def test_tfdataset_with_tfrecord(self): train_path = os.path.join(resource_path, "tfrecord/mnist_train.tfrecord") test_path = os.path.join(resource_path, "tfrecord/mnist_test.tfrecord") dataset = TFDataset.from_tfrecord_file(self.sc, train_path, batch_size=16, validation_file_path=test_path) raw_bytes = dataset.tensors[0] images, labels = parse_fn(raw_bytes) flat = tf.layers.flatten(images) logits = tf.layers.dense(flat, 10) loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)) opt = TFOptimizer.from_loss(loss, Adam()) opt.optimize()
def test_tfdataset_with_tfrecord(self): train_path = os.path.join(resource_path, "tfrecord/mnist_train.tfrecord") test_path = os.path.join(resource_path, "tfrecord/mnist_test.tfrecord") dataset = TFDataset.from_tfrecord_file(self.sc, train_path, batch_size=8, validation_file_path=test_path) dataset = dataset.map(lambda x: parse_fn(x[0])) flat = tf.layers.flatten(dataset.feature_tensors) logits = tf.layers.dense(flat, 10) labels = dataset.label_tensors loss = tf.reduce_mean( tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels)) opt = TFOptimizer.from_loss(loss, Adam()) opt.optimize()