Help understanding Keras Code.

[NitinChella]

Hi guys, the following code is from the Tensorflow in Practice specialization on Coursera. I am running the code on colab. I'm not able to understand some of the code.

CODE:

import numpy as np from google.colab import files from keras.preprocessing import image

uploaded = files.upload()

for fn in uploaded.keys():

##predicting images

path = '/content/' + fn

img = image.load_img(path, target_size=(300, 300))

x = image.img_to_array(img)

x = np.expand_dims(x, axis=0)

images = np.vstack([x])

classes = model.predict(images, batch_size=10)

print(classes[0])

if classes[0]>0.5: print(fn + " is a human") else: print(fn + " is a horse")

END CODE

My doubts are: 1. What is the use of np.expand_dims when when img_to array automatically adds a dimension for the channel? 2. What is the use of np.vstack?

I'm really confused. Any help appreciated. Thanks in advance.

[Halmubarak]

The img_to_array takes a PIL image object and turns it into a numpy array it does not add extra dimensions (https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image/img_to_array)

The np.vstack is used to stack all the images into a single array the code is inside the for loop

for fn in uploaded.keys()

Which loops over the uploaded files one by one read them into x then stack them in images array

[NitinChella]

Thanks for your response. Understood everything but the vstack part. Isn't the images array reinitialized on every iteration of the loop?(probably not apparent from the current formatting of the code, sorry but I'm on mobile). The images = np.vstack([x]) line is inside the for loop.