microsoftml.load_image: Mengubah gambar menjadi fitur

Artikel
05/23/2023

Penggunaan

microsoftml.featurize_image(cols: [dict, str], dnn_model: ['Resnet18',
    'Resnet50', 'Resnet101', 'Alexnet'] = 'Resnet18', **kargs)

Deskripsi

Menampilkan gambar menggunakan model jaringan neural dalam yang telah dilatih sebelumnya.

Detail

featurize_image menampilkan gambar menggunakan model jaringan neural dalam yang telah dilatih sebelumnya yang ditentukan. Variabel input untuk transformasi ini harus diekstrak nilai piksel.

Argumen

Cols

Variabel input yang berisi nilai piksel yang diekstrak. Jika dict, kunci mewakili nama variabel baru yang akan dibuat.

dnn_model

Jaringan neural dalam yang telah dilatih sebelumnya. Opsi yang mungkin adalah:

"Resnet18"
"Resnet50"
"Resnet101"
"Alexnet"

Nilai defaultnya adalah "Resnet18". Lihat Pembelajaran Residu Mendalam untuk Pengenalan Gambar untuk detail tentang ResNet.

karg

Argumen tambahan dikirim ke mesin komputasi.

Mengembalikan

Objek yang menentukan transformasi.

Lihat juga

load_image, resize_image, extract_pixels.

Contoh

'''
Example with images.
'''
import numpy
import pandas
from microsoftml import rx_neural_network, rx_predict, rx_fast_linear
from microsoftml import load_image, resize_image, extract_pixels
from microsoftml.datasets.image import get_RevolutionAnalyticslogo

train = pandas.DataFrame(data=dict(Path=[get_RevolutionAnalyticslogo()], Label=[True]))

# Loads the images from variable Path, resizes the images to 1x1 pixels
# and trains a neural net.
model1 = rx_neural_network("Label ~ Features", data=train, 
            ml_transforms=[            
                    load_image(cols=dict(Features="Path")), 
                    resize_image(cols="Features", width=1, height=1, resizing="Aniso"), 
                    extract_pixels(cols="Features")], 
            ml_transform_vars=["Path"], 
            num_hidden_nodes=1, num_iterations=1)

# Featurizes the images from variable Path using the default model, and trains a linear model on the result.
# If dnnModel == "AlexNet", the image has to be resized to 227x227.
model2 = rx_fast_linear("Label ~ Features ", data=train, 
            ml_transforms=[            
                    load_image(cols=dict(Features="Path")), 
                    resize_image(cols="Features", width=224, height=224), 
                    extract_pixels(cols="Features")], 
            ml_transform_vars=["Path"], max_iterations=1)

# We predict even if it does not make too much sense on this single image.
print("\nrx_neural_network")
prediction1 = rx_predict(model1, data=train)
print(prediction1)

print("\nrx_fast_linear")
prediction2 = rx_predict(model2, data=train)
print(prediction2)

Output:

Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Using: AVX Math

***** Net definition *****
  input Data [3];
  hidden H [1] sigmoid { // Depth 1
    from Data all;
  }
  output Result [1] sigmoid { // Depth 0
    from H all;
  }
***** End net definition *****
Input count: 3
Output count: 1
Output Function: Sigmoid
Loss Function: LogLoss
PreTrainer: NoPreTrainer
___________________________________________________________________
Starting training...
Learning rate: 0.001000
Momentum: 0.000000
InitWtsDiameter: 0.100000
___________________________________________________________________
Initializing 1 Hidden Layers, 6 Weights...
Estimated Pre-training MeanError = 0.707823
Iter:1/1, MeanErr=0.707823(0.00%), 0.01M WeightUpdates/sec
Done!
Estimated Post-training MeanError = 0.707499
___________________________________________________________________
Not training a calibrator because it is not needed.
Elapsed time: 00:00:00.0751759
Elapsed time: 00:00:00.0080433
Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0.001, Transform Time: 0
Beginning processing data.
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Using 2 threads to train.
Automatically choosing a check frequency of 2.
Auto-tuning parameters: L2 = 5.
Auto-tuning parameters: L1Threshold (L1/L2) = 1.
Using model from last iteration.
Not training a calibrator because it is not needed.
Elapsed time: 00:00:01.0104773
Elapsed time: 00:00:00.0106935

rx_neural_network
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0420328
Finished writing 1 rows.
Writing completed.
  PredictedLabel     Score  Probability
0          False -0.028504     0.492875

rx_fast_linear
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.4449623
Finished writing 1 rows.
Writing completed.
  PredictedLabel  Score  Probability
0          False    0.0          0.5

Bagikan melalui