"NaN" result when running multi class classification

Question

when i run these lines of code for binary classification it is running well without any problem and get a good result, but when i try to make it for many classes e.g 3 classes it give "NaN" in predict result

# Importing the Keras libraries and packages

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense


# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

# Step 2 - Pooling
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2enter code hereD(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 3, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('data/train',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('data/test',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'categorical')

classifier.fit_generator(training_set,
                         steps_per_epoch = 240 ,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 30)



import numpy as np
from keras.preprocessing import image
test_image = image.load_img('2.jpeg', target_size = (64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices

i tried these lines of code with loss function "binary" with 2 classes it worked well without any problems and get a good result that helped me with my work and the accuracy approximately '93%' .
but my project based on multi class classification, so i tried to change the loss function to 'categorical_crossentropy' and the class mod in fit_generator to 'categorical' to make it multi class, the accuracy start with 60% and grows up to 99 and suddenly drop down to 33%.

the expected result the labels of the classes the actual result is "NaN".

thanks in advance.

Answer 1

For multi-class classification, usually softmax is applied on the last dense layer instead of sigmoid. Change it to softmax to see whether the issue is still there.