I am training a Convolutional Neural Network using Google Colab's CPU and GPU.

This is the architecture of the network:

Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 62, 126, 32)       896       
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 31, 63, 32)        0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 29, 61, 32)        9248      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 30, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 12, 28, 64)        18496     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 6, 14, 64)         0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 4, 12, 64)         36928     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 2, 6, 64)          0         
_________________________________________________________________
flatten (Flatten)            (None, 768)               0         
_________________________________________________________________
dropout (Dropout)            (None, 768)               0         
_________________________________________________________________
lambda (Lambda)              (None, 1, 768)            0         
_________________________________________________________________
dense (Dense)                (None, 1, 256)            196864    
_________________________________________________________________
dense_1 (Dense)              (None, 1, 8)              2056      
_________________________________________________________________
permute (Permute)            (None, 8, 1)              0         
_________________________________________________________________
dense_2 (Dense)              (None, 8, 36)             72        
=================================================================
Total params: 264,560
Trainable params: 264,560
Non-trainable params: 0

So, this is a very small network but a specific output, shape (8, 36) because I want to recognize characters on an image of license plates.

I used this code to train the network:

model.fit_generator(generator=training_generator,validation_data=validation_generator,steps_per_epoch = num_train_samples // 128,validation_steps = num_val_samples // 128,epochs = 10)

The generator resizes the images to (64, 128). This is the code regarding the generator:

class DataGenerator(Sequence):def __init__(self, x_set, y_set, batch_size):self.x, self.y = x_set, y_setself.batch_size = batch_sizedef __len__(self):return math.ceil(len(self.x) / self.batch_size)def __getitem__(self, idx):batch_x = self.x[idx * self.batch_size:(idx + 1) *self.batch_size]batch_y = self.y[idx * self.batch_size:(idx + 1) *self.batch_size]return np.array([resize(imread(file_name), (64, 128))for file_name in batch_x]), np.array(batch_y)

On CPU one epoch takes 70-90 minutes. On GPU (149 Watt) it takes 5 times as long as on CPU.

1. Do you know, why it takes so long? Is there something wrong with the generator?
2. Can I speed this process up somehow?

Edit: This ist the link to my notebook: https://colab.research.google.com/drive/1ux9E8DhxPxtgaV60WUiYI2ew2s74Xrwh?usp=sharing

My data is stored in my Google Drive. The training data set contains 105 k images and the validation data set 76 k. All in all, I have 1.8 GB of data.

Should I maybe store the data at another place?

Thanks a lot!

I think, you did not enable a GPU

Go to Edit -> Notebook Settings and choose GPU. Then click SAVE