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from keras.applications.vgg16 import VGG16 | |
from keras.preprocessing.image import ImageDataGenerator | |
from keras.applications.vgg16 import preprocess_input | |
from keras.models import Sequential | |
from keras.layers import Dropout, Flatten, Dense | |
import numpy as np | |
img_width, img_height = 224, 224 | |
model = VGG16(weights='imagenet', include_top=False) | |
model.layers.pop() | |
#model.layers.pop() | |
train_data_dir = '/home/gertjan/Documents/vgg164096/test' | |
nb_train_samples = 1 | |
datagen = ImageDataGenerator(rescale=1. /255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) | |
generator = datagen.flow_from_directory(train_data_dir, target_size=(img_width, img_height), batch_size=8, class_mode=None, shuffle=False) | |
bottleneck_features_train = model.predict_generator(generator, nb_train_samples, 8) | |
print(bottleneck_features_train) | |
np.savetxt('test.out', bottleneck_features_train, '%s') | |
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