Extract features from convolutional base on Dogs vs. Cats Kaggle competition

extract_features

# Extract features
import os, shutil
from keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(rescale=1./255)
batch_size = 32

def extract_features(directory, sample_count):
    features = np.zeros(shape=(sample_count, 7, 7, 512))  # Must be equal to the output of the convolutional base
    labels = np.zeros(shape=(sample_count))
    # Preprocess data
    generator = datagen.flow_from_directory(directory,
                                            target_size=(img_width,img_height),
                                            batch_size = batch_size,
                                            class_mode='binary')
    # Pass data through convolutional base
    i = 0
    for inputs_batch, labels_batch in generator:
        features_batch = conv_base.predict(inputs_batch)
        features[i * batch_size: (i + 1) * batch_size] = features_batch
        labels[i * batch_size: (i + 1) * batch_size] = labels_batch
        i += 1
        if i * batch_size >= sample_count:
            break
    return features, labels
    
train_features, train_labels = extract_features(train_dir, train_size)  # Agree with our small dataset size
validation_features, validation_labels = extract_features(validation_dir, validation_size)
test_features, test_labels = extract_features(test_dir, test_size)

i want for multiclass