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()
