def export():
tf.logging.set_verbosity(tf.logging.INFO)
inp = tf.placeholder(tf.float32, [None], name=INPUT_TENSOR_NAME)
model_fn(dict(data=inp), None, tf.estimator.ModeKeys.PREDICT)
sess = get_session()
tf.train.Saver().save(sess, os.path.join(EXPORT_FOLDER, 'checkpoint.ckpt'))
tf.train.write_graph(sess.graph_def, EXPORT_FOLDER, 'graph.pbtxt', True)
sess.close()
print("Freezing graph")
lp = get_latest_export()
ckpt = tf.train.get_checkpoint_state(EXPORT_FOLDER)
freeze_graph(
os.path.join(EXPORT_FOLDER, 'graph.pbtxt'),
None,
False,
ckpt.model_checkpoint_path,
OUTPUT_TENSOR_NAME,
'save/restore_all',
'save/Const:0',
os.path.join(EXPORT_FOLDER, 'fozen.pb'),
True,
''
)
input_graph_def = tf.GraphDef()
with tf.gfile.Open(os.path.join(EXPORT_FOLDER, 'fozen.pb'), "rb") as f:
input_graph_def.ParseFromString(f.read())
output_graph = optimize_for_inference(
input_graph_def,
[INPUT_TENSOR_NAME],
[OUTPUT_TENSOR_NAME],
tf.float32.as_datatype_enum
)
with tf.gfile.FastGFile(EXPORTED_MODEL_NAME, 'w') as f:
f.write(output_graph.SerializeToString())
def build_and_run_exports(latest, job_dir, serving_input_fn, hidden_units):
"""Given the latest checkpoint file export the saved model.
Args:
latest (string): Latest checkpoint file
job_dir (string): Location of checkpoints and model files
name (string): Name of the checkpoint to be exported. Used in building the
export path.
hidden_units (list): Number of hidden units
learning_rate (float): Learning rate for the SGD
"""
prediction_graph = tf.Graph()
with prediction_graph.as_default():
features, inputs_dict = serving_input_fn()
prediction_dict = model.model_fn(
model.PREDICT,
features.copy(),
None, # labels
hidden_units=hidden_units,
learning_rate=None # learning_rate unused in prediction mode
)
saver = tf.train.Saver()
with tf.Session(graph=prediction_graph) as session:
session.run([tf.local_variables_initializer(), tf.tables_initializer()])
saver.restore(session, latest)
saved_model_util.simple_save(
session, os.path.join(job_dir, 'export'), inputs_dict, prediction_dict)
def test(args):
# import parmeter data
hps = params_utils.parse_params(params.mnist_classification_args)
# load dataset
dataset = load_mnist(show_info=False)
# inputs setting
with tf.variable_scope('input'):
if hps.data_params.is_flattened:
shape = [None, np.prod(hps.data_params.image_size)]
else:
shape = [None] + list(hps.data_params.image_size)
x = tf.placeholder(tf.float32, shape)
y = tf.placeholder(tf.float32, [None, 10])
is_training = tf.placeholder(tf.bool, shape=None)
# format image
if hps.data_params.is_flattened:
if len(hps.data_params.image_size) == 3:
image_shape = [-1] + list(hps.data_params.image_size)
elif len(hps.data_params.image_size) == 2:
image_shape = [-1] + list(hps.data_params.image_size) + [1]
else:
raise NotImplementedError('image shape should be NHW or NHWC')
_x = tf.reshape(x, image_shape)
# input -> model -> output
y_hat = model.model_fn(_x, hps, is_training)
with tf.name_scope('metrics'):
with tf.name_scope('accuracy'):
correctness = tf.equal(tf.argmax(y_hat), tf.argmax(y))
correctness = tf.cast(correctness, tf.float32)
accuracy = tf.reduce_mean(correctness)
saver = tf.train.Saver()
path_prefix = Path(args.prefix)
save_path = hps.paths.model_path / path_prefix
ckpt = tf.train.get_checkpoint_state(save_path)
with tf.Session() as sess:
if ckpt:
print('restore variable')
last_model = ckpt.model_checkpoint_path
saver.restore(sess, last_model)
else:
raise Exception()
accs = []
for step in tqdm(range(hps.hyper_parameters.step)):
batch = dataset.test.next_batch(hps.hyper_parameters.batch_size)
acc = sess.run(accuracy,
feed_dict={
x: batch[0],
y: batch[1],
is_training: False
})
accs.append(acc)
print(np.mean(accs))
def evaluate(args):
# Build model
model = model_fn(
first_layer=[args.inp_kernel, args.inp_window],
gcn_layers=[args.n_GC_units] * args.n_GC_layers,
conv_layer_filters=[int(k) for k in args.conv_kernels.split(',')],
conv_layer_windows=[int(k) for k in args.conv_windows.split(',')],
nBins=1250,
lr=args.lr,
nMarks=args.n_marks,
verbose=1
)
model.load_weights('v11_temp_model_7.h5')
# Use the model to predict chromosome 2
ch = 'chr2'
res = 1000 # Input resolution: 1000 bp
# Load epigenetic data
epi_names = ['ATAC_seq', 'CTCF', 'H3K4me1', 'H3K4me3',
'H3K9ac', 'H3K27ac', 'H3K27me3', 'H3K36me3']
epigenetic_data = load_epigenetic_data([ch], epi_names, args.cell_line)
path = '/nfs/turbo/umms-drjieliu/proj/4dn/data/microC/high_res_map_project_training_data'
for pos in range(100000, 242100000 - 249999, 125000):
print(pos)
hic = np.load(f'{path}/HiC/{args.cell_line}/{ch}/{ch}_{res}bp_{pos}_{pos + 250000}.npy')
epi = epigenetic_data[ch][pos // 200 - (args.inp_window // 2): pos // 200 + 1250 + (args.inp_window // 2), :]
hics = np.array([hic])
epis = np.array([epi])
m = model.predict([hics, epis])[0, :, :] # Model Input: HiC and Epigenetic data
np.save(f'outputs/pred_{ch}_{res}bp_{pos}.npy', m)
def dispatch(train_files,
eval_files,
job_dir,
train_steps,
eval_steps,
train_batch_size,
eval_batch_size,
learning_rate,
eval_frequency,
first_layer_size,
num_layers,
scale_factor,
eval_num_epochs,
num_epochs,
checkpoint_epochs):
retinopathy_model = model.model_fn(CLASS_SIZE)
try:
os.makedirs(job_dir)
except:
pass
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
checkpoint_path = FILE_PATH
if not job_dir.startswith("gs://"):
checkpoint_path = os.path.join(job_dir, checkpoint_path)
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(
checkpoint_path,
monitor='val_loss',
verbose=2,
period=checkpoint_epochs,
mode='max')
# Continuous eval callback
evaluation = ContinuousEval(eval_frequency,
job_dir)
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(
log_dir=os.path.join(job_dir, 'logs'),
histogram_freq=0,
write_graph=True,
embeddings_freq=0)
callbacks=[checkpoint, evaluation, tblog]
[X_train,Y_train]=model.read_train_data()
datagen = ImageDataGenerator(
shear_range=0.1,
zoom_range=0.1,
horizontal_flip=True)
def test_set(x_test_labelled, x_test_unlabelled, y_test_labelled,
y_test_unlabelled, params):
'''
Helper function for training
'''
# Prepare input and model
x_l = tf.placeholder(x_test_labelled.dtype, x_test_labelled.shape)
y_l = tf.placeholder(y_test_labelled.dtype, y_test_labelled.shape)
x_u = tf.placeholder(x_test_unlabelled.dtype, x_test_unlabelled.shape)
y_u = tf.placeholder(y_test_unlabelled.dtype, y_test_unlabelled.shape)
inputs = test_input_fn(x_l, x_u, y_l, y_u, params['batch_size'],
params['buffer_size'])
model = model_fn(inputs, tf.estimator.ModeKeys.EVAL, params)
l_accuracy = model['l_accuracy']
u_accuracy = model['u_accuracy']
update_l_acc = model['update_l_acc']
update_u_acc = model['update_u_acc']
acc_init = model['acc_init']
loss_sum = 0.0
total_l_acc = 0.0
total_u_acc = 0.0
step = 0
saver = tf.train.Saver()
#chkp.print_tensors_in_checkpoint_file(os.path.join(params['model_dir'], 'model.ckpt'), tensor_name='', all_tensors=False, all_tensor_names=True)
with tf.Session() as sess:
tf.logging.info('Starting testing')
sess.run(model['var_init'])
sess.run(acc_init)
# Restoring trained weights from training in params['model_dir']
saver.restore(sess, os.path.join(params['model_dir'], 'model.ckpt'))
sess.run(
model['iter_init'], {
x_l: x_test_labelled,
x_u: x_test_unlabelled,
y_l: y_test_labelled,
y_u: y_test_unlabelled
})
#writer = tf.summary.FileWriter(os.path.join(params['model_dir'], 'test_summaries'), sess.graph)
while True:
try:
step += 1
# Update accuracy values
_, _, l_acc_val, u_acc_val = \
sess.run([update_l_acc, update_u_acc, l_accuracy, u_accuracy])
total_l_acc = l_acc_val
total_u_acc = u_acc_val
if params['log_step']:
if step % params['log_step'] == 0:
# Log accuracies so far if appropriate
tf.logging.info(
'Loss: {}; Labelled Accuracy: {}; Unlabelled Accuracy: {}'
.format(loss_sum / step, total_l_acc, total_u_acc))
except tf.errors.OutOfRangeError:
# At the end of the dataset
break
return total_l_acc, total_u_acc
def train():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.log_device_placement = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)
model = model_fn((height, width, 3), learning_rate)
model.fit(training_input_fn, steps_per_epoch=steps_per_epoch, epochs=epochs,
validation_data=validation_input_fn, validation_steps=1,
callbacks=[tf.keras.callbacks.TensorBoard(log_dir=log_dir)])
def build_and_run_exports(latest, job_dir, name, serving_input_fn,
hidden_units):
"""Given the latest checkpoint file export the saved model.
Args:
latest (string): Latest checkpoint file
job_dir (string): Location of checkpoints and model files
name (string): Name of the checkpoint to be exported. Used in building the
export path.
hidden_units (list): Number of hidden units
learning_rate (float): Learning rate for the SGD
"""
prediction_graph = tf.Graph()
exporter = tf.saved_model.builder.SavedModelBuilder(
os.path.join(job_dir, 'export', name))
with prediction_graph.as_default():
features, inputs_dict = serving_input_fn()
prediction_dict = model.model_fn(
model.PREDICT,
features,
None, # labels
hidden_units=hidden_units,
learning_rate=None # learning_rate unused in prediction mode
)
saver = tf.train.Saver()
inputs_info = {
name: tf.saved_model.utils.build_tensor_info(tensor)
for name, tensor in inputs_dict.iteritems()
}
output_info = {
name: tf.saved_model.utils.build_tensor_info(tensor)
for name, tensor in prediction_dict.iteritems()
}
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs_info,
outputs=output_info,
method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME)
with tf.Session(graph=prediction_graph) as session:
session.run(
[tf.local_variables_initializer(),
tf.tables_initializer()])
saver.restore(session, latest)
exporter.add_meta_graph_and_variables(
session,
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature_def
},
)
exporter.save()
def dispatch(train_files, eval_files, job_dir, train_steps, eval_steps,
train_batch_size, eval_batch_size, learning_rate, eval_frequency,
first_layer_size, num_layers, scale_factor, eval_num_epochs,
num_epochs, checkpoint_epochs):
census_model = model.model_fn(INPUT_SIZE, CLASS_SIZE)
try:
os.makedirs(job_dir)
except:
pass
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
checkpoint_path = FILE_PATH
if not job_dir.startswith("gs://"):
checkpoint_path = os.path.join(job_dir, checkpoint_path)
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(checkpoint_path,
monitor='val_loss',
verbose=1,
period=checkpoint_epochs,
mode='max')
# Continuous eval callback
evaluation = ContinuousEval(eval_frequency,
eval_files,
learning_rate,
job_dir,
steps=train_steps)
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(log_dir=os.path.join(job_dir, 'logs'),
histogram_freq=0,
write_graph=True,
embeddings_freq=0)
callbacks = [checkpoint, evaluation, tblog]
census_model.fit_generator(model.generator_input(train_files,
chunk_size=CHUNK_SIZE),
steps_per_epoch=train_steps,
epochs=num_epochs,
callbacks=callbacks)
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
if job_dir.startswith("gs://"):
census_model.save(CENSUS_MODEL)
copy_file_to_gcs(job_dir, CENSUS_MODEL)
else:
census_model.save(os.path.join(job_dir, CENSUS_MODEL))
# Convert the Keras model to TensorFlow SavedModel
model.to_savedmodel(census_model, os.path.join(job_dir, 'export'))
def build_and_run_exports(latest, job_dir, serving_input_fn, hidden_units):
"""Given the latest checkpoint file export the saved model.
Args:
latest (string): Latest checkpoint file
job_dir (string): Location of checkpoints and model files
name (string): Name of the checkpoint to be exported. Used in building the
export path.
hidden_units (list): Number of hidden units
learning_rate (float): Learning rate for the SGD
"""
prediction_graph = tf.Graph()
exporter = tf.saved_model.builder.SavedModelBuilder(
os.path.join(job_dir, 'export'))
with prediction_graph.as_default():
features, inputs_dict = serving_input_fn()
prediction_dict = model.model_fn(
model.PREDICT,
features.copy(),
None, # labels
hidden_units=hidden_units,
learning_rate=None # learning_rate unused in prediction mode
)
saver = tf.train.Saver()
inputs_info = {
name: tf.saved_model.utils.build_tensor_info(tensor)
for name, tensor in inputs_dict.iteritems()
}
output_info = {
name: tf.saved_model.utils.build_tensor_info(tensor)
for name, tensor in prediction_dict.iteritems()
}
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs_info,
outputs=output_info,
method_name=sig_constants.PREDICT_METHOD_NAME
)
with tf.Session(graph=prediction_graph) as session:
session.run([tf.local_variables_initializer(), tf.tables_initializer()])
saver.restore(session, latest)
exporter.add_meta_graph_and_variables(
session,
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
sig_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_def
},
legacy_init_op=main_op()
)
exporter.save()
def train_and_evaluate(args):
# Build model
model = model_fn(
first_layer=[args.inp_kernel, args.inp_window],
gcn_layers=[args.n_GC_units] * args.n_GC_layers,
conv_layer_filters=[int(k) for k in args.conv_kernels.split(',')],
conv_layer_windows=[int(k) for k in args.conv_windows.split(',')],
nBins=1250,
lr=args.lr,
nMarks=args.n_marks,
verbose=1)
# Load chromosomes
st_ed_pos = chromosome_sizes[args.cell_line]
chromosomes = ['chr' + elm for elm in args.chrs.split(',')
] if args.chrs.lower() != 'all' else st_ed_pos.keys()
# Load resolutions
resolutions = [int(elm) for elm in args.inp_resolutions.split(',')]
# Load epigenetic data
epi_names = [
'ATAC_seq', 'CTCF', 'H3K4me1', 'H3K4me3', 'H3K9ac', 'H3K27ac',
'H3K27me3', 'H3K36me3'
]
epigenetic_data = load_epigenetic_data(args.cell_line, chromosomes,
epi_names)
# epigenetic_data = load_processed_epigenetic_data_2(chromosomes, epi_names, args.cell_line)
for i in range(args.epochs):
print('Epoch', i, ':')
t1 = time.time()
for (epi, hics), micros in generate_batches(args.cell_line,
chromosomes, resolutions,
epi_names, epigenetic_data,
args.batch_size,
args.inp_window):
t2 = time.time()
print(' - Loading data:', t2 - t1, 's')
model.train_on_batch([hics, epi], micros)
t3 = time.time()
print(' - Training:', t3 - t2, 's')
mse = model.evaluate([hics, epi],
micros,
batch_size=args.batch_size,
verbose=0)
t1 = time.time()
print(' - Evaluating:', t1 - t3, 's')
print(' - MSE:', mse)
if i % args.checkpoint_frequency == 0:
model.save_weights('temp_model_{0}.h5'.format(i))
def load_model(args):
model = model_fn(
first_layer=[args.inp_kernel, args.inp_window],
gcn_layers=[args.n_GC_units] * args.n_GC_layers,
conv_layer_filters=[int(k) for k in args.conv_kernels.split(',')],
conv_layer_windows=[int(k) for k in args.conv_windows.split(',')],
nBins=1250,
lr=args.lr,
nMarks=args.n_marks,
verbose=1)
model.load_weights('v16_temp_model_11.h5')
return model
def _run_export(self):
export_dir = 'export_ckpt_' + re.findall('\d+',
self._latest_checkpoint)[-1]
tf.logging.info('Exporting model from checkpoint {0}'.format(
self._latest_checkpoint))
prediction_graph = tf.Graph()
try:
exporter = tf.saved_model.builder.SavedModelBuilder(
os.path.join(self._checkpoint_dir, export_dir))
except IOError:
tf.logging.info(
'Checkpoint {0} already exported, continuing...'.format(
self._latest_checkpoint))
return
with prediction_graph.as_default():
image, name, inputs_dict = model.serving_input_fn()
prediction_dict = model.model_fn(model.PREDICT, name, image, None,
6, None)
saver = tf.train.Saver()
inputs_info = {
name: tf.saved_model.utils.build_tensor_info(tensor)
for name, tensor in inputs_dict.iteritems()
}
output_info = {
name: tf.saved_model.utils.build_tensor_info(tensor)
for name, tensor in prediction_dict.iteritems()
}
signature_def = tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs_info,
outputs=output_info,
method_name=sig_constants.PREDICT_METHOD_NAME)
with tf.Session(graph=prediction_graph) as session:
saver.restore(session, self._latest_checkpoint)
exporter.add_meta_graph_and_variables(
session,
tags=[tf.saved_model.tag_constants.SERVING],
signature_def_map={
sig_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
signature_def
},
legacy_init_op=my_main_op())
exporter.save()
def dispatch(train_files, eval_files, job_dir, learning_rate, eval_frequency,
num_epochs, checkpoint_epochs):
# setting the seed for reproducibility
np.random.seed(13)
forecast_model = model.model_fn()
scaler = model.build_scaler(train_files + eval_files)
try:
os.makedirs(job_dir)
except Exception as e:
print(e)
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
checkpoint_path = CHECKPOINT_PATH
if not job_dir.startswith("gs://"):
checkpoint_path = os.path.join(job_dir, checkpoint_path)
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(checkpoint_path,
verbose=1,
period=checkpoint_epochs)
# Continuous eval callback
with ContinuousEval(eval_frequency, eval_files, learning_rate, job_dir,
scaler) as evaluation:
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(log_dir=os.path.join(
job_dir, 'logs'),
histogram_freq=0,
write_graph=True,
embeddings_freq=0)
callbacks = [checkpoint, evaluation, tblog]
x, y = model.load_features(train_files, scaler)
forecast_model.fit(x, y, epochs=num_epochs, callbacks=callbacks)
# Unhappy hack to work around h5py not being able to write to GCS.
# Force snapshots and saves to local filesystem, then copy them over to GCS.
if job_dir.startswith("gs://"):
forecast_model.save(MODEL_FILENAME)
copy_file_to_gcs(job_dir, MODEL_FILENAME)
else:
forecast_model.save(os.path.join(job_dir, MODEL_FILENAME))
def dispatch(train_files, eval_files, job_dir, train_steps, eval_steps,
train_batch_size, eval_batch_size, learning_rate, eval_frequency,
first_layer_size, num_layers, scale_factor, eval_num_epochs,
num_epochs, checkpoint_epochs):
census_model = model.model_fn(INPUT_SIZE, CLASS_SIZE)
try:
os.makedirs(job_dir)
except:
pass
# Model checkpoint callback
checkpoint = keras.callbacks.ModelCheckpoint(os.path.join(
job_dir, FILE_PATH),
monitor='val_loss',
verbose=1,
period=checkpoint_epochs,
mode='max')
# Continuous eval callback
evaluation = ContinuousEval(eval_frequency, eval_files, learning_rate,
job_dir)
# Tensorboard logs callback
tblog = keras.callbacks.TensorBoard(log_dir=os.path.join(job_dir, 'logs'),
histogram_freq=0,
write_graph=True,
embeddings_freq=0)
# TODO: This needs to be fixed in h5py so that writes to GCS are possible
# Don't attempt to create checkpoints on Cloud ML Engine for now because
# h5py doesn't come with native GCS write capability
if job_dir.startswith('gs://'):
callbacks = [evaluation, tblog]
else:
callbacks = [checkpoint, evaluation, tblog]
start_time = time.time()
census_model.fit_generator(model.generator_input(train_files,
chunk_size=CHUNK_SIZE),
steps_per_epoch=train_steps,
epochs=num_epochs,
callbacks=callbacks)
print "\nTime used.", time.time() - start_time
census_model.save(os.path.join(job_dir, CENSUS_MODEL))
def run(args):
ms.context.set_context(
mode=ms.context.GRAPH_MODE,
device_target=args.device,
save_graphs=False,
)
net = model_fn()
loss = ms.nn.loss.SoftmaxCrossEntropyWithLogits(
sparse=True,
reduction='mean',
)
opt = build_optimizer(args, net)
if args.mode == 'init':
save_checkpoint(
net,
ckpt_file_name=os.path.join('seeds', '%d.ckpt' % (time.time())),
)
if args.mode == 'train':
ds_train = create_dataset(
data_path=os.path.join(args.data_path, 'train'),
batch_size=args.device_batch_size,
)
if args.init_ckpt:
print('using init checkpoint %s' % (args.init_ckpt))
load_ckpt(net, args.init_ckpt)
train(args, net, loss, opt, ds_train)
if args.mode == 'test':
ds_test = create_dataset(
data_path=os.path.join(args.data_path, 'test'),
batch_size=args.device_batch_size,
)
if args.ckpt_files:
checkpoints = args.ckpt_files.split(',')
else:
steps = [10, 20, 30, 40]
checkpoints = [get_ckpt_file_name(args, i) for i in steps]
print('will test %d checkpoints' % (len(checkpoints)))
# for i, n in enumerate(checkpoints):
# print('[%d]=%s' % (i, n))
test(args, net, loss, opt, ds_test, checkpoints)
def train_and_evaluate(hparams):
img_shape, num_classes, test_input_fn, train_input_fn = get_dataset(hparams.dataset)
model = model_fn(img_shape, num_classes, hparams.learning_rate)
# model.summary()
model.load_weights('my_model_weights.h5')
strategy = {
'mirror': MirroredStrategy(num_gpus=hparams.num_gpus, prefetch_on_device=True),
'collective': CollectiveAllReduceStrategy(num_gpus_per_worker=hparams.num_gpus)
}[hparams.dist]
# config = tf.estimator.RunConfig(train_distribute=strategy, save_checkpoints_steps=500)
dist_config = tf.contrib.distribute.DistributeConfig(
train_distribute=strategy,
eval_distribute=strategy,
remote_cluster=None
)
session_config = tf.ConfigProto(
inter_op_parallelism_threads=0,
intra_op_parallelism_threads=0,
allow_soft_placement=True
)
config = tf.estimator.RunConfig(
save_checkpoints_steps=2000,
session_config=session_config,
experimental_distribute=dist_config
)
estimator = tf.keras.estimator.model_to_estimator(model,
model_dir=hparams.job_dir,
config=config)
train_spec = tf.estimator.TrainSpec(
input_fn=lambda: train_input_fn(hparams.batch_size),
max_steps=8000)
eval_spec = tf.estimator.EvalSpec(
input_fn=lambda: test_input_fn(hparams.batch_size),
steps=50,
start_delay_secs=10,
throttle_secs=10)
tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)
def evaluate_files(pattern, x_splits=7, y_splits=5):
"""
Evaluates the images matching the pattern and outputs the results as a generator
"""
image_reader = tf.WholeFileReader()
sip = tf.train.string_input_producer(
tf.train.match_filenames_once(pattern), num_epochs=1)
image_name, image_file = image_reader.read(sip)
image = tf.image.decode_png(image_file)
images = []
shape = tf.shape(image)
height, width = shape[0], shape[1]
dx = (width - CROPPED_IMAGE_SIZE) / (x_splits - 1)
dy = (height - CROPPED_IMAGE_SIZE) / (y_splits - 1)
for x in range(x_splits):
for y in range(y_splits):
images.append(
tf.image.crop_to_bounding_box(
image,
tf.minimum(tf.to_int32(dy * y),
height - CROPPED_IMAGE_SIZE),
tf.minimum(tf.to_int32(dx * x),
width - CROPPED_IMAGE_SIZE), CROPPED_IMAGE_SIZE,
CROPPED_IMAGE_SIZE))
images = tf.reshape(
tf.to_float(images) / 255.0,
(-1, CROPPED_IMAGE_SIZE, CROPPED_IMAGE_SIZE, 3))
nn = model_fn(dict(input=images), None, tf.estimator.ModeKeys.PREDICT)
preds = tf.reshape(nn.predictions['predictions'], (x_splits, y_splits))
sess = get_session()
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord)
try:
while True:
name, predictions = sess.run([image_name, preds])
yield name, predictions
except KeyboardInterrupt:
pass
except tf.errors.OutOfRangeError:
pass
finally:
coord.request_stop()
coord.join()
sess.close()
def create_model(args):
model = keras.Model(*model_fn(args))
if args.nb_ipus_per_replica > 1:
set_pipeline_options(model, args)
model.print_pipeline_stage_assignment_summary()
elif args.nb_ipus_per_replica == 1:
model.set_gradient_accumulation_options(
gradient_accumulation_steps_per_replica=args.
gradient_accumulation_count,
offload_weight_update_variables=False)
model.compile(
optimizer=get_optimizer(args),
loss=dice_ce_loss,
# Number of micro batches to process sequentially in a single execution
steps_per_execution=args.steps_per_execution
if args.nb_ipus_per_replica > 0 else None,
metrics=[dice_coef_accuracy_fn, ce_loss])
return model
def evaluate():
tf.logging.set_verbosity(tf.logging.INFO)
input_dict, label_dict = input_fn()
nn = model_fn(input_dict, None, tf.estimator.ModeKeys.PREDICT)
stats = PredictionStats()
sess = get_session()
coord = tf.train.Coordinator()
tf.train.start_queue_runners(sess, coord)
try:
while True:
pred, act = sess.run(
[nn.predictions['predictions'], label_dict['labels']])
stats.add_predictions(pred, act)
print('Predictions: %00000d, Accuracy: %.4f' %
(stats.get_amount(), stats.get_accuracy()))
except KeyboardInterrupt:
pass
finally:
coord.request_stop()
coord.join()
sess.close()
print()
stats.print_result()
class ModelWrapper(object):
config = load_config()
model = model_fn(config)
model_path = '../model/best/%s-%d' % (config.model_name, 5)
model.load_weights(model_path)
# Hack: This fixes a keras bug with TF backend in async environment,
# see https://github.com/keras-team/keras/issues/2397 for details.
graph = tf.get_default_graph()
print 'successfully loaded model from: %s' % model_path
@classmethod
def normalize(cls, img):
"""
cropping and zero-centering
"""
img = imutils.crop(img, std_size=(cls.config.width, cls.config.height))
img = (img - 128.0) / 255.0
return np.array([img])
@classmethod
def predict(cls, img):
with cls.graph.as_default():
X = cls.normalize(img)
return id2building[np.argmax(cls.model.predict(X)[0])]
type=str,
choices=['one', 'all'],
required=True)
args = parser.parse_args()
return args
if __name__ == '__main__':
args = get_args()
X = np.load(X_path)
y = np.load(y_path)
ids = np.load(ids_path)
config = load_config()
model = model_fn(config)
if args.mode == 'all':
for epoch in xrange(20):
model.load_weights('../model/%s-%d' % (config.model_name, epoch))
score = model.evaluate(X, y)
print 'epoch:', epoch
print 'score:', score
else:
model.load_weights('../model/best/%s-%d' %
(config.model_name, args.epoch))
score = model.evaluate(X, y)
print 'score:', score
def run():
config = Config() # Load configs
save_path = 'keras_models/keras_model' # Model save path
x_train_path = 'data/xtrain.txt'
x_test_path = 'data/xtest.txt'
y_train_path = 'data/ytrain.txt'
x_idx = prep.Indexer()
X = prep.read_file(x_train_path, raw=True)
y = prep.read_file(y_train_path, label=True)
t = CountVectorizer(analyzer='char',
ngram_range=(config.ngram, config.ngram))
t.fit(X)
X = prep.transform(X, t, x_idx)
X = np.array(pad_sequences(X, config.maxlen))
x_train, x_test, y_train, y_test = train_test_split(
X, y, test_size=config.test_size, shuffle=config.shuffle)
#############################################
# Train model
print("BEGINNING TRAINING")
tsv_logger = CSVLogger('training-data.tsv', append=True, separator='\t')
m = model_fn(config=config, input_length=x_idx.max_number() + 1)
# m = load_model(save_path)
m.fit(x_train,
y_train,
epochs=config.n_epochs,
batch_size=config.batch_size,
verbose=1,
shuffle=True,
callbacks=[tsv_logger],
validation_data=(x_test, y_test))
m.save(save_path)
print("MODEL REPORT")
score, acc = m.evaluate(x_test, y_test)
print("\nSCORE: ", score)
print("ACCURACY: ", acc)
pred = [np.argmax(label) for label in m.predict(x_test)]
report = classification_report(y_test, pred)
print(report)
###############################################
# Predict and write labels for xtest.txt
print("PREDICTION")
X = prep.read_file(x_test_path, raw=True)
X = prep.transform(X, t, x_idx, add_if_new=False)
X = np.array(pad_sequences(X, config.maxlen))
pred = [np.argmax(label) for label in m.predict(X)]
with open("".join(["keras_prediction/ytest.txt"]), "w+",
encoding="utf-8") as rec:
for label in pred:
rec.write("%s\n" % label)
rec.close()
def run(target, cluster_spec, is_chief, train_steps, eval_steps, job_dir,
train_files, eval_files, train_batch_size, eval_batch_size,
learning_rate, eval_frequency, first_layer_size, num_layers,
scale_factor, num_epochs, export_format):
"""Run the training and evaluation graph.
Args:
target (string): Tensorflow server target
is_chief (bool): Boolean flag to specify a chief server
train_steps (int): Maximum number of training steps
eval_steps (int): Number of steps to run evaluation for at each checkpoint.
if eval_steps is None, evaluation will run for 1 epoch.
job_dir (string): Output dir for checkpoint and summary
train_files (string): List of CSV files to read train data
eval_files (string): List of CSV files to read eval data
train_batch_size (int): Batch size for training
eval_batch_size (int): Batch size for evaluation
learning_rate (float): Learning rate for Gradient Descent
eval_frequency (int): Run evaluation frequency every n training steps.
Do not evaluate too frequently otherwise you will
pay for performance and do not evaluate too in-frequently
otherwise you will not know how soon to stop training.
Use default values to start with
first_layer_size (int): Size of the first DNN layer
num_layers (int): Number of hidden layers in the DNN
scale_factor (float): Decay rate for the size of hidden layers
num_epochs (int): Maximum number of training data epochs on which to train
export_format (str): One of 'JSON', 'CSV' or 'EXAMPLE'. The input format
for the outputed saved_model binary.
"""
# Calculate the number of hidden units
hidden_units = [
max(2, int(first_layer_size * scale_factor**i))
for i in range(num_layers)
]
# If the server is chief which is `master`
# In between graph replication Chief is one node in
# the cluster with extra responsibility and by default
# is worker task zero. We have assigned master as the chief.
#
# See https://youtu.be/la_M6bCV91M?t=1203 for details on
# distributed TensorFlow and motivation about chief.
if is_chief:
tf.logging.info("Created DNN hidden units {}".format(hidden_units))
evaluation_graph = tf.Graph()
with evaluation_graph.as_default():
# Features and label tensors
features, labels = model.input_fn(
eval_files,
num_epochs=None if eval_steps else 1,
batch_size=eval_batch_size,
shuffle=False)
# Accuracy and AUROC metrics
# model.model_fn returns the dict when EVAL mode
metric_dict = model.model_fn(model.EVAL,
features.copy(),
labels,
hidden_units=hidden_units,
learning_rate=learning_rate)
hooks = [
EvaluationRunHook(
job_dir,
metric_dict,
evaluation_graph,
eval_frequency,
eval_steps=eval_steps,
)
]
else:
hooks = []
# Create a new graph and specify that as default
with tf.Graph().as_default():
# Placement of ops on devices using replica device setter
# which automatically places the parameters on the `ps` server
# and the `ops` on the workers
#
# See:
# https://www.tensorflow.org/api_docs/python/tf/train/replica_device_setter
with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
# Features and label tensors as read using filename queue
features, labels = model.input_fn(train_files,
num_epochs=num_epochs,
batch_size=train_batch_size)
# Returns the training graph and global step tensor
train_op, global_step_tensor = model.model_fn(
model.TRAIN,
features.copy(),
labels,
hidden_units=hidden_units,
learning_rate=learning_rate)
# Creates a MonitoredSession for training
# MonitoredSession is a Session-like object that handles
# initialization, recovery and hooks
# https://www.tensorflow.org/api_docs/python/tf/train/MonitoredTrainingSession
with tf.train.MonitoredTrainingSession(
master=target,
is_chief=is_chief,
checkpoint_dir=job_dir,
hooks=hooks,
save_checkpoint_secs=20,
save_summaries_steps=50) as session:
# Global step to keep track of global number of steps particularly in
# distributed setting
step = global_step_tensor.eval(session=session)
# Run the training graph which returns the step number as tracked by
# the global step tensor.
# When train epochs is reached, session.should_stop() will be true.
while (train_steps is None
or step < train_steps) and not session.should_stop():
step, _ = session.run([global_step_tensor, train_op])
# Find the filename of the latest saved checkpoint file
latest_checkpoint = tf.train.latest_checkpoint(job_dir)
# Only perform this if chief
if is_chief:
build_and_run_exports(latest_checkpoint, job_dir,
model.SERVING_INPUT_FUNCTIONS[export_format],
hidden_units)
# Load Vocabularies
words = tf.contrib.lookup.index_table_from_file(path_words,
num_oov_buckets=1)
# Create the input data pipeline
logging.info("Creating the datasets...")
train_sentences = load_dataset_from_text(path_train_sentences)
eval_sentences = load_dataset_from_text(path_eval_sentences)
# Specify other parameters for the dataset and the model
params.eval_size = params.dev_size
params.buffer_size = params.train_size # buffer size for shuffling
params.id_pad_word = words.lookup(tf.constant(params.pad_word))
# Create the two iterators over the two datasets
train_inputs = input_fn('train', train_sentences, words, params)
eval_inputs = input_fn('eval', eval_sentences, words, params)
logging.info("- done.")
# Define the models (2 different set of nodes that share weights for train and eval)
logging.info("Creating the model...")
train_model_spec = model_fn('train', train_inputs, params)
eval_model_spec = model_fn('eval', eval_inputs, params, reuse=True)
logging.info("- done.")
# Train the model
logging.info("Starting training for {} epoch(s)".format(params.num_epochs))
train_and_evaluate(train_model_spec, eval_model_spec, args.model_dir,
params, args.restore_dir)
def build_model(self):
x_shape = self.train_iter.get_shape("x")
self.model = model_fn(x_shape=x_shape, rnn=self.config.rnn)
def main():
parser = argparse.ArgumentParser(
description='train the model for all model')
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--batch_size', type=int, default=32)
parser.add_argument('--model_dir', type=str, default='training_model')
parser.add_argument('--n_threads', type=int, default=4)
args = parser.parse_args()
epochs = args.epochs
batch_size = args.batch_size
model_dir = args.model_dir
input_shape = [28, 28, 1]
num_classes = 10
mnist_generator = MnistDataGenerator(args.batch_size)
custom_runner = CustomRunner(input_shape, num_classes, args.batch_size,
mnist_generator.train_iterator_heavy)
images, labels = custom_runner.get_inputs()
train_inputs = {'x': images, 'y': labels}
valid_inputs = {
'x': tf.placeholder(tf.float32, [
None,
] + input_shape),
'y': tf.placeholder(tf.float32, [None, num_classes])
}
train_model_spec = model_fn(train_inputs, is_train=True)
valid_model_spec = model_fn(valid_inputs, reuse=True, is_train=False)
os.makedirs(model_dir, exist_ok=True)
set_logger(os.path.join(model_dir, 'train.log'))
save_dir = os.path.join(model_dir, 'weights')
save_path = os.path.join(save_dir, 'epoch')
begin_at_epoch = 0
steps_per_epoch = mnist_generator.num_train_sample // batch_size
with tf.Session() as sess:
saver = tf.train.Saver(max_to_keep=5) # will keep last 5 epochs
sess.run(tf.global_variables_initializer())
tf.train.start_queue_runners(sess=sess)
custom_runner.start_threads(sess, n_threads=args.n_threads)
if os.path.isdir(save_dir):
restore_from = tf.train.latest_checkpoint(save_dir)
begin_at_epoch = int(restore_from.split('-')[-1])
saver.restore(sess, restore_from)
epochs += begin_at_epoch
for epoch in range(begin_at_epoch, epochs):
logging.info('Epoch {}/{}'.format(epoch + 1, epochs))
train_loss, train_acc = train(sess, train_model_spec,
steps_per_epoch)
valid_loss, valid_acc = eval(sess, valid_model_spec,
mnist_generator.test_iterator)
logging.info('train/acc: {:.4f}, train/loss: {:.4f}'.format(
train_acc, train_loss))
logging.info('valid/acc: {:.4f}, valid/loss: {:.4f}'.format(
valid_acc, valid_loss))
saver.save(sess, save_path, global_step=epoch + 1)
metrics = {}
# Count batches
print('Counting batches')
num_batches = 0
for _ in batch_generator(hparams, 'test'):
num_batches += 1
print(num_batches, 'batches')
for mode in ['eval', 'eval_sample']:
# Model
tf.reset_default_graph()
tf.set_random_seed(0)
inputs = tf.placeholder(tf.int32, [None, None])
labels = tf.placeholder(tf.int32, [None, None])
probs, losses = model_fn(inputs, hparams, mode, labels)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# This kostyl helps to run evaluation second time with different graph
gc.collect()
# Evaluate model on test set
with tf.Session() as sess:
# Load model
saver.restore(sess, os.path.join(hparams['model_path'], 'model.ckpt'))
# Get batches from generator
with PG(batch_generator(hparams, 'test'), 10) as g:
g = GeneratorLen(g, num_batches) # use progressbar
for batch_x, batch_y in tqdm(g):
def train(args: Dict):
# import parmeter data
hps = params_utils.parse_params(params.mnist_classification_args)
# load dataset
dataset = load_mnist(show_info=False)
# inputs setting
with tf.variable_scope('input'):
if hps.data_params.is_flattened:
shape = [None, np.prod(hps.data_params.image_size)]
else:
shape = [None] + list(hps.data_params.image_size)
x = tf.placeholder(tf.float32, shape)
y = tf.placeholder(tf.float32, [None, 10])
is_training = tf.placeholder(tf.bool, shape=None)
# format image
if hps.data_params.is_flattened:
if len(hps.data_params.image_size) == 3:
image_shape = [-1] + list(hps.data_params.image_size)
elif len(hps.data_params.image_size) == 2:
image_shape = [-1] + list(hps.data_params.image_size) + [1]
else:
raise NotImplementedError('image shape should be NHW or NHWC')
_x = tf.reshape(x, image_shape)
# input -> model -> output
y_hat = model.model_fn(_x, hps, is_training)
# setup metrics
with tf.name_scope('metrics'):
with tf.name_scope('accuracy'):
correctness = tf.equal(tf.argmax(y_hat), tf.argmax(y))
correctness = tf.cast(correctness, tf.float32)
accuracy = tf.reduce_mean(correctness)
with tf.name_scope('loss'):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y, logits=y_hat)
loss = tf.reduce_mean(cross_entropy)
# note:
# xxx is THE value, xxx_op is the OPERATION to update xxx
with tf.name_scope('train'):
train_loss, train_loss_op = tf.metrics.mean(loss,
name='train_loss')
train_acc, train_acc_op = tf.metrics.mean(accuracy,
name='train_acc')
tf.summary.scalar('loss', train_loss, collections=['train'])
tf.summary.scalar('acc', train_acc, collections=['train'])
with tf.name_scope('val'):
val_loss, val_loss_op = tf.metrics.mean(loss, name='val_loss')
val_acc, val_acc_op = tf.metrics.mean(accuracy, name='val_acc')
tf.summary.scalar('loss', val_loss, collections=['val'])
tf.summary.scalar('acc', val_acc, collections=['val'])
# metrics initializer
train_metrics_initialzie_op = tf.variables_initializer(
[var for var in tf.local_variables() if 'train/' in var.name])
val_metrics_initialize_op = tf.variables_initializer(
[var for var in tf.local_variables() if 'val/' in var.name])
# gathered summary operation
train_summary_op = tf.summary.merge_all('train')
val_summary_op = tf.summary.merge_all('val')
# optimizer settings
with tf.name_scope('optimizer'):
global_step = tf.Variable(0, trainable=False, name='global_step')
learning_rate = hps.hyper_parameters.learning_rate
if hps.hyper_parameters.optimizer == model.Optimizer.ADAM:
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
elif hps.hypter_paramters.optimizer == model.Optimizer.SGD:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=learning_rate)
else:
raise NotImplementedError('optimizer is in {}'.format(
list(model.Optimizer)))
train_step = optimizer.minimize(loss, global_step=global_step)
init_op = tf.global_variables_initializer()
local_init_op = tf.local_variables_initializer()
saver = tf.train.Saver()
path_prefix = Path(args.prefix)
save_path = hps.paths.model_path / path_prefix
save_path.mkdir(parents=True, exist_ok=True)
ckpt = tf.train.get_checkpoint_state(save_path)
with tf.Session() as sess:
if ckpt:
print('restore variable')
last_model = ckpt.model_checkpoint_path
saver.restore(sess, last_model)
sess.run(train_metrics_initialzie_op)
sess.run(val_metrics_initialize_op)
writer = tf.summary.FileWriter(hps.paths.log_path / path_prefix,
sess.graph)
else:
# initialize all variable and operations
sess.run(init_op)
sess.run(local_init_op)
sess.run(train_metrics_initialzie_op)
sess.run(val_metrics_initialize_op)
sess.run(init_op)
writer = tf.summary.FileWriter(hps.paths.log_path / path_prefix,
sess.graph)
for step in tqdm(range(hps.hyper_parameters.step)):
step += 1
batch = dataset.train.next_batch(hps.hyper_parameters.batch_size)
sess.run([train_step, train_loss_op, train_acc_op],
feed_dict={
x: batch[0],
y: batch[1],
is_training: True
})
# train_log
if step % 100 == 0:
summary, gstep = sess.run([train_summary_op, global_step])
writer.add_summary(summary, global_step=gstep)
sess.run(train_metrics_initialzie_op)
saver.save(sess,
save_path / Path('model.ckpt'),
global_step=global_step)
# validation log
if step % 1000 == 0:
sess.run(val_metrics_initialize_op)
for _ in range(50):
val_batch = dataset.train.next_batch(100)
sess.run([val_loss_op, val_acc_op],
feed_dict={
x: val_batch[0],
y: val_batch[1],
is_training: False
})
summary, gstep = sess.run([val_summary_op, global_step])
writer.add_summary(summary, global_step=gstep)
def run(target,
cluster_spec,
is_chief,
train_steps,
eval_steps,
job_dir,
train_files,
eval_files,
train_batch_size,
eval_batch_size,
learning_rate,
eval_frequency,
first_layer_size,
num_layers,
scale_factor,
num_epochs,
export_format):
"""Run the training and evaluation graph.
Args:
target (string): Tensorflow server target
is_chief (bool): Boolean flag to specify a chief server
train_steps (int): Maximum number of training steps
eval_steps (int): Number of steps to run evaluation for at each checkpoint.
if eval_steps is None, evaluation will run for 1 epoch.
job_dir (string): Output dir for checkpoint and summary
train_files (string): List of CSV files to read train data
eval_files (string): List of CSV files to read eval data
train_batch_size (int): Batch size for training
eval_batch_size (int): Batch size for evaluation
learning_rate (float): Learning rate for Gradient Descent
eval_frequency (int): Run evaluation frequency every n training steps.
Do not evaluate too frequently otherwise you will
pay for performance and do not evaluate too in-frequently
otherwise you will not know how soon to stop training.
Use default values to start with
first_layer_size (int): Size of the first DNN layer
num_layers (int): Number of hidden layers in the DNN
scale_factor (float): Decay rate for the size of hidden layers
num_epochs (int): Maximum number of training data epochs on which to train
export_format (str): One of 'JSON', 'CSV' or 'EXAMPLE'. The input format
for the outputed saved_model binary.
"""
# Calculate the number of hidden units
hidden_units = [
max(2, int(first_layer_size * scale_factor**i))
for i in range(num_layers)
]
# If the server is chief which is `master`
# In between graph replication Chief is one node in
# the cluster with extra responsibility and by default
# is worker task zero. We have assigned master as the chief.
#
# See https://youtu.be/la_M6bCV91M?t=1203 for details on
# distributed TensorFlow and motivation about chief.
if is_chief:
tf.logging.info("Created DNN hidden units {}".format(hidden_units))
evaluation_graph = tf.Graph()
with evaluation_graph.as_default():
# Features and label tensors
features, labels = model.input_fn(
eval_files,
num_epochs=None if eval_steps else 1,
batch_size=eval_batch_size,
shuffle=False
)
# Accuracy and AUROC metrics
# model.model_fn returns the dict when EVAL mode
metric_dict = model.model_fn(
model.EVAL,
features.copy(),
labels,
hidden_units=hidden_units,
learning_rate=learning_rate
)
hooks = [EvaluationRunHook(
job_dir,
metric_dict,
evaluation_graph,
eval_frequency,
eval_steps=eval_steps,
)]
else:
hooks = []
# Create a new graph and specify that as default
with tf.Graph().as_default():
# Placement of ops on devices using replica device setter
# which automatically places the parameters on the `ps` server
# and the `ops` on the workers
#
# See:
# https://www.tensorflow.org/api_docs/python/tf/train/replica_device_setter
with tf.device(tf.train.replica_device_setter(cluster=cluster_spec)):
# Features and label tensors as read using filename queue
features, labels = model.input_fn(
train_files,
num_epochs=num_epochs,
batch_size=train_batch_size
)
# Returns the training graph and global step tensor
train_op, global_step_tensor = model.model_fn(
model.TRAIN,
features.copy(),
labels,
hidden_units=hidden_units,
learning_rate=learning_rate
)
# Creates a MonitoredSession for training
# MonitoredSession is a Session-like object that handles
# initialization, recovery and hooks
# https://www.tensorflow.org/api_docs/python/tf/train/MonitoredTrainingSession
with tf.train.MonitoredTrainingSession(master=target,
is_chief=is_chief,
checkpoint_dir=job_dir,
hooks=hooks,
save_checkpoint_secs=20,
save_summaries_steps=50) as session:
# Global step to keep track of global number of steps particularly in
# distributed setting
step = global_step_tensor.eval(session=session)
# Run the training graph which returns the step number as tracked by
# the global step tensor.
# When train epochs is reached, session.should_stop() will be true.
while (train_steps is None or
step < train_steps) and not session.should_stop():
step, _ = session.run([global_step_tensor, train_op])
# Find the filename of the latest saved checkpoint file
latest_checkpoint = tf.train.latest_checkpoint(job_dir)
# Only perform this if chief
if is_chief:
build_and_run_exports(latest_checkpoint,
job_dir,
model.SERVING_INPUT_FUNCTIONS[export_format],
hidden_units)
import tensorflow as tf
import numpy as np
from multiprocessing_generator import ParallelGenerator as PG
import os
from tqdm import tqdm
from featurizer import Encoder
from model import model_fn
from my_utils import *
from load_hparams import hparams, PrintHparamsInfo
PrintHparamsInfo(hparams)
# Model
inputs = tf.placeholder(tf.float32, [None, hparams['latent_size']])
decoded = model_fn(inputs, hparams, 'decode')
text_encoder = Encoder(hparams)
# Add ops to save and restore all the variables.
saver = tf.train.Saver()
# Create output file
opath = os.path.join(hparams['output_path'], 'decoded.txt')
ofile = open(opath, 'w')
# Decode sequences
with tf.Session() as sess:
# Load model
saver.restore(sess, os.path.join(hparams['model_path'], 'model.ckpt'))
# Get vectors from file
for line in tqdm(
from model import model_fn, pipeline_model_fn
from utils import load_data, parse_params
import time, os
# Store class and shape information.
num_classes = 10
input_shape = (28, 28, 1)
x_train, y_train, x_test, y_test = load_data()
args = parse_params()
start = time.time() # 시작 시간 저장
if not args.use_ipu:
# Model.__init__ takes two required arguments, inputs and outputs.
model = keras.Model(*model_fn(input_shape, num_classes))
# Compile our model with Stochastic Gradient Descent as an optimizer
# and Categorical Cross Entropy as a loss.
model.compile(optimizer='sgd',
loss='categorical_crossentropy',
metrics=["accuracy"])
model.summary()
print('Training')
model.fit(x_train, y_train, epochs=3, batch_size=64)
print('Evaluation')
model.evaluate(x_test, y_test)
else:
# Standard IPU TensorFlow setup.
ipu_config = ipu.utils.create_ipu_config()
ipu_config = ipu.utils.auto_select_ipus(ipu_config, 2)
