# Import Tensorflow and check the version
import tensorflow as tf
print(tf.__version__)
2.1.0
a = tf.constant("Hello world!")
print(a)
tf.Tensor(b'Hello world!', shape=(), dtype=string)
# Autograph and tf.function()
@tf.function
def f(x):
return tf.add(x, 1.)
scalar = tf.constant(1.0)
vector = tf.constant([1.0, 1.0])
matrix = tf.constant([[3.0]])
print(f(scalar))
print(f(vector))
print(f(matrix))
tf.Tensor(2.0, shape=(), dtype=float32) tf.Tensor([2. 2.], shape=(2,), dtype=float32) tf.Tensor([[4.]], shape=(1, 1), dtype=float32)
# Building a model
n_input = 4
n_output = 3
n_hidden = 10
# hyperparameter
learning_rate = 0.01
training_epochs = 2000
display_steps = 200
# Getting data
from sklearn. datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import OneHotEncoder
iris_data = load_iris() # load the iris dataset
x = iris_data.data
y_ = iris_data.target.reshape(-1, 1) # Convert data to a single column
# One Hot encode the class labels
encoder = OneHotEncoder(sparse = False)
y = encoder.fit_transform(y_)
train_x, test_x, train_y, test_y = train_test_split(x, y, test_size=0.20)
# Build the model
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(n_hidden, input_shape=(n_input,), activation='relu', name='fc1'))
model.add(tf.keras.layers.Dense(n_output, activation='softmax', name='output'))
# Adam optimizer with learning rate of 0.001
optimizer = tf.keras.optimizers.Adam(lr=0.001)
model.compile(optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
print('Neural Network Model Summary: ')
print(model.summary())
Neural Network Model Summary: Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= fc1 (Dense) (None, 10) 50 _________________________________________________________________ output (Dense) (None, 3) 33 ================================================================= Total params: 83 Trainable params: 83 Non-trainable params: 0 _________________________________________________________________ None
# Train the model
model.fit(train_x, train_y, verbose=2, batch_size=5, epochs=200)
# Test on unseen data
results = model.evaluate(test_x, test_y)
print('Final test set loss: {:4f}'.format(results[0]))
print('Final test set accuracy: {:4f}'.format(results[1]))
Epoch 1/200 24/24 - 0s - loss: 4.0796 - accuracy: 0.3417 Epoch 2/200 24/24 - 0s - loss: 3.2345 - accuracy: 0.3417 Epoch 3/200 24/24 - 0s - loss: 2.6355 - accuracy: 0.3417 Epoch 4/200 24/24 - 0s - loss: 2.1942 - accuracy: 0.3417 Epoch 5/200 24/24 - 0s - loss: 1.8734 - accuracy: 0.3417 Epoch 6/200 24/24 - 0s - loss: 1.6620 - accuracy: 0.4500 Epoch 7/200 24/24 - 0s - loss: 1.4956 - accuracy: 0.6667 Epoch 8/200 24/24 - 0s - loss: 1.3713 - accuracy: 0.6833 Epoch 9/200 24/24 - 0s - loss: 1.2623 - accuracy: 0.6833 Epoch 10/200 24/24 - 0s - loss: 1.1800 - accuracy: 0.6833 Epoch 11/200 24/24 - 0s - loss: 1.1269 - accuracy: 0.6833 Epoch 12/200 24/24 - 0s - loss: 1.0675 - accuracy: 0.6833 Epoch 13/200 24/24 - 0s - loss: 1.0308 - accuracy: 0.6833 Epoch 14/200 24/24 - 0s - loss: 1.0018 - accuracy: 0.6833 Epoch 15/200 24/24 - 0s - loss: 0.9756 - accuracy: 0.6833 Epoch 16/200 24/24 - 0s - loss: 0.9544 - accuracy: 0.6833 Epoch 17/200 24/24 - 0s - loss: 0.9416 - accuracy: 0.6750 Epoch 18/200 24/24 - 0s - loss: 0.9235 - accuracy: 0.6750 Epoch 19/200 24/24 - 0s - loss: 0.9105 - accuracy: 0.6583 Epoch 20/200 24/24 - 0s - loss: 0.8986 - accuracy: 0.6417 Epoch 21/200 24/24 - 0s - loss: 0.8873 - accuracy: 0.6167 Epoch 22/200 24/24 - 0s - loss: 0.8777 - accuracy: 0.5167 Epoch 23/200 24/24 - 0s - loss: 0.8672 - accuracy: 0.5083 Epoch 24/200 24/24 - 0s - loss: 0.8563 - accuracy: 0.4500 Epoch 25/200 24/24 - 0s - loss: 0.8468 - accuracy: 0.4333 Epoch 26/200 24/24 - 0s - loss: 0.8384 - accuracy: 0.4250 Epoch 27/200 24/24 - 0s - loss: 0.8265 - accuracy: 0.4250 Epoch 28/200 24/24 - 0s - loss: 0.8180 - accuracy: 0.4000 Epoch 29/200 24/24 - 0s - loss: 0.8071 - accuracy: 0.4083 Epoch 30/200 24/24 - 0s - loss: 0.7983 - accuracy: 0.4167 Epoch 31/200 24/24 - 0s - loss: 0.7878 - accuracy: 0.4250 Epoch 32/200 24/24 - 0s - loss: 0.7796 - accuracy: 0.4000 Epoch 33/200 24/24 - 0s - loss: 0.7683 - accuracy: 0.4000 Epoch 34/200 24/24 - 0s - loss: 0.7599 - accuracy: 0.4250 Epoch 35/200 24/24 - 0s - loss: 0.7511 - accuracy: 0.4083 Epoch 36/200 24/24 - 0s - loss: 0.7402 - accuracy: 0.4250 Epoch 37/200 24/24 - 0s - loss: 0.7313 - accuracy: 0.4333 Epoch 38/200 24/24 - 0s - loss: 0.7223 - accuracy: 0.4333 Epoch 39/200 24/24 - 0s - loss: 0.7141 - accuracy: 0.4250 Epoch 40/200 24/24 - 0s - loss: 0.7044 - accuracy: 0.4250 Epoch 41/200 24/24 - 0s - loss: 0.6964 - accuracy: 0.4333 Epoch 42/200 24/24 - 0s - loss: 0.6895 - accuracy: 0.4250 Epoch 43/200 24/24 - 0s - loss: 0.6800 - accuracy: 0.4500 Epoch 44/200 24/24 - 0s - loss: 0.6727 - accuracy: 0.4583 Epoch 45/200 24/24 - 0s - loss: 0.6653 - accuracy: 0.4833 Epoch 46/200 24/24 - 0s - loss: 0.6574 - accuracy: 0.4500 Epoch 47/200 24/24 - 0s - loss: 0.6503 - accuracy: 0.4667 Epoch 48/200 24/24 - 0s - loss: 0.6433 - accuracy: 0.4917 Epoch 49/200 24/24 - 0s - loss: 0.6379 - accuracy: 0.4750 Epoch 50/200 24/24 - 0s - loss: 0.6299 - accuracy: 0.4417 Epoch 51/200 24/24 - 0s - loss: 0.6237 - accuracy: 0.5083 Epoch 52/200 24/24 - 0s - loss: 0.6173 - accuracy: 0.5500 Epoch 53/200 24/24 - 0s - loss: 0.6110 - accuracy: 0.5000 Epoch 54/200 24/24 - 0s - loss: 0.6070 - accuracy: 0.5417 Epoch 55/200 24/24 - 0s - loss: 0.6002 - accuracy: 0.5333 Epoch 56/200 24/24 - 0s - loss: 0.5945 - accuracy: 0.5167 Epoch 57/200 24/24 - 0s - loss: 0.5895 - accuracy: 0.5500 Epoch 58/200 24/24 - 0s - loss: 0.5847 - accuracy: 0.5417 Epoch 59/200 24/24 - 0s - loss: 0.5800 - accuracy: 0.5917 Epoch 60/200 24/24 - 0s - loss: 0.5753 - accuracy: 0.6083 Epoch 61/200 24/24 - 0s - loss: 0.5712 - accuracy: 0.5500 Epoch 62/200 24/24 - 0s - loss: 0.5666 - accuracy: 0.6000 Epoch 63/200 24/24 - 0s - loss: 0.5620 - accuracy: 0.6250 Epoch 64/200 24/24 - 0s - loss: 0.5583 - accuracy: 0.6083 Epoch 65/200 24/24 - 0s - loss: 0.5544 - accuracy: 0.6333 Epoch 66/200 24/24 - 0s - loss: 0.5499 - accuracy: 0.6417 Epoch 67/200 24/24 - 0s - loss: 0.5466 - accuracy: 0.6417 Epoch 68/200 24/24 - 0s - loss: 0.5410 - accuracy: 0.6333 Epoch 69/200 24/24 - 0s - loss: 0.5248 - accuracy: 0.6417 Epoch 70/200 24/24 - 0s - loss: 0.5063 - accuracy: 0.7583 Epoch 71/200 24/24 - 0s - loss: 0.5015 - accuracy: 0.7667 Epoch 72/200 24/24 - 0s - loss: 0.4920 - accuracy: 0.8000 Epoch 73/200 24/24 - 0s - loss: 0.4844 - accuracy: 0.8000 Epoch 74/200 24/24 - 0s - loss: 0.4777 - accuracy: 0.8333 Epoch 75/200 24/24 - 0s - loss: 0.4765 - accuracy: 0.8417 Epoch 76/200 24/24 - 0s - loss: 0.4639 - accuracy: 0.8667 Epoch 77/200 24/24 - 0s - loss: 0.4576 - accuracy: 0.8917 Epoch 78/200 24/24 - 0s - loss: 0.4523 - accuracy: 0.8833 Epoch 79/200 24/24 - 0s - loss: 0.4483 - accuracy: 0.8667 Epoch 80/200 24/24 - 0s - loss: 0.4409 - accuracy: 0.9000 Epoch 81/200 24/24 - 0s - loss: 0.4364 - accuracy: 0.8833 Epoch 82/200 24/24 - 0s - loss: 0.4321 - accuracy: 0.8750 Epoch 83/200 24/24 - 0s - loss: 0.4308 - accuracy: 0.9000 Epoch 84/200 24/24 - 0s - loss: 0.4206 - accuracy: 0.9000 Epoch 85/200 24/24 - 0s - loss: 0.4129 - accuracy: 0.9083 Epoch 86/200 24/24 - 0s - loss: 0.4113 - accuracy: 0.8917 Epoch 87/200 24/24 - 0s - loss: 0.4063 - accuracy: 0.9333 Epoch 88/200 24/24 - 0s - loss: 0.3997 - accuracy: 0.9250 Epoch 89/200 24/24 - 0s - loss: 0.3958 - accuracy: 0.9250 Epoch 90/200 24/24 - 0s - loss: 0.3939 - accuracy: 0.9000 Epoch 91/200 24/24 - 0s - loss: 0.3853 - accuracy: 0.9333 Epoch 92/200 24/24 - 0s - loss: 0.3813 - accuracy: 0.9250 Epoch 93/200 24/24 - 0s - loss: 0.3763 - accuracy: 0.9333 Epoch 94/200 24/24 - 0s - loss: 0.3711 - accuracy: 0.9417 Epoch 95/200 24/24 - 0s - loss: 0.3672 - accuracy: 0.9417 Epoch 96/200 24/24 - 0s - loss: 0.3646 - accuracy: 0.9417 Epoch 97/200 24/24 - 0s - loss: 0.3582 - accuracy: 0.9333 Epoch 98/200 24/24 - 0s - loss: 0.3581 - accuracy: 0.9417 Epoch 99/200 24/24 - 0s - loss: 0.3531 - accuracy: 0.9167 Epoch 100/200 24/24 - 0s - loss: 0.3469 - accuracy: 0.9417 Epoch 101/200 24/24 - 0s - loss: 0.3446 - accuracy: 0.9417 Epoch 102/200 24/24 - 0s - loss: 0.3441 - accuracy: 0.9083 Epoch 103/200 24/24 - 0s - loss: 0.3326 - accuracy: 0.9500 Epoch 104/200 24/24 - 0s - loss: 0.3305 - accuracy: 0.9417 Epoch 105/200 24/24 - 0s - loss: 0.3264 - accuracy: 0.9667 Epoch 106/200 24/24 - 0s - loss: 0.3233 - accuracy: 0.9500 Epoch 107/200 24/24 - 0s - loss: 0.3173 - accuracy: 0.9667 Epoch 108/200 24/24 - 0s - loss: 0.3159 - accuracy: 0.9583 Epoch 109/200 24/24 - 0s - loss: 0.3122 - accuracy: 0.9583 Epoch 110/200 24/24 - 0s - loss: 0.3064 - accuracy: 0.9583 Epoch 111/200 24/24 - 0s - loss: 0.3035 - accuracy: 0.9667 Epoch 112/200 24/24 - 0s - loss: 0.3005 - accuracy: 0.9583 Epoch 113/200 24/24 - 0s - loss: 0.2964 - accuracy: 0.9500 Epoch 114/200 24/24 - 0s - loss: 0.2955 - accuracy: 0.9667 Epoch 115/200 24/24 - 0s - loss: 0.2891 - accuracy: 0.9833 Epoch 116/200 24/24 - 0s - loss: 0.2853 - accuracy: 0.9583 Epoch 117/200 24/24 - 0s - loss: 0.2850 - accuracy: 0.9750 Epoch 118/200 24/24 - 0s - loss: 0.2804 - accuracy: 0.9667 Epoch 119/200 24/24 - 0s - loss: 0.2768 - accuracy: 0.9833 Epoch 120/200 24/24 - 0s - loss: 0.2737 - accuracy: 0.9750 Epoch 121/200 24/24 - 0s - loss: 0.2725 - accuracy: 0.9500 Epoch 122/200 24/24 - 0s - loss: 0.2700 - accuracy: 0.9583 Epoch 123/200 24/24 - 0s - loss: 0.2649 - accuracy: 0.9750 Epoch 124/200 24/24 - 0s - loss: 0.2614 - accuracy: 0.9750 Epoch 125/200 24/24 - 0s - loss: 0.2685 - accuracy: 0.9417 Epoch 126/200 24/24 - 0s - loss: 0.2606 - accuracy: 0.9500 Epoch 127/200 24/24 - 0s - loss: 0.2513 - accuracy: 0.9750 Epoch 128/200 24/24 - 0s - loss: 0.2522 - accuracy: 0.9583 Epoch 129/200 24/24 - 0s - loss: 0.2468 - accuracy: 0.9500 Epoch 130/200 24/24 - 0s - loss: 0.2465 - accuracy: 0.9750 Epoch 131/200 24/24 - 0s - loss: 0.2430 - accuracy: 0.9667 Epoch 132/200 24/24 - 0s - loss: 0.2388 - accuracy: 0.9583 Epoch 133/200 24/24 - 0s - loss: 0.2384 - accuracy: 0.9750 Epoch 134/200 24/24 - 0s - loss: 0.2349 - accuracy: 0.9667 Epoch 135/200 24/24 - 0s - loss: 0.2322 - accuracy: 0.9583 Epoch 136/200 24/24 - 0s - loss: 0.2289 - accuracy: 0.9667 Epoch 137/200 24/24 - 0s - loss: 0.2269 - accuracy: 0.9667 Epoch 138/200 24/24 - 0s - loss: 0.2256 - accuracy: 0.9667 Epoch 139/200 24/24 - 0s - loss: 0.2212 - accuracy: 0.9667 Epoch 140/200 24/24 - 0s - loss: 0.2184 - accuracy: 0.9833 Epoch 141/200 24/24 - 0s - loss: 0.2175 - accuracy: 0.9667 Epoch 142/200 24/24 - 0s - loss: 0.2156 - accuracy: 0.9667 Epoch 143/200 24/24 - 0s - loss: 0.2146 - accuracy: 0.9667 Epoch 144/200 24/24 - 0s - loss: 0.2091 - accuracy: 0.9667 Epoch 145/200 24/24 - 0s - loss: 0.2108 - accuracy: 0.9667 Epoch 146/200 24/24 - 0s - loss: 0.2057 - accuracy: 0.9750 Epoch 147/200 24/24 - 0s - loss: 0.2032 - accuracy: 0.9750 Epoch 148/200 24/24 - 0s - loss: 0.2012 - accuracy: 0.9833 Epoch 149/200 24/24 - 0s - loss: 0.2014 - accuracy: 0.9667 Epoch 150/200 24/24 - 0s - loss: 0.1973 - accuracy: 0.9667 Epoch 151/200 24/24 - 0s - loss: 0.1958 - accuracy: 0.9833 Epoch 152/200 24/24 - 0s - loss: 0.1936 - accuracy: 0.9750 Epoch 153/200 24/24 - 0s - loss: 0.1919 - accuracy: 0.9750 Epoch 154/200 24/24 - 0s - loss: 0.1898 - accuracy: 0.9833 Epoch 155/200 24/24 - 0s - loss: 0.1887 - accuracy: 0.9833 Epoch 156/200 24/24 - 0s - loss: 0.1890 - accuracy: 0.9583 Epoch 157/200 24/24 - 0s - loss: 0.1840 - accuracy: 0.9833 Epoch 158/200 24/24 - 0s - loss: 0.1886 - accuracy: 0.9750 Epoch 159/200 24/24 - 0s - loss: 0.1832 - accuracy: 0.9750 Epoch 160/200 24/24 - 0s - loss: 0.1791 - accuracy: 0.9833 Epoch 161/200 24/24 - 0s - loss: 0.1791 - accuracy: 0.9750 Epoch 162/200 24/24 - 0s - loss: 0.1763 - accuracy: 0.9750 Epoch 163/200 24/24 - 0s - loss: 0.1747 - accuracy: 0.9750 Epoch 164/200 24/24 - 0s - loss: 0.1740 - accuracy: 0.9833 Epoch 165/200 24/24 - 0s - loss: 0.1786 - accuracy: 0.9667 Epoch 166/200 24/24 - 0s - loss: 0.1705 - accuracy: 0.9750 Epoch 167/200 24/24 - 0s - loss: 0.1680 - accuracy: 0.9833 Epoch 168/200 24/24 - 0s - loss: 0.1666 - accuracy: 0.9750 Epoch 169/200 24/24 - 0s - loss: 0.1663 - accuracy: 0.9833 Epoch 170/200 24/24 - 0s - loss: 0.1653 - accuracy: 0.9833 Epoch 171/200 24/24 - 0s - loss: 0.1658 - accuracy: 0.9583 Epoch 172/200 24/24 - 0s - loss: 0.1626 - accuracy: 0.9750 Epoch 173/200 24/24 - 0s - loss: 0.1606 - accuracy: 0.9750 Epoch 174/200 24/24 - 0s - loss: 0.1590 - accuracy: 0.9833 Epoch 175/200 24/24 - 0s - loss: 0.1572 - accuracy: 0.9833 Epoch 176/200 24/24 - 0s - loss: 0.1574 - accuracy: 0.9750 Epoch 177/200 24/24 - 0s - loss: 0.1551 - accuracy: 0.9833 Epoch 178/200 24/24 - 0s - loss: 0.1552 - accuracy: 0.9750 Epoch 179/200 24/24 - 0s - loss: 0.1541 - accuracy: 0.9833 Epoch 180/200 24/24 - 0s - loss: 0.1530 - accuracy: 0.9833 Epoch 181/200 24/24 - 0s - loss: 0.1513 - accuracy: 0.9833 Epoch 182/200 24/24 - 0s - loss: 0.1486 - accuracy: 0.9833 Epoch 183/200 24/24 - 0s - loss: 0.1483 - accuracy: 0.9750 Epoch 184/200 24/24 - 0s - loss: 0.1458 - accuracy: 0.9833 Epoch 185/200 24/24 - 0s - loss: 0.1471 - accuracy: 0.9667 Epoch 186/200 24/24 - 0s - loss: 0.1476 - accuracy: 0.9750 Epoch 187/200 24/24 - 0s - loss: 0.1437 - accuracy: 0.9750 Epoch 188/200 24/24 - 0s - loss: 0.1418 - accuracy: 0.9833 Epoch 189/200 24/24 - 0s - loss: 0.1427 - accuracy: 0.9833 Epoch 190/200 24/24 - 0s - loss: 0.1418 - accuracy: 0.9833 Epoch 191/200 24/24 - 0s - loss: 0.1447 - accuracy: 0.9750 Epoch 192/200 24/24 - 0s - loss: 0.1388 - accuracy: 0.9750 Epoch 193/200 24/24 - 0s - loss: 0.1375 - accuracy: 0.9833 Epoch 194/200 24/24 - 0s - loss: 0.1362 - accuracy: 0.9750 Epoch 195/200 24/24 - 0s - loss: 0.1380 - accuracy: 0.9667 Epoch 196/200 24/24 - 0s - loss: 0.1355 - accuracy: 0.9750 Epoch 197/200 24/24 - 0s - loss: 0.1358 - accuracy: 0.9833 Epoch 198/200 24/24 - 0s - loss: 0.1329 - accuracy: 0.9833 Epoch 199/200 24/24 - 0s - loss: 0.1320 - accuracy: 0.9750 Epoch 200/200 24/24 - 0s - loss: 0.1322 - accuracy: 0.9917 1/1 [==============================] - 0s 2ms/step - loss: 0.1019 - accuracy: 1.0000 Final test set loss: 0.101947 Final test set accuracy: 1.000000
import tensorflow_datasets as tfds
tfds.list_builders()
['abstract_reasoning', 'aeslc', 'aflw2k3d', 'amazon_us_reviews', 'arc', 'bair_robot_pushing_small', 'beans', 'big_patent', 'bigearthnet', 'billsum', 'binarized_mnist', 'binary_alpha_digits', 'c4', 'caltech101', 'caltech_birds2010', 'caltech_birds2011', 'cars196', 'cassava', 'cats_vs_dogs', 'celeb_a', 'celeb_a_hq', 'cfq', 'chexpert', 'cifar10', 'cifar100', 'cifar10_1', 'cifar10_corrupted', 'citrus_leaves', 'cityscapes', 'civil_comments', 'clevr', 'cmaterdb', 'cnn_dailymail', 'coco', 'coil100', 'colorectal_histology', 'colorectal_histology_large', 'cos_e', 'curated_breast_imaging_ddsm', 'cycle_gan', 'deep_weeds', 'definite_pronoun_resolution', 'diabetic_retinopathy_detection', 'div2k', 'dmlab', 'downsampled_imagenet', 'dsprites', 'dtd', 'duke_ultrasound', 'dummy_dataset_shared_generator', 'dummy_mnist', 'emnist', 'eraser_multi_rc', 'esnli', 'eurosat', 'fashion_mnist', 'flic', 'flores', 'food101', 'gap', 'gigaword', 'glue', 'groove', 'higgs', 'horses_or_humans', 'i_naturalist2017', 'image_label_folder', 'imagenet2012', 'imagenet2012_corrupted', 'imagenet_resized', 'imagenette', 'imagewang', 'imdb_reviews', 'iris', 'kitti', 'kmnist', 'lfw', 'librispeech', 'librispeech_lm', 'libritts', 'lm1b', 'lost_and_found', 'lsun', 'malaria', 'math_dataset', 'mnist', 'mnist_corrupted', 'movie_rationales', 'moving_mnist', 'multi_news', 'multi_nli', 'multi_nli_mismatch', 'natural_questions', 'newsroom', 'nsynth', 'omniglot', 'open_images_v4', 'opinosis', 'oxford_flowers102', 'oxford_iiit_pet', 'para_crawl', 'patch_camelyon', 'pet_finder', 'places365_small', 'plant_leaves', 'plant_village', 'plantae_k', 'qa4mre', 'quickdraw_bitmap', 'reddit_tifu', 'resisc45', 'rock_paper_scissors', 'rock_you', 'scan', 'scene_parse150', 'scicite', 'scientific_papers', 'shapes3d', 'smallnorb', 'snli', 'so2sat', 'speech_commands', 'squad', 'stanford_dogs', 'stanford_online_products', 'starcraft_video', 'sun397', 'super_glue', 'svhn_cropped', 'ted_hrlr_translate', 'ted_multi_translate', 'tf_flowers', 'the300w_lp', 'tiny_shakespeare', 'titanic', 'trivia_qa', 'uc_merced', 'ucf101', 'vgg_face2', 'visual_domain_decathlon', 'voc', 'wider_face', 'wikihow', 'wikipedia', 'wmt14_translate', 'wmt15_translate', 'wmt16_translate', 'wmt17_translate', 'wmt18_translate', 'wmt19_translate', 'wmt_t2t_translate', 'wmt_translate', 'xnli', 'xsum', 'yelp_polarity_reviews']
iris = tfds.load(name="iris", split=None)