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vision_learner

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Jeremy Howard
2022-04-25 08:35:15 +10:00
parent b7f756b49d
commit 9f9e59790c
20 changed files with 78 additions and 54 deletions
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2
clean/01_intro.ipynb
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@@ -107,7 +107,7 @@
" path, get_image_files(path), valid_pct=0.2, seed=42,\n",
" label_func=is_cat, item_tfms=Resize(224))\n",
"\n",
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fine_tune(1)"
]
},

2
clean/02_production.ipynb
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@@ -358,7 +358,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet18, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
"learn.fine_tune(4)"
]
},

2
clean/04_mnist_basics.ipynb
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@@ -1553,7 +1553,7 @@
"outputs": [],
"source": [
"dls = ImageDataLoaders.from_folder(path)\n",
"learn = cnn_learner(dls, resnet18, pretrained=False,\n",
"learn = vision_learner(dls, resnet18, pretrained=False,\n",
" loss_func=F.cross_entropy, metrics=accuracy)\n",
"learn.fit_one_cycle(1, 0.1)"
]

14
clean/05_pet_breeds.ipynb
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@@ -178,7 +178,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fine_tune(2)"
]
},
@@ -499,7 +499,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fine_tune(1, base_lr=0.1)"
]
},
@@ -509,7 +509,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"lr_min,lr_steep = learn.lr_find(suggest_funcs=(minimum, steep))"
]
},
@@ -528,7 +528,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fine_tune(2, base_lr=3e-3)"
]
},
@@ -554,7 +554,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fit_one_cycle(3, 3e-3)"
]
},
@@ -598,7 +598,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fit_one_cycle(3, 3e-3)\n",
"learn.unfreeze()\n",
"learn.fit_one_cycle(12, lr_max=slice(1e-6,1e-4))"
@@ -634,7 +634,7 @@
"outputs": [],
"source": [
"from fastai.callback.fp16 import *\n",
"learn = cnn_learner(dls, resnet50, metrics=error_rate).to_fp16()\n",
"learn = vision_learner(dls, resnet50, metrics=error_rate).to_fp16()\n",
"learn.fine_tune(6, freeze_epochs=3)"
]
},

6
clean/06_multicat.ipynb
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@@ -295,7 +295,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet18)"
"learn = vision_learner(dls, resnet18)"
]
},
{
@@ -366,7 +366,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n",
"learn = vision_learner(dls, resnet50, metrics=partial(accuracy_multi, thresh=0.2))\n",
"learn.fine_tune(3, base_lr=3e-3, freeze_epochs=4)"
]
},
@@ -580,7 +580,7 @@
"metadata": {},
"outputs": [],
"source": [
"learn = cnn_learner(dls, resnet18, y_range=(-1,1))"
"learn = vision_learner(dls, resnet18, y_range=(-1,1))"
]
},
{

4
clean/15_arch_details.ipynb
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@@ -40,7 +40,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### cnn_learner"
"### vision_learner"
]
},
{
@@ -256,7 +256,7 @@
"1. What is `model_meta`? Try printing it to see what's inside.\n",
"1. Read the source code for `create_head` and make sure you understand what each line does.\n",
"1. Look at the output of `create_head` and make sure you understand why each layer is there, and how the `create_head` source created it.\n",
"1. Figure out how to change the dropout, layer size, and number of layers created by `cnn_learner`, and see if you can find values that result in better accuracy from the pet recognizer.\n",
"1. Figure out how to change the dropout, layer size, and number of layers created by `vision_learner`, and see if you can find values that result in better accuracy from the pet recognizer.\n",
"1. What does `AdaptiveConcatPool2d` do?\n",
"1. What is \"nearest neighbor interpolation\"? How can it be used to upsample convolutional activations?\n",
"1. What is a \"transposed convolution\"? What is another name for it?\n",

4
clean/16_accel_sgd.ipynb
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@@ -69,7 +69,7 @@
"outputs": [],
"source": [
"def get_learner(**kwargs):\n",
" return cnn_learner(dls, resnet34, pretrained=False,\n",
" return vision_learner(dls, resnet34, pretrained=False,\n",
" metrics=accuracy, **kwargs).to_fp16()"
]
},
@@ -386,7 +386,7 @@
"metadata": {},
"source": [
"1. What is the equation for a step of SGD, in math or code (as you prefer)?\n",
"1. What do we pass to `cnn_learner` to use a non-default optimizer?\n",
"1. What do we pass to `vision_learner` to use a non-default optimizer?\n",
"1. What are optimizer callbacks?\n",
"1. What does `zero_grad` do in an optimizer?\n",
"1. What does `step` do in an optimizer? How is it implemented in the general optimizer?\n",

2
clean/18_CAM.ipynb
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@@ -47,7 +47,7 @@
"dls = ImageDataLoaders.from_name_func(\n",
" path, get_image_files(path), valid_pct=0.2, seed=21,\n",
" label_func=is_cat, item_tfms=Resize(224))\n",
"learn = cnn_learner(dls, resnet34, metrics=error_rate)\n",
"learn = vision_learner(dls, resnet34, metrics=error_rate)\n",
"learn.fine_tune(1)"
]
},