Ready for review

gallery/how_to/work_with_microtvm/micro_train.py

@@ -36,6 +36,7 @@
 # .. image:: https://raw.githubusercontent.com/guberti/web-data/micro-train-tutorial-data/images/utilities/colab_button.png
 #      :align: center
 #      :target: https://colab.research.google.com/github/guberti/tvm-site/blob/asf-site/docs/_downloads/a7c7ea4b5017ae70db1f51dd8e6dcd82/micro_train.ipynb
+#      :width: 600px
 #
 # Motivation
 # ----------
@@ -98,7 +99,8 @@
 # ^^^^^^^^^^^^^^^^^^^^^
 # We need to pick a directory where our image datasets, trained model, and eventual Arduino sketch
 # will all live. If running on Google Colab, we'll save everything in ``/root`` (aka ``~``) but you'll
-# probably want to store it elsewhere if running locally.
+# probably want to store it elsewhere if running locally. Note that this variable only affects Python
+# scripts - you'll have to adjust the Bash commands too.
 
 import os
 
@@ -172,6 +174,7 @@
 shutil.move(f"{FOLDER}/images/cars_train", f"{FOLDER}/images/target")
 shutil.move(f"{FOLDER}/images/val2017", f"{FOLDER}/images/random")
 
+######################################################################
 # Loading the Data
 # ----------------
 # Currently, our data is stored on-disk as JPG files of various sizes. To train with it, we'll have
@@ -230,8 +233,8 @@
     plt.axis("off")
 
 ######################################################################
-# What's Inside Our Dataset?
-# ^^^^^^^^^^^^^^^^^^^^^^^^^^
+# Validating our Accuracy
+# ^^^^^^^^^^^^^^^^^^^^^^^
 # While developing our model, we'll often want to check how accurate it is (e.g. to see if it
 # improves during training). How do we do this? We could just train it on *all* of the data, and
 # then ask it to classify that same data. However, our model could cheat by just memorizing all of
@@ -341,7 +344,7 @@
     loss="categorical_crossentropy",
     metrics=["accuracy"],
 )
-# model.fit(train_dataset, validation_data=validation_dataset, epochs=3, verbose=2)
+model.fit(train_dataset, validation_data=validation_dataset, epochs=3, verbose=2)
 
 ######################################################################
 # Quantization
@@ -506,7 +509,7 @@ def representative_dataset():
 #     .. code-block:: bash
 #
 #       %%bash
-#       mkdir -p /root/tests
+#       mkdir -p ~/tests
 #       curl "https://i.imgur.com/JBbEhxN.png" -o ~/tests/car_224.png
 #       convert ~/tests/car_224.png -resize 64 ~/tests/car_64.png
 #       stream ~/tests/car_64.png ~/tests/car.raw
@@ -559,7 +562,7 @@ def representative_dataset():
 # subject for a different tutorial. To finish up, we'll first test that our program compiles does
 # not throw any compiler errors:
 
-shutil.rmtree("{FOLDER}/models/project/build", ignore_errors=True)
+shutil.rmtree(f"{FOLDER}/models/project/build", ignore_errors=True)
 arduino_project.build()
 print("Compilation succeeded!")
 
@@ -572,7 +575,7 @@ def representative_dataset():
 # If you're running on Google Colab, you'll have to uncomment the last two lines to download the file
 # after writing it.
 
-ZIP_FOLDER = "{FOLDER}/models/project"
+ZIP_FOLDER = f"{FOLDER}/models/project"
 shutil.make_archive(ZIP_FOLDER, "zip", ZIP_FOLDER)
 # from google.colab import files
 # files.download(f"{FOLDER}/models/project.zip")
@@ -587,8 +590,8 @@ def representative_dataset():
 assert len(quantized_model) <= 350000  # Exact value depends on quantization
 
 # Assert .tflite and .zip files were written to disk
-assert os.path.isfile("{FOLDER}/models/quantized.tflite")
-assert os.path.isfile("{FOLDER}/models/project.zip")
+assert os.path.isfile(f"{FOLDER}/models/quantized.tflite")
+assert os.path.isfile(f"{FOLDER}/models/project.zip")
 
 # Assert MLF file was correctly generated
 assert str(mod.executor) == "aot"