def model_fn(features, labels, mode):
from nets import lenet
slim = tf.contrib.slim
with slim.arg_scope(lenet.lenet_arg_scope()):
logits, end_points = lenet.lenet(features, num_classes=10, is_training=True)
if mode == tf.estimator.ModeKeys.EVAL or mode == tf.estimator.ModeKeys.TRAIN:
loss = tf.reduce_mean(
tf.losses.sparse_softmax_cross_entropy(logits=logits, labels=labels))
optimizer = ZooOptimizer(tf.train.AdamOptimizer())
train_op = optimizer.minimize(loss)
return tf.estimator.EstimatorSpec(mode, predictions=logits,
loss=loss, train_op=train_op)
else:
return tf.estimator.EstimatorSpec(mode, predictions=logits)
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(data_num):
sc = init_nncontext()
# get data, pre-process and create TFDataset
(images_data, labels_data) = mnist.read_data_sets("/tmp/mnist", "test")
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])])
dataset = TFDataset.from_rdd(rdd,
names=["features", "labels"],
shapes=[[28, 28, 1], [1]],
types=[tf.float32, tf.int32],
batch_per_thread=20)
# 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=False)
predictions = tf.to_int32(tf.argmax(logits, axis=1))
correct = tf.expand_dims(tf.to_int32(tf.equal(predictions, labels)),
axis=1)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, "/tmp/lenet/model")
predictor = TFPredictor(sess, [correct])
accuracy = predictor.predict().mean()
print("predict accuracy is %s" % accuracy)
def main(max_epoch, data_num):
sc = init_nncontext()
# get data, pre-process and create TFDataset
(train_images_data,
train_labels_data) = mnist.read_data_sets("/tmp/mnist", "train")
(test_images_data,
test_labels_data) = mnist.read_data_sets("/tmp/mnist", "train")
train_images_data = (train_images_data[:data_num] -
mnist.TRAIN_MEAN) / mnist.TRAIN_STD
train_labels_data = train_labels_data[:data_num].astype(np.int)
test_images_data = (test_images_data[:data_num] -
mnist.TRAIN_MEAN) / mnist.TRAIN_STD
test_labels_data = (test_labels_data[:data_num]).astype(np.int)
dataset = TFDataset.from_ndarrays(
(train_images_data, train_labels_data),
batch_size=360,
val_tensors=(test_images_data, test_labels_data))
# 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))
acc = accuracy(logits, labels)
# create a optimizer
optimizer = TFOptimizer.from_loss(loss,
Adam(1e-3),
metrics={"acc": acc},
model_dir="/tmp/lenet/")
# kick off training
optimizer.optimize(end_trigger=MaxEpoch(max_epoch))
saver = tf.train.Saver()
saver.save(optimizer.sess, "/tmp/lenet/model")
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=[(None, 28, 28, 1), (None, 1)],
types=[tf.float32, tf.int32]
)
# construct the model from TFDataset
images, labels = dataset.inputs
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))
# kick off training
# you may change the MaxIteration to MaxEpoch(5) to make it converge
optimizer.optimize(end_trigger=MaxIteration(20), batch_size=280)
# evaluate
(images_data, labels_data) = mnist.read_data_sets("/tmp/mnist", "test")
images_data = normalizer(images_data, mnist.TRAIN_MEAN, mnist.TRAIN_STD)
predictions = tf.argmax(logits, axis=1)
predictions_data, loss_value = optimizer.sess.run([predictions, loss],
feed_dict={images: images_data,
labels: labels_data})
print(np.mean(np.equal(predictions_data, labels_data)))
print(loss_value)
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,
features=(tf.float32, [28, 28, 1]),
labels=(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))
acc = accuracy(logits, labels)
# create a optimizer
optimizer = TFOptimizer.from_loss(loss, Adam(1e-3),
metrics={"acc": acc},
model_dir="/tmp/lenet/")
# kick off training
optimizer.optimize(end_trigger=MaxEpoch(max_epoch))
saver = tf.train.Saver()
saver.save(optimizer.sess, "/tmp/lenet/model")
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/")
def build_train_op(image_tensor, label_tensor, is_training):
lenet_argscope = lenet_arg_scope(weight_decay=FLAGS.weight_decay)
global_step = tf.get_variable(name="global_step",
shape=[],
dtype=tf.int32,
trainable=False)
with slim.arg_scope(lenet_argscope):
logits, end_points = lenet(image_tensor, is_training=is_training)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=label_tensor))
accuracy = tf.reduce_sum(
tf.cast(
tf.equal(tf.cast(tf.argmax(logits, 1), tf.int32), label_tensor),
tf.int32))
end_points['loss'], end_points['accuracy'] = loss, accuracy
if is_training:
optimizer = tf.train.AdadeltaOptimizer(
learning_rate=FLAGS.learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
return train_op, end_points
else:
return None, end_points
def main(options, data_num):
data_path = '/tmp/mnist' if not options.data_path else options.data_path
sc = init_nncontext()
# get data, pre-process and create TFDataset
(images_data, labels_data) = mnist.read_data_sets(data_path, "test")
images_data = (images_data[:data_num] - mnist.TRAIN_MEAN) / mnist.TRAIN_STD
labels_data = labels_data[:data_num].astype(np.int32)
dataset = TFDataset.from_ndarrays((images_data, labels_data),
batch_per_thread=20)
# 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=False)
predictions = tf.to_int32(tf.argmax(logits, axis=1))
correct = tf.expand_dims(tf.to_int32(tf.equal(predictions, labels)),
axis=1)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, "/tmp/lenet/model")
predictor = TFPredictor(sess, [correct])
accuracy = predictor.predict().mean()
print("predict accuracy is %s" % accuracy)
def classify_image(filepath):
with tf.Graph().as_default():
image = open(filepath, 'rb')
# Open specified url and load image as a string
image_string = image.read()
# Decode string into matrix with intensity values
image = tf.image.decode_png(image_string, channels=3)
# Resize the input image, preserving the aspect ratio
# and make a central crop of the resulted image.
# The crop will be of the size of the default image size of
# the network.
processed_image = lenet_preprocessing.preprocess_image(
image, image_size, image_size, is_training=False)
# Networks accept images in batches.
# The first dimension usually represents the batch size.
# In our case the batch size is one.
processed_images = tf.expand_dims(processed_image, 0)
# Create the model, use the default arg scope to configure
# the batch norm parameters. arg_scope is a very convenient
# feature of slim library -- you can define default
# parameters for layers -- like stride, padding etc.
with slim.arg_scope(lenet.lenet_arg_scope()):
logits, _ = lenet.lenet(processed_images,
num_classes=11,
is_training=False)
# In order to get probabilities we apply softmax on the output.
probabilities = tf.nn.softmax(logits)
# Create a function that reads the network weights
# from the checkpoint file that you downloaded.
# We will run it in session later.
init_fn = slim.assign_from_checkpoint_fn(
os.path.join(checkpoints_dir, 'model.ckpt-10000'),
slim.get_model_variables(None))
with tf.Session() as sess:
# Load weights
init_fn(sess)
# We want to get predictions, image as numpy matrix
# and resized and cropped piece that is actually
# being fed to the network.
np_image, network_input, probabilities = sess.run(
[image, processed_image, probabilities])
probabilities = probabilities[0, 0:]
sorted_inds = [
i[0]
for i in sorted(enumerate(-probabilities), key=lambda x: x[1])
]
for i in range(11):
index = sorted_inds[i]
print('Probability %0.2f => [%s]' %
(probabilities[index], names[index]))
return sorted_inds[0], probabilities
# Network Parameters
n_input = 28 # MNIST data input (img shape: 28*28)
n_classes = 5 # MNIST total classes (0-4 digits)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# tf Graph input
X = tf.placeholder("float", [None, n_input, n_input, 3])
#X = tf.placeholder("float", [None, 784])
Y = tf.placeholder("float", [None, n_classes])
alpha_place_holder = tf.placeholder(tf.float32, shape=())
lr_place_holder = tf.placeholder(tf.float32, shape=())
with slim.arg_scope(lenet.lenet_arg_scope()):
#training and test graph
net_output, _ = lenet.lenet(X,
embedding_dim=args.embedding_dim,
use_bn=args.bn) #
test_net_output, _ = lenet.lenet(X,embedding_dim = args.embedding_dim,\
reuse = True, is_training = False, use_bn = args.bn)#
#retrieval layer
with tf.variable_scope('retrieval'):
retrieval_layer = layers.retrieval_layer_2(args.embedding_dim, n_classes)
out_layer, bottleneck = retrieval_layer.get_output(net_output,\
alpha = alpha_place_holder, l2_norm = args.l2_norm,\
learn_norm = args.learn_norm, norm_weights = args.norm_weights)
test_out_layer, test_bottleneck = retrieval_layer.get_output(test_net_output,\
def main(max_epoch, data_num):
args = parser.parse_args()
cluster_mode = args.cluster_mode
if cluster_mode.startswith("yarn"):
hadoop_conf = os.environ.get("HADOOP_CONF_DIR")
assert hadoop_conf, "Directory path to hadoop conf not found for yarn-client mode. Please " \
"set the environment variable HADOOP_CONF_DIR"
spark_conf = create_spark_conf().set("spark.executor.memory", "5g") \
.set("spark.executor.cores", 2) \
.set("spark.executor.instances", 2) \
.set("spark.driver.memory", "2g")
if cluster_mode == "yarn-client":
sc = init_nncontext(spark_conf,
cluster_mode="yarn-client",
hadoop_conf=hadoop_conf)
else:
sc = init_nncontext(spark_conf,
cluster_mode="yarn-cluster",
hadoop_conf=hadoop_conf)
else:
sc = init_nncontext()
# get data, pre-process and create TFDataset
(train_images_data,
train_labels_data) = mnist.read_data_sets("/tmp/mnist", "train")
(test_images_data,
test_labels_data) = mnist.read_data_sets("/tmp/mnist", "train")
train_images_data = (train_images_data[:data_num] -
mnist.TRAIN_MEAN) / mnist.TRAIN_STD
train_labels_data = train_labels_data[:data_num].astype(np.int)
test_images_data = (test_images_data[:data_num] -
mnist.TRAIN_MEAN) / mnist.TRAIN_STD
test_labels_data = (test_labels_data[:data_num]).astype(np.int)
dataset = TFDataset.from_ndarrays(
(train_images_data, train_labels_data),
batch_size=360,
val_tensors=(test_images_data, test_labels_data))
# 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))
acc = accuracy(logits, labels)
# create a optimizer
optimizer = TFOptimizer.from_loss(loss,
Adam(1e-3),
metrics={"acc": acc},
model_dir="/tmp/lenet/")
# kick off training
optimizer.optimize(end_trigger=MaxEpoch(max_epoch))
saver = tf.train.Saver()
saver.save(optimizer.sess, "/tmp/lenet/model")
