def run_benchmark(self, gpus=0):
input_dim_1 = 40
input_dim_2 = 60
input_shape = (self.num_samples, input_dim_1, 60)
x, y = generate_text_input_data(input_shape)
# build the model: a single LSTM
model = Sequential()
model.add(LSTM(128, input_shape=(input_dim_1, input_dim_2)))
model.add(Dense(input_dim_2), activation='softmax')
optimizer = RMSprop(lr=0.01)
if keras.backend.backend() is "tensorflow" and gpus > 1:
model = multi_gpu_model(model, gpus=gpus)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
# create a distributed trainer for cntk
if keras.backend.backend() is "cntk" and gpus > 1:
start, end = cntk_gpu_mode_config(model, x.shape[0])
x = x[start: end]
y = y[start: end]
time_callback = timehistory.TimeHistory()
model.fit(x, y,
batch_size=self.batch_size,
epochs=self.epochs,
callbacks=[time_callback])
self.total_time = 0
for i in range(1, self.epochs):
self.total_time += time_callback.times[i]
def run_benchmark(self, gpus=0):
input_dim_1 = 40
input_dim_2 = 60
input_shape = (self.num_samples, input_dim_1, 60)
x, y = generate_text_input_data(input_shape)
# build the model: a single LSTM
model = Sequential()
model.add(LSTM(128, input_shape=(input_dim_1, input_dim_2)))
model.add(Dense(input_dim_2), activation='softmax')
optimizer = RMSprop(lr=0.01)
if keras.backend.backend() is "tensorflow" and gpus > 1:
model = multi_gpu_model(model, gpus=gpus)
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
# create a distributed trainer for cntk
if keras.backend.backend() is "cntk" and gpus > 1:
start, end = cntk_gpu_mode_config(model, x.shape[0])
x = x[start: end]
y = y[start: end]
time_callback = timehistory.TimeHistory()
model.fit(x, y,
batch_size=self.batch_size,
epochs=self.epochs,
callbacks=[time_callback])
self.total_time = 0
for i in range(1, self.epochs):
self.total_time += time_callback.times[i]
def run_benchmark(self,
gpus=0,
inference=False,
use_dataset_tensors=False):
print("Running model ", self.test_name)
keras.backend.set_learning_phase(True)
input_dim_1 = 40
input_dim_2 = 60
input_shape = (self.num_samples, input_dim_1, 60)
x_train, y_train = generate_text_input_data(input_shape)
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
# build the model: a single LSTM
model = Sequential()
model.add(
LSTM(128, input_shape=(input_dim_1, input_dim_2), unroll=True))
optimizer = RMSprop(lr=0.01)
if use_dataset_tensors:
# Create the dataset and its associated one-shot iterator.
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.repeat()
dataset = dataset.shuffle(10000)
dataset = dataset.batch(self.batch_size)
iterator = dataset.make_one_shot_iterator()
# Model creation using tensors from the get_next() graph node.
inputs, targets = iterator.get_next()
if use_dataset_tensors:
input_tensor = keras.layers.Input(tensor=inputs)
model.add(Dense(input_dim_2))
predictions = model(input_tensor)
model = keras.models.Model(input_tensor, predictions)
else:
model.add(Dense(input_dim_2, activation='softmax'))
# use multi gpu model for more than 1 gpu
if (keras.backend.backend() == 'tensorflow'
or keras.backend.backend() == 'mxnet') and gpus > 1:
model = multi_gpu_model(model, gpus=gpus)
if use_dataset_tensors:
model.compile(loss=crossentropy_from_logits,
optimizer=optimizer,
metrics=['accuracy'],
target_tensors=[targets])
else:
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
time_callback = timehistory.TimeHistory()
if use_dataset_tensors:
model.fit(epochs=self.epochs,
steps_per_epoch=15,
callbacks=[time_callback])
else:
model.fit(x_train,
y_train,
batch_size=self.batch_size,
epochs=self.epochs,
callbacks=[time_callback])
self.total_time = 0
for i in range(1, self.epochs):
self.total_time += time_callback.times[i]
if keras.backend.backend() == "tensorflow":
keras.backend.clear_session()
def run_benchmark(self,
gpus=0,
inference=False,
use_dataset_tensors=False,
epochs=20):
# prepare logging
# file name: backend_data_format_dataset_model_batch_size_gpus.log
log_file = keras.backend.backend(
) + '_' + keras.backend.image_data_format(
) + '_lstm_synthetic_batch_size_' + str(
self.batch_size) + '_' + str(gpus) + 'gpus.log' # nopep8
logging.basicConfig(level=logging.INFO, filename=log_file)
self.epochs = epochs
print("Running model ", self.test_name)
keras.backend.set_learning_phase(True)
input_dim_1 = 40
input_dim_2 = 60
input_shape = (self.num_samples, input_dim_1, 60)
x_train, y_train = generate_text_input_data(input_shape)
x_train = x_train.astype('float32')
y_train = y_train.astype('float32')
# build the model: a single LSTM
model = Sequential()
model.add(
LSTM(128, input_shape=(input_dim_1, input_dim_2), unroll=True))
optimizer = RMSprop(lr=0.01)
if use_dataset_tensors:
# Create the dataset and its associated one-shot iterator.
dataset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
dataset = dataset.repeat()
dataset = dataset.shuffle(10000)
dataset = dataset.batch(self.batch_size)
iterator = dataset.make_one_shot_iterator()
# Model creation using tensors from the get_next() graph node.
inputs, targets = iterator.get_next()
if use_dataset_tensors:
input_tensor = keras.layers.Input(tensor=inputs)
model.add(Dense(input_dim_2))
predictions = model(input_tensor)
model = keras.models.Model(input_tensor, predictions)
else:
model.add(Dense(input_dim_2, activation='softmax'))
# use multi gpu model for more than 1 gpu
if (keras.backend.backend() == 'tensorflow'
or keras.backend.backend() == 'mxnet') and gpus > 1:
model = multi_gpu_model(model, gpus=gpus, cpu_merge=False)
if use_dataset_tensors:
model.compile(loss=crossentropy_from_logits,
optimizer=optimizer,
metrics=['accuracy'],
target_tensors=[targets])
else:
model.compile(loss='categorical_crossentropy', optimizer=optimizer)
time_callback = TimeHistory()
if use_dataset_tensors:
history_callback = model.fit(epochs=self.epochs,
steps_per_epoch=15,
callbacks=[time_callback])
else:
history_callback = model.fit(x_train,
y_train,
batch_size=self.batch_size,
epochs=self.epochs,
callbacks=[time_callback])
log = LoggingMetrics(history_callback, time_callback)
log.save_metrics_to_log(logging)
if keras.backend.backend() == "tensorflow":
keras.backend.clear_session()