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Muat data NumPy

Lihat di TensorFlow.org Jalankan di Google Colab Lihat sumber di GitHub Unduh buku catatan

Tutorial ini memberikan contoh memuat data dari array NumPy ke dalam tf.data.Dataset .

Contoh ini memuat kumpulan data MNIST dari file .npz . Namun, sumber array NumPy tidak penting.

Mempersiapkan

import numpy as np
import tensorflow as tf

Muat dari file .npz

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']

Muat array NumPy dengan tf.data.Dataset

Dengan asumsi Anda memiliki larik contoh dan larik label yang sesuai, teruskan kedua larik tersebut sebagai tuple ke tf.data.Dataset.from_tensor_slices untuk membuat tf.data.Dataset .

train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))
test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))

Gunakan kumpulan data

Acak dan kelompokkan kumpulan data

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)

Bangun dan latih model

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)
Epoch 1/10
938/938 [==============================] - 3s 2ms/step - loss: 3.5318 - sparse_categorical_accuracy: 0.8762
Epoch 2/10
938/938 [==============================] - 2s 2ms/step - loss: 0.5408 - sparse_categorical_accuracy: 0.9289
Epoch 3/10
938/938 [==============================] - 2s 2ms/step - loss: 0.3770 - sparse_categorical_accuracy: 0.9473
Epoch 4/10
938/938 [==============================] - 2s 2ms/step - loss: 0.3281 - sparse_categorical_accuracy: 0.9566
Epoch 5/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2940 - sparse_categorical_accuracy: 0.9621
Epoch 6/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2622 - sparse_categorical_accuracy: 0.9657
Epoch 7/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2446 - sparse_categorical_accuracy: 0.9698
Epoch 8/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2147 - sparse_categorical_accuracy: 0.9739
Epoch 9/10
938/938 [==============================] - 2s 2ms/step - loss: 0.1956 - sparse_categorical_accuracy: 0.9750
Epoch 10/10
938/938 [==============================] - 2s 2ms/step - loss: 0.1964 - sparse_categorical_accuracy: 0.9759
<keras.callbacks.History at 0x7fc7a80beb50>
model.evaluate(test_dataset)
157/157 [==============================] - 0s 2ms/step - loss: 0.7089 - sparse_categorical_accuracy: 0.9572
[0.7088937163352966, 0.9571999907493591]