microsoftml.load_image: Hiermit wird ein Bild geladen.
Verwendung
microsoftml.load_image(cols: [str, dict, list], **kargs)
BESCHREIBUNG
Lädt Bilddaten.
Details
load_image
lädt Bilder aus Pfaden.
Argumente
cols
Eine Zeichenfolge oder Liste mit den zu transformierenden Variablennamen. Wenn dict
gilt, stellen die Schlüssel die Namen der neu zu erstellenden Variablen dar.
kargs
Zusätzliche Argumente, die an die Compute-Engine gesendet werden.
Gibt zurück
Ein Objekt, das die Transformation definiert.
Siehe auch
resize_image
, extract_pixels
, featurize_image
.
Beispiel
'''
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)
Ausgabe:
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: 00:00:00.0891958
Elapsed time: 00:00:00.0095013
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 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: 00:00:01.0541236
Elapsed time: 00:00:00.0113811
rx_neural_network
Beginning processing data.
Rows Read: 1, Read Time: 0, Transform Time: 0
Beginning processing data.
Elapsed time: 00:00:00.0401500
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: 00:00:00.4957253
Finished writing 1 rows.
Writing completed.
PredictedLabel Score Probability
0 False 0.0 0.5