Chapter 4, README updates

flourscent · Sep 16, 2019 · 6e6fa2a · 6e6fa2a
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diff --git a/04_extensions/Code.ipynb b/04_extensions/Code.ipynb
diff --git a/04_extensions/Math.ipynb b/04_extensions/Math.ipynb
diff --git a/04_extensions/img/00_weight_init_activation_input.png b/04_extensions/img/00_weight_init_activation_input.png
diff --git a/04_extensions/img/01_weight_init_activation_output.png b/04_extensions/img/01_weight_init_activation_output.png
diff --git a/04_extensions/img/Log_loss_vs_MSE_y_eq_0.png b/04_extensions/img/Log_loss_vs_MSE_y_eq_0.png
diff --git a/README.md b/README.md
@@ -7,3 +7,13 @@ It was mostly for me to keep the code I was writing for the book organized, but
 ## Structure
 
 Each chapter has two notebooks: a `Code` notebook and a `Math` notebook. Each `Code` notebook contains the Python code for corresponding chapter and can be run start to finish to generate the results from the chapters. The `Math` notebooks were just for me to store the LaTeX equations used in the book, taking advantage of Jupyter's LaTeX rendering functionality.
+
+## `lincoln`
+
+In the notebooks in the Chapters 4 and 5 folder, I import classes from `lincoln`, rather than putting those classes in the Jupyter Notebook itself. `lincoln` is not currently a `pip` installable library; th way I'd recommend to be able to `import` it and run these notebooks is to add a line like the following your `.bashrc` file:
+
+```bash
+export PYTHONPATH=$PYTHONPATH:/Users/seth/development/DLFS_code/lincoln
+```
+
+This will cause Python to search this path for a module called `lincoln` when you run the `import` command (of course, you'll have to replace the path above with the relevant path on your machine once you clone this repo). Then, simply `source` your `.bashrc` file before running the `jupyter notebook` command and you should be good to go.
diff --git a/lincoln/lincoln/conv.py b/lincoln/lincoln/conv.py
@@ -75,15 +75,14 @@ def _output(self,
 
         patches = self._get_image_patches(self.input_)
 
-        import pdb; pdb.set_trace()
         patches_reshaped = (patches
                             .transpose(1, 0, 2, 3, 4)
                             .reshape(batch_size, img_size, -1))
 
         param_reshaped = (self.param
                           .transpose(0, 2, 3, 1)
                           .reshape(patch_size, -1))
-        # import pdb; pdb.set_trace()
+
         output_reshaped = (
             np.matmul(patches_reshaped, param_reshaped)
             .reshape(batch_size, img_height, img_height, -1)

diff --git a/lincoln/lincoln/losses.py b/lincoln/lincoln/losses.py
@@ -88,8 +88,10 @@ def _output(self) -> float:
         self.softmax_preds = np.clip(softmax_preds, self.eps, 1 - self.eps)
 
         # actual loss computation
-        softmax_cross_entropy_loss = -1.0 * self.target * np.log(self.softmax_preds) - \
-            (1.0 - self.target) * np.log(1 - self.softmax_preds)
+        softmax_cross_entropy_loss = (
+            -1.0 * self.target * np.log(self.softmax_preds) - \
+                (1.0 - self.target) * np.log(1 - self.softmax_preds)
+        )
 
         return np.sum(softmax_cross_entropy_loss) / self.prediction.shape[0]