Projects in this repo do not refer to current Data Science Nanodegree but refer to the projects in the current Intro To Machine Learning Nanodegree

This caused a little bit of confusion when first cloning the repo and starting on projects within the Intro To Machine Learning Nanodegree. Both the README.md files and the notebook files are out of date.

Importing the module with
from sklearn.cross_validation import train_test_split
does not work anymore.
from sklearn.model_selection import train_test_split
works.

This Error occurs while running the Solution given in Notebook

Error

Error in callback <function install_repl_displayhook..post_execute at 0x7f60a397e6a8> (for post_execute):

ValueError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py in post_execute()
145 def post_execute():
146 if matplotlib.is_interactive():
--> 147 draw_all()
148
149 # IPython >= 2

/opt/conda/lib/python3.6/site-packages/matplotlib/_pylab_helpers.py in draw_all(cls, force)
148 for f_mgr in cls.get_all_fig_managers():
149 if force or f_mgr.canvas.figure.stale:
--> 150 f_mgr.canvas.draw_idle()
151
152 atexit.register(Gcf.destroy_all)

/opt/conda/lib/python3.6/site-packages/matplotlib/backend_bases.py in draw_idle(self, *args, **kwargs)
2051 if not self._is_idle_drawing:
2052 with self._idle_draw_cntx():
-> 2053 self.draw(*args, **kwargs)
2054
2055 def draw_cursor(self, event):

/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in draw(self)
428 if toolbar:
429 toolbar.set_cursor(cursors.WAIT)
--> 430 self.figure.draw(self.renderer)
431 finally:
432 if toolbar:

/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:

/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py in draw(self, renderer)
1293
1294 mimage._draw_list_compositing_images(
-> 1295 renderer, self, artists, self.suppressComposite)
1296
1297 renderer.close_group('figure')

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together

/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:

/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
2397 renderer.stop_rasterizing()
2398
-> 2399 mimage._draw_list_compositing_images(renderer, self, artists)
2400
2401 renderer.close_group('axes')

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together

/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in draw(self, renderer, *args, **kwargs)
546 else:
547 im, l, b, trans = self.make_image(
--> 548 renderer, renderer.get_image_magnification())
549 if im is not None:
550 renderer.draw_image(gc, l, b, im)

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in make_image(self, renderer, magnification, unsampled)
772 return self._make_image(
773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification,
--> 774 unsampled=unsampled)
775
776 def _check_unsampled_image(self, renderer):

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)
464 # (of int or float)
465 # or an RGBA array of re-sampled input
--> 466 output = self.to_rgba(output, bytes=True, norm=False)
467 # output is now a correctly sized RGBA array of uint8
468

/opt/conda/lib/python3.6/site-packages/matplotlib/cm.py in to_rgba(self, x, alpha, bytes, norm)
255 if xx.dtype.kind == 'f':
256 if norm and xx.max() > 1 or xx.min() < 0:
--> 257 raise ValueError("Floating point image RGB values "
258 "must be in the 0..1 range.")
259 if bytes:

ValueError: Floating point image RGB values must be in the 0..1 range.

ValueError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/IPython/core/formatters.py in call(self, obj)
339 pass
340 else:
--> 341 return printer(obj)
342 # Finally look for special method names
343 method = get_real_method(obj, self.print_method)

/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py in (fig)
241 png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
242 if 'retina' in formats or 'png2x' in formats:
--> 243 png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))
244 if 'jpg' in formats or 'jpeg' in formats:
245 jpg_formatter.for_type(Figure, lambda fig: print_figure(fig, 'jpg', **kwargs))

/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py in retina_figure(fig, **kwargs)
131 def retina_figure(fig, **kwargs):
132 """format a figure as a pixel-doubled (retina) PNG"""
--> 133 pngdata = print_figure(fig, fmt='retina', **kwargs)
134 # Make sure that retina_figure acts just like print_figure and returns
135 # None when the figure is empty.

/opt/conda/lib/python3.6/site-packages/IPython/core/pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
123
124 bytes_io = BytesIO()
--> 125 fig.canvas.print_figure(bytes_io, **kw)
126 data = bytes_io.getvalue()
127 if fmt == 'svg':

/opt/conda/lib/python3.6/site-packages/matplotlib/backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
2206 orientation=orientation,
2207 dryrun=True,
-> 2208 **kwargs)
2209 renderer = self.figure._cachedRenderer
2210 bbox_inches = self.figure.get_tightbbox(renderer)

/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
505
506 def print_png(self, filename_or_obj, *args, **kwargs):
--> 507 FigureCanvasAgg.draw(self)
508 renderer = self.get_renderer()
509 original_dpi = renderer.dpi

/opt/conda/lib/python3.6/site-packages/matplotlib/backends/backend_agg.py in draw(self)
428 if toolbar:
429 toolbar.set_cursor(cursors.WAIT)
--> 430 self.figure.draw(self.renderer)
431 finally:
432 if toolbar:

/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:

/opt/conda/lib/python3.6/site-packages/matplotlib/figure.py in draw(self, renderer)
1293
1294 mimage._draw_list_compositing_images(
-> 1295 renderer, self, artists, self.suppressComposite)
1296
1297 renderer.close_group('figure')

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together

/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:

/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py in draw(self, renderer, inframe)
2397 renderer.stop_rasterizing()
2398
-> 2399 mimage._draw_list_compositing_images(renderer, self, artists)
2400
2401 renderer.close_group('axes')

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
136 if not_composite or not has_images:
137 for a in artists:
--> 138 a.draw(renderer)
139 else:
140 # Composite any adjacent images together

/opt/conda/lib/python3.6/site-packages/matplotlib/artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
53 renderer.start_filter()
54
---> 55 return draw(artist, renderer, *args, **kwargs)
56 finally:
57 if artist.get_agg_filter() is not None:

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in draw(self, renderer, *args, **kwargs)
546 else:
547 im, l, b, trans = self.make_image(
--> 548 renderer, renderer.get_image_magnification())
549 if im is not None:
550 renderer.draw_image(gc, l, b, im)

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in make_image(self, renderer, magnification, unsampled)
772 return self._make_image(
773 self._A, bbox, transformed_bbox, self.axes.bbox, magnification,
--> 774 unsampled=unsampled)
775
776 def _check_unsampled_image(self, renderer):

/opt/conda/lib/python3.6/site-packages/matplotlib/image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)
464 # (of int or float)
465 # or an RGBA array of re-sampled input
--> 466 output = self.to_rgba(output, bytes=True, norm=False)
467 # output is now a correctly sized RGBA array of uint8
468

/opt/conda/lib/python3.6/site-packages/matplotlib/cm.py in to_rgba(self, x, alpha, bytes, norm)
255 if xx.dtype.kind == 'f':
256 if norm and xx.max() > 1 or xx.min() < 0:
--> 257 raise ValueError("Floating point image RGB values "
258 "must be in the 0..1 range.")
259 if bytes:

ValueError: Floating point image RGB values must be in the 0..1 range.

<matplotlib.figure.Figure at 0x7f607466c780>

Code contains the following import statement
from sklearn.cross_validation import train_test_split

sklearn.cross_validation does not exist. Code should be
from sklearn.model_selection import train_test_split

To me it seems as if there was an error in Notebook(s) "Part 3 - Training neural networks":
When executing Code-Cell 1 with transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), ]) Code-Cell 2 produces an error stating RuntimeError: output with shape [1, 28, 28] doesn't match the broadcast shape [3, 28, 28].

In my opinion it should be transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)), ]) (like in Part 2).

Thanks for checking and amending if necessary!

I can't see any cloning option