Keras:Unable to add Dense layer to VGG16 - machine-learning

Question

I am trying to fine-tune the last convolution block of vgg16 (imagenet pretrained) with a few dense layers added on top. My code is below. I am not able to figure out why I get this error upon execution Error when checking target: expected sequential_9 to have shape (None, 11) but got array with shape (4, 1). The number of classes in my dataset is 11 and the batch size is 4. Am I somehow mixing these two? Please help.
def finetune( epochs):
num_classes = 11
batch_size = 4
base_model = VGG16(weights='imagenet', include_top=False, input_shape = (224,224,3))
print('Model loaded.')
print(base_model.output_shape[1:])
top_model = Sequential()
top_model.add(Flatten(input_shape=base_model.output_shape[1:]))
top_model.add(Dense(512, activation='relu',kernel_regularizer=regularizers.l2(0.01)))
top_model.add(Dropout(0.25))
top_model.add(Dense(256, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
top_model.add(Dropout(0.25))
top_model.add(Dense(num_classes, activation='softmax'))
top_model.load_weights('vgg_ft_best.h5')
# add the model on top of the convolutional base
#model = Model(inputs= base_model.input, outputs= top_model(base_model.output))
#base_model.add(top_model)
#print(base_model.summary())
new_model = Sequential()
for l in base_model.layers:
new_model.add(l)
# CONCATENATE THE TWO MODELS
new_model.add(top_model)
print(new_model.summary())
# set the first 10 layers (up to the last conv block)
# to non-trainable (weights will not be updated)
for layer in new_model.layers[:11]:
layer.trainable = False
# prepare data augmentation configuration
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
train_data_dir = "./images/train"
validation_data_dir = "./images/validation"
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(224, 224),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(224, 224),
batch_size=batch_size,
class_mode='binary')
num_train_samples = len(train_generator.filenames)
num_validation_samples = len(validation_generator.filenames)
print(num_validation_samples)
new_model.compile(loss='categorical_crossentropy',
optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
metrics=['accuracy'])
# fine-tune the model
new_model.fit_generator(
train_generator,
steps_per_epoch=int(num_train_samples/batch_size),
epochs=epochs,
validation_data=validation_generator,
validation_steps = int(num_validation_samples/batch_size))

Answer 1

The problem is your data (target = true outputs, true labels, etc.).
Your target has shape (batch,1), and your model with 11 classes is expecting (batch,11)
So, the problem lies in your generator. It must output tensors with 11 classes.
For that, see the documentation for flow_from_directory and the highlighted parts:
classes: optional list of class subdirectories (e.g. ['dogs', 'cats']). Default: None. If not provided, the list of classes will be automatically inferred from the subdirectory names/structure under directory, where each subdirectory will be treated as a different class (and the order of the classes, which will map to the label indices, will be alphanumeric). The dictionary containing the mapping from class names to class indices can be obtained via the attribute class_indices.
class_mode: one of "categorical", "binary", "sparse", "input" or None. Default: "categorical". Determines the type of label arrays that are returned: "categorical" will be 2D one-hot encoded labels, "binary" will be 1D binary labels, "sparse" will be 1D integer labels, "input" will be images identical to input images (mainly used to work with autoencoders). If None, no labels are returned (the generator will only yield batches of image data, which is useful to use model.predict_generator(), model.evaluate_generator(), etc.). Please note that in case of class_mode None, the data still needs to reside in a subdirectory of directory for it to work correctly.
Solution
You need to dispose your images in 11 different folders, each folder being a different class.
You need to use class_mode='categorical' to have the (batch,11) format.
Now, if your classes are not categorical (one image can have two or more classes), then you need to create your own custom generator.