Steps taking too long to complete

I'm trying to train a model which in my opinion is taking too long compared to other datasets given that it's taking about 9s to complete a step. I think that the problem is because the dataset is not being stored on ram, but I'm not sure of this.

The code is the following:

def load_data():

    train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)
    train_generator = train_datagen.flow_from_directory(path1, target_size=(200, 200), batch_size=32, class_mode="binary")

    test_datagen = ImageDataGenerator(rescale=1./255)
    test_generator = test_datagen.flow_from_directory(path2, target_size=(200, 200),batch_size=32, class_mode="binary")

    return train_generator, test_generator

Model:

• Sequential model
• 2 Convolutional layers with 32 neurons, activation = relu.
• 1 Convolutional layer with 64 neurons, activation = relu.
• Flattening and Dense layer, activation = relu.
• Dropout of 0.5
• Output layer (Dense) with sigmoid activation.
• Adam optimizer.
• Loss: binary cross entropy.

Fit:

model.fit_generator(x, steps_per_epoch=37, epochs=50, validation_data=y, validation_steps=3, callbacks=[tensorboard])

• My dataset has 1201 images and 2 classes.
• I built the model following this tutorial.
• My GPU is a GTX 1060 3gb.
• 8gb of ram.
• The images are being reshaped to 200x200.

If you could help me I'd appreciate it. Thank you very much!