Source code from https://medium.com/@hammaad.memon "Creating an image-identifier from scratch with Tensorflow"

imageclassifier.py

# get packages 
import tensorflow as tf
import numpy as np
from tensorflow import keras

# Load predefined data set
fashion_mnist = keras.datasets.fashion_mnist

# get data from data set
(training_images, training_labels), (test_images, test_labels) = fashion_mnist.load_data()

# Define neural net structure
# Sequential means that there a sequence of coloumns and that data must pass through each one
model = keras.Sequential([

    # (Step 1) input layer: 28x28 image 
    # "Flatten" extracts the 2d array into a single array of 784 characters
    keras.layers.Flatten(input_shape=(28, 28)),

    # (Step 3) hidden layer: 128 nodes long 
    # relu just gets rid of negative numbers at the hidden layer
    keras.layers.Dense(units=128, activation=tf.nn.relu),

    # (Step 2) output layer: 0-10 (a label for each possible clothing)
    # ("Dense") means that every node is connected to every other parrallel node
    # ("units=10") means that there are 10 possible outcomes
    # ("activation=") takes an activation function which is a filtering mechanism within each node with specifies if data
    # traveling through that layer is close enough to activate that node
    # softmax simply maps the data to the node with the closest number
    keras.layers.Dense(units=10, activation=tf.nn.softmax)
])


# Compile the model; do some config and get it ready for training
# optimizer--biasly reduces chances of certain outputs returning false positives
# loss--represents the loss of accuracy
# using optimizers and loss functions specialized for images
model.compile(optimizer=tf.optimizers.Adam(), loss='sparse_categorical_crossentropy')

# Train model using the training data
# ("epochs=5") means analyze and optimize the model 5 times with the function 
# specified above
model.fit(
    training_images, 
    training_labels, 
    epochs=5
)

# Get results from model
predictions = model.predict(test_images)

# process results and display
num_correct = 0
for i in range(len(test_labels)):
    if test_labels[i] == int(list(predictions[i]).index(max(predictions[i]))):
        num_correct +=1

num_incorrect = len(test_labels) - num_correct
print("correctly identified: " + str(num_correct) + " out of " + str(len(test_labels)))
print("total loss: " + str(num_incorrect))
print("accuracy: " + str(1 - (num_incorrect/num_correct)))