Classifying images.py

import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt

data = keras.datasets.fashion_mnist

(train_images, train_labels), (test_images, test_labels) = data.load_data()

class_names = ["T-shirt", "Pants", "Hoodie", "Dress", "Coat", "Sandal", "Shirt", "Sneaker", "Bag", "Boot"]

# keep inputs between 0 and 1
train_images = train_images/255.0
test_images = test_images/255.0


model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation="relu"),
    keras.layers.Dense(10, activation="softmax")
    ])

model.compile(omptmizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])

model.fit(train_images, train_labels, epochs=5)


prediction = model.predict(test_images)

for i in range(5):
    plt.grid(False)
    plt.imshow(test_images[i], cmap=plt.cm.binary)
    plt.xlabel("Actual: " + class_names[test_labels[i]])
    plt.title("Prediction: " + class_names[np.argmax(prediction[i])])