tutorial12_numpy.py

# 210119_https://www.tensorflow.org/tutorials/load_data/numpy?hl=ko

import numpy as np
import tensorflow as tf

# npz 파일에서 로드하기
DATA_URL = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz"

path = tf.keras.utils.get_file('mnist.npz', DATA_URL)
with np.load(path) as data:
    train_examples = data['x_train']
    train_labels = data['y_train']
    test_examples = data['x_test']
    test_labels = data['y_test']

# numpy 배열 로드
train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))
test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))

# 데이터셋 셔플 및 배치
BATCH_SIZE = 64
SHUFFLE_BUFFER_SIZE = 100

train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

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 = tf.keras.optimizers.RMSprop(),
        loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),
        metrics = ['sparse_categorical_accuracy'])
model.fit(train_dataset, epochs = 10)
model.evaluate(test_dataset)