def main(max_epoch, data_num):
sc = init_nncontext()
# get data, pre-process and create TFDataset
def get_data_rdd(dataset):
(images_data,
labels_data) = mnist.read_data_sets("/tmp/mnist", dataset)
image_rdd = sc.parallelize(images_data[:data_num])
labels_rdd = sc.parallelize(labels_data[:data_num])
rdd = image_rdd.zip(labels_rdd) \
.map(lambda rec_tuple: [normalizer(rec_tuple[0], mnist.TRAIN_MEAN, mnist.TRAIN_STD),
np.array(rec_tuple[1])])
return rdd
training_rdd = get_data_rdd("train")
testing_rdd = get_data_rdd("test")
dataset = TFDataset.from_rdd(training_rdd,
names=["features", "labels"],
shapes=[[28, 28, 1], []],
types=[tf.float32, tf.int32],
batch_size=280,
val_rdd=testing_rdd)
# construct the model from TFDataset
images, labels = dataset.tensors
with slim.arg_scope(lenet.lenet_arg_scope()):
logits, end_points = lenet.lenet(images,
num_classes=10,
is_training=True)
loss = tf.reduce_mean(
tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels))
# create a optimizer
optimizer = TFOptimizer(loss,
Adam(1e-3),
val_outputs=[logits],
val_labels=[labels],
val_method=Top1Accuracy())
optimizer.set_train_summary(TrainSummary("/tmp/az_lenet", "lenet"))
optimizer.set_val_summary(ValidationSummary("/tmp/az_lenet", "lenet"))
# kick off training
optimizer.optimize(end_trigger=MaxEpoch(max_epoch))
saver = tf.train.Saver()
saver.save(optimizer.sess, "/tmp/lenet/")
def main():
sc = init_nncontext()
# get data, pre-process and create TFDataset
(images_data, labels_data) = mnist.read_data_sets("/tmp/mnist", "train")
image_rdd = sc.parallelize(images_data)
labels_rdd = sc.parallelize(labels_data)
rdd = image_rdd.zip(labels_rdd) \
.map(lambda rec_tuple: [normalizer(rec_tuple[0], mnist.TRAIN_MEAN, mnist.TRAIN_STD),
np.array(rec_tuple[1])])
dataset = TFDataset.from_rdd(rdd,
names=["features", "labels"],
shapes=[[28, 28, 1], [1]],
types=[tf.float32, tf.int32],
batch_size=280)
# construct the model from TFDataset
images, labels = dataset.tensors
labels = tf.squeeze(labels)
with slim.arg_scope(lenet.lenet_arg_scope()):
logits, end_points = lenet.lenet(images,
num_classes=10,
is_training=True)
loss = tf.reduce_mean(
tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels))
# create a optimizer
optimizer = TFOptimizer(loss, Adam(1e-3))
optimizer.set_train_summary(TrainSummary("/tmp/az_lenet", "lenet"))
# kick off training
for i in range(5):
optimizer.optimize(end_trigger=MaxEpoch(i + 1))
saver = tf.train.Saver()
saver.save(optimizer.sess, "/tmp/lenet/")
