import tensorflow as tf

import numpy as np
import matplotlib.pyplot as plt

import wandb
from wandb.integration.tensorflow import WandbHook


wandb.login()
wandb.init(config=tf.python.flags.FLAGS, project="tensorflow-examples", entity='rwill128')

fashion_mnist = tf.keras.datasets.fashion_mnist
(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']
train_images = train_images / 255.0
test_images = test_images / 255.0

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])
model.compile(optimizer='adam', loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])


history = model.fit(train_images, train_labels, epochs=10)

print(history)

test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
