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fixes typos

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lgvaz 2020-03-31 13:25:40 -03:00
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commit 189c691788
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11_midlevel_data.ipynb
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@ -941,7 +941,7 @@
"source": [
"A *Siamese model* takes two images and has to determine if they are of the same class or not. For this example, we will use the pets dataset again, and prepare the data for a model that will have to predict if two images of pets are of the same breed or not. We will explain here how to prepare the data for such a model, then we will train that model in <<chapter_arch_details>>.\n",
"\n",
"Firs things first, let's get the images in our dataset."
"First things first, let's get the images in our dataset."
]
},
{
@ -1098,7 +1098,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Then here is our main transform. For each image, il will, with a probability of 0.5, draw an image from the same class and return a `SiameseImage` with a true label, or draw an image from another class and a return a `SiameseImage` with a false label. This is all done in the private `_draw` function. There is one difference between the training and validation set, which is why the transform needs to be initialized with the splits: on the training set, we will make that random pick each time we read an image, whereas on the validation set, we make this random pick once and for all at initialization. This way, we get more varied samples during training, but always the same validation set."
"Then here is our main transform. For each image, it will, with a probability of 0.5, draw an image from the same class and return a `SiameseImage` with a true label, or draw an image from another class and a return a `SiameseImage` with a false label. This is all done in the private `_draw` function. There is one difference between the training and validation set, which is why the transform needs to be initialized with the splits: on the training set, we will make that random pick each time we read an image, whereas on the validation set, we make this random pick once and for all at initialization. This way, we get more varied samples during training, but always the same validation set."
]
},
{
@ -1275,7 +1275,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
"version": "3.7.6"
}
},
"nbformat": 4,