PCA in CNN deep learning model using Keras - tensorflow

Question

I built a CNN model for image classification using Keras and I want to use principal component analysis (PCA) with the model. How to use PCA in CNN for image recognition using Keras?
I have tried the following code but when I run pca.fit() code, the code still running for hours and the RAM become full.
#Data files
train_iris_data = 'Iris_Database_01/Training'
valid_iris_data = 'Iris_Database_01/Validation'
test_iris_data = 'Iris_Database_01/Testing'
#Image data generator
train_iris_datagen = ImageDataGenerator(
rotation_range=10,
shear_range=0.2,
zoom_range=0.1,
width_shift_range=0.1,
height_shift_range=0.1
)
test_iris_datagen = ImageDataGenerator()
#Image batches
image_size = (224, 224)
batch = 32
# Training
train_iris_generator = train_iris_datagen.flow_from_directory(
train_iris_data,
target_size=image_size,
batch_size=batch,
class_mode='categorical')
# Validation
validation_iris_generator = test_iris_datagen.flow_from_directory(
valid_iris_data,
target_size=image_size,
batch_size=batch,
class_mode='categorical',
shuffle = False)
# Testing
test_iris_generator = test_iris_datagen.flow_from_directory(
test_iris_data,
target_size=image_size,
batch_size=1,
class_mode='categorical',
shuffle = False)
pca = PCA(n_components=2)
pca.fit(train_iris_generator)
#pca = PCA(n_components=0.8)
#pca.fit(train_iris_generator)

Answer 1

you can use truncated SVD instead. Another method that you can use is, IncrementalPCA PCA.
from sklearn.decomposition import TruncatedSVD
from sklearn.decomposition import IncrementalPCA
def func_PCA(input_data):
input_data = np.array(input_data)
pca = IncrementalPCA(n_components=50, batch_size=50)
pca.fit(input_data)
pca_input_data = pca.transform(input_data)
eigenvalues = pca.explained_variance_
eigenvectors = pca.components_
return pca_input_data, eigenvalues, eigenvectors
def svd_func(input_data):
svd = TruncatedSVD(n_components=50)
svd.fit(input_data)
pca_input_data = svd.transform(input_data)
eigenvalues = svd.explained_variance_
eigenvectors = svd.components_
return pca_input_data, eigenvalues, eigenvectors