Tensorflow Pretrained

Preprocessing Images

1. Put path of all images in a list

2. For all images, load image

from tensorflow.python.keras.preprocessing.image import load_img
imgs = [load_img(img, target_size=(height, width), for img in imgs)]

1. Convert images into 3D tensors (an image is a 3D tensor as seen in the last lesson) using Keras img_to_array(). Multiple 3D tensors stack together to form another array.

import numpy as np
from tensorflow.python.keras.preprocessing.image import img_to_array
img_array = np.array([img_to_array(img) for img in imgs])

1. Do some arithmetic on the new array using preprocess_input(). Squishes values to $[-1, 1]$ since the pretrained model resnet also had these values.

from tensorflow.python.keras.applications.resnet50 import preprocess_input
output = preprocess_input(img_array)

Predicting using Pretrained Models

from tensorflow.python.keras.applications import ResNet50

my_model = ResNet50(weights='../input/resnet50/resnet50_weights_tf_dim_ordering_tf_kernels.h5')
test_data = read_and_prep_images(img_paths)
preds = my_model.predict(test_data)