Load NumPy data via tf.data API

 Code 1

(.env) [boris@fedora34server KERAS]$ cat tfdataLoad.py

import numpy as np

import tensorflow as tf

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']

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

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

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=15)

print(model.evaluate(test_dataset))

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Run Code 1

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