Merge pull request #2 from mbrukman/update-python-from-notebook

Re-generate Python code from IPython notebooks

ch03/ch03.py

@@ -283,7 +283,7 @@ def loss_0(z):
 
 plt.ylim(0.0, 5.1)
 plt.xlim([0, 1])
-plt.xlabel('$\sigma$(z)')
+plt.xlabel('$\sigma(z)$')
 plt.ylabel('L(w, b)')
 plt.legend(loc='best')
 plt.tight_layout()

ch04/ch04.py

@@ -448,6 +448,7 @@
 
 
 
+
 LogisticRegression(penalty='l1')
 
 

ch07/ch07.py

@@ -890,3 +890,8 @@ def get_params(self, deep=True):
 
 
 
+
+
+
+
+

ch08/ch08.py

@@ -700,7 +700,7 @@ def get_minibatch(doc_stream, size):
 
 
 n_top_words = 5
-feature_names = count.get_feature_names()
+feature_names = count.get_feature_names_out()
 
 for topic_idx, topic in enumerate(lda.components_):
     print(f'Topic {(topic_idx + 1)}:')

ch09/ch09.py

@@ -877,3 +877,8 @@ def mean_absolute_deviation(data):
 
 
 
+
+
+
+
+

ch13/ch13_part1.py

@@ -579,3 +579,8 @@ def predict(self, x):
 
 
 
+
+
+
+
+

ch13/ch13_part3_lightning.py

@@ -68,7 +68,7 @@
 
 
 class MultiLayerPerceptron(pl.LightningModule):
-    def __init__(self,image_shape=(1, 28, 28), hidden_units=(32, 16)):
+    def __init__(self, image_shape=(1, 28, 28), hidden_units=(32, 16)):
         super().__init__()
 
         # new PL attributes:

ch13/ch13_part4_ignite.py

@@ -1,6 +1,8 @@
 # coding: utf-8
 
 
+import sys
+from python_environment_check import check_packages
 import torch 
 import torch.nn as nn 
 from torch.utils.data import DataLoader 
@@ -11,6 +13,37 @@
 from ignite.handlers import Checkpoint, DiskSaver
 from ignite.contrib.handlers import TensorboardLogger, global_step_from_engine
 
+
+
+
+
+
+# # Machine Learning with PyTorch and Scikit-Learn  
+# # -- Code Examples
+
+# ## Package version checks
+
+# Add folder to path in order to load from the check_packages.py script:
+
+
+
+sys.path.insert(0, '..')
+
+
+# Check recommended package versions:
+
+
+
+
+
+d = {
+    'numpy': '1.21.2',
+    'matplotlib': '3.4.3',
+    'sklearn': '1.0',
+}
+check_packages(d)
+
+
 # # Chapter 13: Going Deeper -- the Mechanics of PyTorch
 
 # **Big thanks and credit to Victor Fomin for creating and helping with the original draft of this section!**

ch15/ch15_part2.py

@@ -410,8 +410,3 @@ def forward(self, text, lengths):
 
 
 
-
-
-
-
-

ch15/ch15_part3.py

@@ -179,7 +179,7 @@ def __getitem__(self, idx):
 
 class RNN(nn.Module):
     def __init__(self, vocab_size, embed_dim, rnn_hidden_size):
-        super(RNN, self).__init__()
+        super().__init__()
         self.embedding = nn.Embedding(vocab_size, embed_dim) 
         self.rnn_hidden_size = rnn_hidden_size
         self.rnn = nn.LSTM(embed_dim, rnn_hidden_size, 
@@ -365,8 +365,3 @@ def sample(model, starting_str,
 
 
 
-
-
-
-
-

ch17/ch17_part1.py

@@ -443,7 +443,7 @@ def create_samples(g_model, input_z):
 ax.set_xlabel('Iteration', size=15)
 ax.set_ylabel('Discriminator output', size=15)
 
-#plt.savefig('images/ch17-gan-learning-curve.pdf')
+#plt.savefig('figures/ch17-gan-learning-curve.pdf')
 plt.show()
 
 
@@ -467,7 +467,7 @@ def create_samples(g_model, input_z):
         image = epoch_samples[e-1][j]
         ax.imshow(image, cmap='gray_r')
 
-#plt.savefig('images/ch17-vanila-gan-samples.pdf')
+#plt.savefig('figures/ch17-vanila-gan-samples.pdf')
 plt.show()
 
 
@@ -546,3 +546,8 @@ def compute_score(fake, real , k=1, sigma=1, sqrt=True):
 
 
 
+
+
+
+
+

ch17/ch17_part2.py

@@ -1,6 +1,8 @@
 # coding: utf-8
 
 
+import sys
+from python_environment_check import check_packages
 #from google.colab import drive
 import torch
 import torch.nn as nn
@@ -11,11 +13,33 @@
 from torch.utils.data import DataLoader
 from torch.autograd import grad as torch_grad
 
-# *Python Machine Learning, PyTorch Edition* by [Sebastian Raschka](https://sebastianraschka.com), [Yuxi (Hayden) Liu](https://www.mlexample.com/) & [Vahid Mirjalili](http://vahidmirjalili.com), Packt Publishing Ltd. 2021
-# 
-# Code Repository: 
-# 
-# Code License: [MIT License]()
+# # Machine Learning with PyTorch and Scikit-Learn  
+# # -- Code Examples
+
+# ## Package version checks
+
+# Add folder to path in order to load from the check_packages.py script:
+
+
+
+sys.path.insert(0, '..')
+
+
+# Check recommended package versions:
+
+
+
+
+
+d = {
+    'torch': '1.8.0',
+    'torchvision': '0.9.0',
+    'numpy': '1.21.2',
+    'matplotlib': '3.4.3',
+}
+
+check_packages(d)
+
 
 # # Chapter 17 - Generative Adversarial Networks for Synthesizing New Data (Part 2/2)
 
@@ -80,6 +104,8 @@
 
 
 
+
+
 print(torch.__version__)
 print("GPU Available:", torch.cuda.is_available())
 
@@ -93,10 +119,13 @@
 
 
 
+
 # ## Train the DCGAN model
 
 
 
+
+
 image_path = './'
 transform = transforms.Compose([
     transforms.ToTensor(),
@@ -140,7 +169,7 @@ def make_generator_network(input_size, n_filters):
 
 class Discriminator(nn.Module):
     def __init__(self, n_filters):
-        super(Discriminator, self).__init__()
+        super().__init__()
         self.network = nn.Sequential(
             nn.Conv2d(1, n_filters, 4, 2, 1, bias=False),
             nn.LeakyReLU(0.2),
@@ -292,7 +321,7 @@ def create_samples(g_model, input_z):
         image = epoch_samples[e-1][j]
         ax.imshow(image, cmap='gray_r')
 
-# plt.savefig('images/ch17-dcgan-samples.pdf')
+# plt.savefig('figures/ch17-dcgan-samples.pdf')
 plt.show()
 
 
@@ -335,7 +364,7 @@ def make_generator_network_wgan(input_size, n_filters):
 
 class DiscriminatorWGAN(nn.Module):
     def __init__(self, n_filters):
-        super(DiscriminatorWGAN, self).__init__()
+        super().__init__()
         self.network = nn.Sequential(
             nn.Conv2d(1, n_filters, 4, 2, 1, bias=False),
             nn.LeakyReLU(0.2),
@@ -368,6 +397,7 @@ def forward(self, input):
 
 
 
+
 def gradient_penalty(real_data, generated_data):
     batch_size = real_data.size(0)
 
@@ -474,7 +504,7 @@ def g_train_wgan(x):
         image = epoch_samples_wgan[e-1][j]
         ax.imshow(image, cmap='gray_r')
 
-# plt.savefig('images/ch17-wgan-gp-samples.pdf')
+# plt.savefig('figures/ch17-wgan-gp-samples.pdf')
 plt.show()
 
 
@@ -496,8 +526,3 @@ def g_train_wgan(x):
 
 
 
-
-
-
-
-

ch18/ch18_part2.py

@@ -230,8 +230,3 @@ def forward(self, data):
 
 
 
-
-
-
-
-

update_python_from_notebook.sh

@@ -0,0 +1,12 @@
+#!/bin/bash
+
+for ch in ch[0-9][0-9] ; do
+  pushd "${ch}"
+  for ipynb in *.ipynb ; do
+    py="${ipynb/ipynb/py}"
+    if [ -e "${py}" ]; then
+      python ../.convert_notebook_to_script.py --input "${ipynb}" --output "${py}"
+    fi
+  done
+  popd
+done