Progress Update 1

Workshop Resources

If you would like to test the following code, visit this link

To edit this code, click on the ‘Copy to Drive’ button to make a personal copy of this notebook. Make sure you are logged in to your Google account.

If you are using a Nuevo Google account temporarily

Once you make a copy, please make sure to replace the “Copy of” with your name, along with the file name. This will be on the top left corner of your notebook.

Before moving on, please check your Google Colab notebook against the code below:

# Importing TensorFlow and tf.keras libraries
import tensorflow as tf
from tensorflow import keras 

# Helper libraries for statistics and plotting import numpy as np import matplotlib.pyplot as plt

#This variable is declared from the fashion_mist library of the datasets section
fashion_mnist = keras.datasets.fashion_mnist
#This loads four variables from the dataset. 
#The train_images and train_labels are data that the model uses to learn
#The test_images and test_labels are used by the model to compare against.
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'  ]
plt.figure()
plt.imshow(train_images[0]) #Shows the first image in the data set as a plot or different colored pixels
plt.colorbar() #displays the color bar on the right
plt.grid(False)
plt.show() #displays the entire plot

Ankle Boot

plt.figure()
plt.imshow(train_images[7]) #Shows the first image in the data set as a plot or different colored pixels
plt.colorbar() #displays the color bar on the right
plt.grid(False)
plt.show() #displays the entire plot

Shirt

#the train_images and test_images range between values from 0 to 255. 
#To maintain consistency between the training and testing set, we will divide train_images and test_images by 255
train_images = train_images / 255.0 
test_images = test_images / 255.0