Issue
I want to generate a ROC curve of my trained model, but I do no know how to do this using a ImageDataGenerator()
.
I saw this link How can I plot AUC and ROC while using fit_generator and evaluate_generator to train my network?, but this only answered the question of how to get the AUC
.
I also tried it in the following way:
y_pred = model.predict_generator(test_generator, steps= step_size_test)
fpr, tpr, tresholds = roc_curve(y_pred, test_generator.classes)
This gave me an error
This is a part of my code
model.compile(loss="binary_crossentropy", optimizer= 'Adam', metrics=['accuracy', auc])
train_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
train_generator = train_datagen.flow_from_directory(
directory=f'./data/train/',
target_size=(Preprocess.image_resolution, Preprocess.image_resolution),
color_mode="grayscale",
batch_size=64,
classes=['a', 'b'],
class_mode="binary",
shuffle=True,
seed=42
)
valid_datagen = ImageDataGenerator(rescale=1.0 / 255.0)
valid_generator = valid_datagen.flow_from_directory(
directory=f'./data/valid/',
target_size=(Preprocess.image_resolution, Preprocess.image_resolution),
color_mode="grayscale",
batch_size=8,
classes=['a', 'b'],
class_mode="binary",
shuffle=True,
seed=42
)
test_datagen = ImageDataGenerator()
test_generator = test_datagen.flow_from_directory(
directory=f'./data/test/',
target_size=(Preprocess.image_resolution, Preprocess.image_resolution),
color_mode="grayscale",
batch_size=1,
classes=['a', 'b'],
class_mode='binary',
shuffle=False,
seed=42
)
step_size_train = train_generator.n // train_generator.batch_size
step_size_valid = valid_generator.n // valid_generator.batch_size
step_size_test = test_generator.n // test_generator.batch_size
model = build_three_layer_cnn_model()
history = model.fit_generator(generator=train_generator,
steps_per_epoch=step_size_train,
validation_data=valid_generator,
validation_steps=step_size_valid,
epochs=10)
Solution
The problem with your code is here :
roc_curve(y_pred, test_generator.classes)
According to the documentation of scikit-learn, you will need to pass the scores(probabilities), instead of classes as a second parameter.
Also, please note that your first parameter is y_pred
instead of y_true
.
Try by calling roc_curve(y_true,y_scores), where y_true is your ground truth and y_scores are the output probabilities by your model(i.e. model.predict(X_test))
Documentation for ROC-Curve: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html#sklearn.metrics.roc_curve
Answered By - Timbus Calin
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