Ways to decrease validation loss % and increase validation accuracy %? - tensorflow

Question

I'm trying to work with a image classification model for gravity waves detection.
So I want to check if there is something I could do to lower validation loss %, or more importantly, increase validation accuracy %.
The dataset is about a total of 460 images, split into
300 images that belong to 2 classes
60 images belonging to 2 classes
100 images belonging to 2 classes
For context, this is the code for pre processing:
batch_size = 32
Data Augmentation:
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
horizontal_flip=True,
)
test_datagen = ImageDataGenerator(
rescale=1./255)
The generator that reads images to generate batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'./train', target directory
target_size=(256, 256), images resized to 150x150
batch_size=batch_size,
# batch_size=40,
class_mode='binary')
validation_generator = train_datagen.flow_from_directory(
'./validation',
target_size=(256, 256),
batch_size=batch_size,
# batch_size=20,
class_mode='binary')
test_generator = test_datagen.flow_from_directory(
'./test',
target_size=(256, 256),
# batch_size=batch_size,
batch_size=batch_size,
#class_mode=None,
class_mode= None,
shuffle=False)
And this is the model used:
from tensorflow.keras.applications.inception_v3 import InceptionV3
import tensorflow as tf
from keras import regularizers
base_model = InceptionV3(input_shape = (256,256,3), include_top = False, weights = 'imagenet')
x = layers.Flatten()(base_model.output)
x = layers.Dense(1024, activation='relu')(x)
x = layers.Dropout(0.2)(x)
x = layers.Dense(1, activation='sigmoid')(x)
model = Model(base_model.input, x)
model.compile(optimizer = tf.keras.optimizers.SGD(learning_rate=0.001), loss = 'binary_crossentropy', metrics = ['accuracy'])
fitness = model.fit(
train_generator,
steps_per_epoch= 120,
epochs = 100,
validation_data=validation_generator,
validation_steps= 64)
So far the accuracy and loss % have been around:
Average training accuracy: 0.9237500047683715
Average training loss: 0.17309745135484264
Average validation accuracy: 0.6489999979734421
Average validation loss: 0.9121886402368545
The predicitons have been around:
validation predictions:
(24, 36)
test predictions:
(45, 55)
And the confusion matrix:
Confusion Matrix:
array([[12, 18],
[12, 18]])