Object Classification
Using the CIFAR-10 database of labeled images, train a convolutional net to predict the class of each object.
First obtain the training data.
In[1]:=
obj = ResourceObject["CIFAR-10"];
trainingData = ResourceData[obj, "TrainingData"];
RandomSample[trainingData, 5]
Out[1]=
Extract the unique classes.
In[2]:=
classes = Union@Values[trainingData]
Out[2]=
Create a convolutional net that predicts the class given an image.
In[3]:=
lenet = NetChain[
{ConvolutionLayer[20, 5], Ramp, PoolingLayer[2, 2],
ConvolutionLayer[50, 5], Ramp, PoolingLayer[2, 2], FlattenLayer[],
500, Ramp, 10, SoftmaxLayer[]},
"Output" -> NetDecoder[{"Class", classes}],
"Input" -> NetEncoder[{"Image", {32, 32}}]
]
Out[3]=
Train the net on the training data.
In[4]:=
trained = NetTrain[lenet, trainingData, MaxTrainingRounds -> 4]
Out[5]=
Predict the most likely classes for a set of images.
In[6]:=
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Out[6]=
For a specific example, give the probabilities of the most likely label assignments.
In[7]:=
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"], {{0, 32}, {32, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{32, 32},
PlotRange->{{0, 32}, {0, 32}}]\), {"TopProbabilities", 3}]
Out[7]=
From a random sample, select the images for which the net produces highest and lowest entropy predictions. High-entropy inputs can be interpreted as those for which the net is most uncertain about the correct class.
In[8]:=
images = RandomSample[Keys[trainingData], 5000];
In[9]:=
entropies = trained[images, "Entropy"];
In[10]:=
Labeled[images[[Ordering[entropies, -10]]], "high entropy"]
Labeled[images[[Ordering[entropies, 10]]], "low entropy"]
Out[10]=
Out[10]=