diff --git a/07_sizing_and_tta.ipynb b/07_sizing_and_tta.ipynb index 9db6b84..34f9ef3 100644 --- a/07_sizing_and_tta.ipynb +++ b/07_sizing_and_tta.ipynb @@ -179,7 +179,7 @@ } ], "source": [ - "model = xresnet50()\n", + "model = xresnet50(n_out=dls.c)\n", "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n", "learn.fit_one_cycle(5, 3e-3)" ] @@ -358,7 +358,7 @@ } ], "source": [ - "model = xresnet50()\n", + "model = xresnet50(n_out=dls.c)\n", "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), metrics=accuracy)\n", "learn.fit_one_cycle(5, 3e-3)" ] @@ -472,7 +472,7 @@ ], "source": [ "dls = get_dls(128, 128)\n", - "learn = Learner(dls, xresnet50(), loss_func=CrossEntropyLossFlat(), \n", + "learn = Learner(dls, xresnet50(n_out=dls.c), loss_func=CrossEntropyLossFlat(), \n", " metrics=accuracy)\n", "learn.fit_one_cycle(4, 3e-3)" ] @@ -846,7 +846,7 @@ "Here is how we train a model with Mixup:\n", "\n", "```python\n", - "model = xresnet50()\n", + "model = xresnet50(n_out=dls.c)\n", "learn = Learner(dls, model, loss_func=CrossEntropyLossFlat(), \n", " metrics=accuracy, cbs=MixUp())\n", "learn.fit_one_cycle(5, 3e-3)\n", @@ -937,7 +937,7 @@ "To use this in practice, we just have to change the loss function in our call to `Learner`:\n", "\n", "```python\n", - "model = xresnet50()\n", + "model = xresnet50(n_out=dls.c)\n", "learn = Learner(dls, model, loss_func=LabelSmoothingCrossEntropy(), \n", " metrics=accuracy)\n", "learn.fit_one_cycle(5, 3e-3)\n", @@ -1019,8 +1019,20 @@ "display_name": "Python 3", "language": "python", "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +}