Wrong loss shape when using keras generators

[landoooo]

I am facing the following issue when trying to train a Squeezenet with no weights and two classes:

expected loss to have shape (None, 2) but got array with shape (64, 1)

the code has been deeply inspired by this tutorial (https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)

from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
from keras_squeezenet import SqueezeNet

img_width, img_height = 227, 227

train_data_dir = 'C:/IMAGES/KERAS/train'
validation_data_dir = 'C:/IMAGES/KERAS/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 2
batch_size = 64

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = SqueezeNet(weights=None, classes=2)

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0,
    zoom_range=0,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1. / 255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    generator=train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

In the folders train and validation there are 2 subfolders (OK and NOK), and both classes are well read by the generators. This is the output from them:

Found 40294 images belonging to 2 classes.
Found 1273 images belonging to 2 classes.

Setting classes=1 when declaring the model solves the error of the expected vs real shape of the loss vector but makes the classifier pointless.

Any idea about how to have more than 1 class when using generators?

[landoooo]

Issue here was the class_mode='binary' used on the data generators. Changing it into categorical made it work.