Why is my Neural Net not learning

Question

I have a CNN that I'm trying to train and I cant figure out why its not learning. It has 32 classes which are different types of clothes of about 1000 images in each folder.

Issue is this is the result at the end of training which takes about 9 hours on my GPU

loss: 3.3403 - acc: 0.0542 - val_loss: 3.3387 - val_acc: 0.0534

If anyone could give me directions on how to get this network to train better I would be grateful.

# dimensions of our images.
img_width, img_height = 228, 228

train_data_dir = 'Clothes/train'
validation_data_dir = 'Clothes/test'
nb_train_samples = 25061
nb_validation_samples = 8360
epochs = 20
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 = Sequential()
model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='tanh', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
model.add(Conv2D(filters=32, kernel_size=2, padding='same', activation='tanh'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(32, activation='softmax'))

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

train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    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='categorical',
    shuffle = True)

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical',
    shuffle = True)

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

Here is the plot of the training & validation loss

Answer 1

A network may not converge/learn for several reasons, but here is a list of tips that I think is relevant in your case (based on my own experience):

• Transfer Learning: The first thing you should know is that it's very hard to train an image classifier from scratch for most problems, you need much more computing power and time for that. I strongly recommend using transfer learning. There are multiple trained architectures available in Keras that you can use as initial weights fr your network (or other methods).

• Training step: For the optimizer, I recommend to use Adam first and to vary the learning rate to see how the loss responds. Also, since you are using Convolutional Layers, you should consider adding Batch Normalization Layers, that can speed significantly the training time, and change the Convolutional activations to 'relu', which make them much faster to train.

  You could also try decreasing the Dropout values but I don't think that's the main issue here. Also, If you are considering training your network from scratch, you should start with fewer layers and add more gradually to get a better idea of ​​what's going on.

• train/test split: I see that you are using 8360 observations in your test set. Given the size of your training set, I think it's too much. 1000 for example is enough. The more training samples you have, the more satisfying your results will be.

Also, before judging the accuracy of your model, you should start by establishing a baseline model to benchmark your model. The baseline model depends on your problem, but in general I choose a model that predicts the most common class in the dataset. You should also look at another metric 'top_k_accuracy' available in Keras that is interesting when you have a relatively high number of classes to predict. It helps you to see how close your model is to the right prediction.

Answer 2

First, in order to keep your sanity, check carefully for any bugs, and that your data is being sent in as intended

You might want to add a Top K accuracy metric to get a better idea of whether it's close to getting it, or totally wrong.

Here are some tuning things to try:

Change the kernel size to 3 and activation to relu

model.add(Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'))

If you think your model is underfitting then try increasing the number of Conv layers per pooling to start with. But you could also increase the number of filters or the number of conv + pool repetitions.

Adam optimizer might learn a bit faster than RMS prop

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

Probably the biggest improvement would be to get more data. I think your data set is probably too small for the scope of the problem. You might want to try transfer learning from a pre-trained image recognition network.