I have been trying to follow Francois example of a binary image classifier of cats and dogs. I have attempted to follow his example in another similar set in kaggle (https://www.kaggle.com/playlist/men-women-classification) and I want to achieve the following
- Visualise the predictions that are wrong
- Come out with the classification report
I already have a model with around 85% accuracy on the validation set but I want to know roughly what kind of images my model is getting wrong as well as coming up with a classification report with sklearn.metric's classification report.
However I do not know how does the image generator works and have a big problem trying to know how to pair the predictions with the labels of the test images.
from sklearn.metrics import classification_report
new_test_datagen = test_datagen.flow_from_directory(
directory = test_dir,
target_size=(150,150),
batch_size=1,
class_mode='binary',
seed = 42,
)
train_image = new_train_generator.next()
plt.imshow(train_image[0].reshape(150,150,-1))
print(train_image[1])
#I want to output images but I am not sure if this is the most efficient way of doing it
predictions = model.predict(test_generator)
predictions.shape
#The predictions is a numpy array of length 476 but I do not know what are the 'correct' labels found in my test set to validate it against this output.
model.evaluate(test_generator)
# [0.3109202980995178, 0.8886554837226868]
