#import librariesimport tensorflow as tfimport time
#download fashion mnist datasetfashion_mnist = tf.keras.datasets.fashion_mnist(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
train_set_count = len(train_labels)test_set_count = len(test_labels)
#setup start timet0 = time.time()
#normalize imagestrain_images = train_images / 255.0test_images = test_images / 255.0
#create ML modelmodel = tf.keras.Sequential([    tf.keras.layers.Flatten(input_shape=(28, 28)),    tf.keras.layers.Dense(128, activation='relu'),    tf.keras.layers.Dense(10)])
#compile ML modelmodel.compile(optimizer='adam',              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),              metrics=['accuracy'])
#train ML modelmodel.fit(train_images, train_labels, epochs=10)
#evaluate ML model on test settest_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
#setup stop timet1 = time.time()total_time = t1-t0
#print resultsprint('\n')print(f'Training set contained {train_set_count} images')print(f'Testing set contained {test_set_count} images')print(f'Model achieved {test_acc:.2f} testing accuracy')print(f'Training and testing took {total_time:.2f} seconds')

#import librariesimport tensorflow as tffrom time import perf_counter
#download cifar10 datasetcifar10 = tf.keras.datasets.cifar10(train_images, train_labels), (test_images, test_labels) = cifar10.load_data()
train_set_count = len(train_labels)test_set_count = len(test_labels)
#setup start timet1_start = perf_counter()
#normalize imagestrain_images = train_images / 255.0test_images = test_images / 255.0
#create ML model using tensorflow provided ResNet50 model, note the [32, 32, 3] shape because that's the shape of cifarmodel = tf.keras.applications.ResNet50(    include_top=True, weights=None, input_tensor=None,    input_shape=(32, 32, 3), pooling=None, classes=10)
# CIFAR 10 labels have one integer for each image (between 0 and 10)# We want to perform a cross entropy which requires a one hot encoded version e.g: [0.0, 0.0, 1.0, 0.0, 0.0...]train_labels = tf.one_hot(train_labels.reshape(-1), depth=10, axis=-1)
# Do the same thing for the test labelstest_labels = tf.one_hot(test_labels.reshape(-1), depth=10, axis=-1)
#compile ML model, use non sparse version here because there is no sparse data.model.compile(optimizer='adam',              loss=tf.keras.losses.CategoricalCrossentropy(),              metrics=['accuracy'])
#train ML modelmodel.fit(train_images,  train_labels, epochs=10)
#evaluate ML model on test settest_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
#setup stop timet1_stop = perf_counter()total_time = t1_stop-t1_start
#print resultsprint('\n')print(f'Training set contained {train_set_count} images')print(f'Testing set contained {test_set_count} images')print(f'Model achieved {test_acc:.2f} testing accuracy')print(f'Training and testing took {total_time:.2f} seconds')