Tukukkk

I'm using Keras to fine tune an existing VGG16 model and am using a fit_generator to train the last 4 layers. Here's the relevant code that I'm working with:

# Create the model

model = models.Sequential()



# Add the vgg convolutional base model

model.add(vgg_conv)



# Add new layers

model.add(layers.Flatten())

model.add(layers.Dense(1024, activation='relu'))

model.add(layers.Dropout(0.5))

model.add(layers.Dense(5, activation='softmax'))



# Show a summary of the model. Check the number of trainable params

model.summary()

from keras.preprocessing.image import ImageDataGenerator



train_datagen = ImageDataGenerator(

    rescale=1./255,

    rotation_range=20,

    width_shift_range=0.2,

    height_shift_range=0.2,

    horizontal_flip=True,

    fill_mode='nearest')



validation_datagen = ImageDataGenerator(rescale=1./255)



#Change the batchsize according to the system RAM

train_batchsize = 100

val_batchsize = 10



train_dir='training_data/train'

validation_dir='training_data/validation'



train_generator = train_datagen.flow_from_directory(

    train_dir,

    target_size=(image_size1, image_size2),

    batch_size=train_batchsize,

    class_mode='categorical')



validation_generator = validation_datagen.flow_from_directory(

    validation_dir,

    target_size=(image_size1, image_size2),

    batch_size=val_batchsize,

    class_mode='categorical',

    shuffle=False)



# Compile the model

model.compile(loss='categorical_crossentropy',

              optimizer=optimizers.RMSprop(lr=1e-4),

              metrics=['acc'])



# Train the model

history = model.fit_generator(

    train_generator,

    steps_per_epoch=train_generator.samples/train_generator.batch_size,

    epochs=30,

    validation_data=validation_generator,

    validation_steps=validation_generator.samples/validation_generator.batch_size,

    verbose=1)

The issue is that when I run my script to train the model, it works fine until the actual training begins. Here, it gets stuck at epoch 1/30.

Layer (type)                 Output Shape              Param #

=================================================================

vgg16 (Model)                (None, 15, 20, 512)       14714688

_________________________________________________________________

flatten_1 (Flatten)          (None, 153600)            0

_________________________________________________________________

dense_1 (Dense)              (None, 1024)              157287424

_________________________________________________________________

dropout_1 (Dropout)          (None, 1024)              0

_________________________________________________________________

dense_2 (Dense)              (None, 5)                 5125

=================================================================

Total params: 172,007,237

Trainable params: 164,371,973

Non-trainable params: 7,635,264

_________________________________________________________________

Found 1989 images belonging to 5 classes.

Found 819 images belonging to 5 classes.

Epoch 1/30

This is no good unfortunately. I looked around online and I believe that the problem is in using fit_generator. There's something about the code for fit_generator in Keras being buggy. However, most of the other people experiencing issues with the epochs end up getting stuck on later epochs (ex. somebody wants to run it for 20 epochs and it halts on epoch 19/20).

How would I go about fixing this issue? This is my first time doing deep learning so I'm incredibly confused and would appreciate any help. Do I just need to move to using model.fit()?

You have to pass a valid integer number to fit_generator() as steps_per_epoch and validation_steps parameters.
So you can use as follows:

history = model.fit_generator(

    train_generator,

    steps_per_epoch=train_generator.samples//train_generator.batch_size,

    epochs=30,

    validation_data=validation_generator, validation_steps=validation_generator.samples//validation_generator.batch_size,

    verbose=1)

The second factor I can see that your model has 165M trainable parameter which has huge memory consumption particularly coupled with a high batchsize.
You should use images with lower resolution, note that in many case we can get better results with them.