data to be between 0 and 1 or -1 and 1\n",
"# Because most layers are build with the assumption of data that is distributed within this range\n",
"x_train = landmassf3_train.data / 255.0\n",
"y_train = landmassf3_train.targets\n",
"x_test = landmassf3_test.data / 255.0\n",
Expand All @@ -1577,11 +1579,11 @@
},
{
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"execution_count": 27,
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Expand Down Expand Up @@ -1620,11 +1622,11 @@
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"execution_count": 46,
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Expand All @@ -1636,7 +1638,8 @@
" total = 0\n",
"\n",
" for idx, image in enumerate(x):\n",
" pred = model(image.unsqueeze(0).unsqueeze(0).cuda()).argmax()\n",
" y_pred_outs = model(image.unsqueeze(0).unsqueeze(0).cuda())\n",
" pred = F.log_softmax(y_pred_outs).argmax()\n",
" if int(pred) == int(y[idx]):\n",
" correct += 1\n",
" total += 1\n",
Expand Down Expand Up @@ -2016,7 +2019,8 @@
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},
"outputs": [
{
Expand Down Expand Up @@ -2095,7 +2099,7 @@
"<div class=\"alert alert-success\" style=\"font-size:100%\">\n",
"<b>Exercise 1</b>: <br>\n",
"\n",
"Modify the previous code to train `BetterCNN` using `Adam` optimizer for a total of 10 `epochs`. Use 1e-3 `learning_rate`.\n",
"Modify the previous code to train `BetterCNN` using `Adam` optimizer for a total of 3 `epochs`. Use 1e-3 `learning_rate`.\n",
"</div>"
]
},
Expand All @@ -2110,31 +2114,18 @@
" <font size=\"4\" color=\"darkblue\"><b>See the solution for Exercise 1</b></font>\n",
"</summary>\n",
" \n",
"If we check the loss, we can notice that Adam is converging faster. However, the model is clearly underfitted. Let's train now the model for 10 epochs more:\n",
"If we check the loss, we can notice that Adam is converging faster. However, the model is clearly underfitted. Let's train now the model for 3 epochs:\n",
" \n",
"```python\n",
"learning_rate = 1e-3\n",
"convnet2 = BetterCNN().to(device)\n",
"optimizer = torch.optim.Adam(convnet2.parameters(), lr=learning_rate)\n",
"model = train(convnet2, x_train, y_train, criterion, optimizer, n_epochs=10)\n",
"test(model, x_test, y_test)\n",
"optimizer = torch.optim.Adam(convnet2.parameters(), lr=1e-3)\n",
"convnet2 = train(convnet2, x_train, y_train, criterion, optimizer, n_epochs=2)\n",
"test(convnet2, x_test, y_test)\n",
"```\n",
"\n",
"</details>"
]
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"cell_type": "markdown",
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Expand All @@ -2144,27 +2135,68 @@
},
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"execution_count": 49,
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"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/anaconda/envs/py37_pytorch/lib/python3.7/site-packages/ipykernel_launcher.py:4: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.\n",
" after removing the cwd from sys.path.\n"
]
},
{
"data": {
"text/plain": [
"tensor([2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 2, 0, 3, 2, 0, 2, 2],\n",
" device='cuda:0')"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Now that we finished the training let's save our best model\n",
"#PATH = \"./landmass_net.pth\"\n",
"#torch.save(model.state_dict(), PATH)"
"y_pred_outs = convnet2(\n",
" # Pass in first 20 images\n",
" x_test[:20].unsqueeze(1).float().to(device)\n",
")\n",
"y_pred = F.log_softmax(y_pred_outs).argmax(-1)\n",
"y_pred"
]
},
{
"cell_type": "markdown",
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"execution_count": 45,
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{
"data": {
"text/plain": [
"tensor([2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 2, 2, 0, 2, 0, 3, 2, 0, 2, 2],\n",
" dtype=torch.uint8)"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Now let's load a new model to check that the performance of the saved model.\n",
"\n",
"Check more information about how to save models in Pytorch [here](https://pytorch.org/tutorials/beginner/saving_loading_models.html)"
"# compare to the real answer\n",
"y_test[:20]"
]
},
{
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34 changes: 17 additions & 17 deletions notebooks/c02_Intro_to_NN_Part_2/Intro_to_NN_Part_2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,6 +281,8 @@ def num_flat_features(self, x):
torch.manual_seed(2020)
# -

# We generally want out input data to be between 0 and 1 or -1 and 1
# Because most layers are build with the assumption of data that is distributed within this range
x_train = landmassf3_train.data / 255.0
y_train = landmassf3_train.targets
x_test = landmassf3_test.data / 255.0
Expand Down Expand Up @@ -331,7 +333,8 @@ def test(model, x, y):
total = 0

for idx, image in enumerate(x):
pred = model(image.unsqueeze(0).unsqueeze(0).cuda()).argmax()
y_pred_outs = model(image.unsqueeze(0).unsqueeze(0).cuda())
pred = F.log_softmax(y_pred_outs).argmax()
if int(pred) == int(y[idx]):
correct += 1
total += 1
Expand Down Expand Up @@ -424,7 +427,7 @@ def forward(self, x):
# <div class="alert alert-success" style="font-size:100%">
# <b>Exercise 1</b>: <br>
#
# Modify the previous code to train `BetterCNN` using `Adam` optimizer for a total of 10 `epochs`. Use 1e-3 `learning_rate`.
# Modify the previous code to train `BetterCNN` using `Adam` optimizer for a total of 3 `epochs`. Use 1e-3 `learning_rate`.
# </div>

# You can click in the button below the reveal the solution for exercise 1
Expand All @@ -434,31 +437,28 @@ def forward(self, x):
# <font size="4" color="darkblue"><b>See the solution for Exercise 1</b></font>
# </summary>
#
# If we check the loss, we can notice that Adam is converging faster. However, the model is clearly underfitted. Let's train now the model for 10 epochs more:
# If we check the loss, we can notice that Adam is converging faster. However, the model is clearly underfitted. Let's train now the model for 3 epochs:
#
# ```python
# learning_rate = 1e-3
# convnet2 = BetterCNN().to(device)
# optimizer = torch.optim.Adam(convnet2.parameters(), lr=learning_rate)
# model = train(convnet2, x_train, y_train, criterion, optimizer, n_epochs=10)
# test(model, x_test, y_test)
# optimizer = torch.optim.Adam(convnet2.parameters(), lr=1e-3)
# convnet2 = train(convnet2, x_train, y_train, criterion, optimizer, n_epochs=2)
# test(convnet2, x_test, y_test)
# ```
#
# </details>



# Finally ! After changing the optimizer, creating a better CNN architecture and train for a couple of epochs we got an accuracy of over 99% on unseen data.

# +
# Now that we finished the training let's save our best model
#PATH = "./landmass_net.pth"
#torch.save(model.state_dict(), PATH)
# -
y_pred_outs = convnet2(
# Pass in first 20 images
x_test[:20].unsqueeze(1).float().to(device)
)
y_pred = F.log_softmax(y_pred_outs).argmax(-1)
y_pred

# Now let's load a new model to check that the performance of the saved model.
#
# Check more information about how to save models in Pytorch [here](https://pytorch.org/tutorials/beginner/saving_loading_models.html)
# compare to the real answer
y_test[:20]

# # References and further reading
#
Expand Down

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