model = Sequential()
model.add(
Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# BytePS: adjust learning rate based on number of GPUs.
opt = keras.optimizers.Adadelta(1.0 * bps.size())
# BytePS: add BytePS Distributed Optimizer.
opt = bps.DistributedOptimizer(opt)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=opt,
metrics=['accuracy'])
callbacks = [
# BytePS: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
bps.callbacks.BroadcastGlobalVariablesCallback(0),
]
# BytePS: save checkpoints only on worker 0 to prevent other workers from corrupting them.
if bps.rank() == 0:
callbacks.append(
regularizer = keras.regularizers.l2(args.wd)
layer_config['config']['kernel_regularizer'] = \
{'class_name': regularizer.__class__.__name__,
'config': regularizer.get_config()}
if type(layer) == keras.layers.BatchNormalization:
layer_config['config']['momentum'] = 0.9
layer_config['config']['epsilon'] = 1e-5
model = keras.models.Model.from_config(model_config)
# BytePS: adjust learning rate based on number of GPUs.
opt = keras.optimizers.SGD(lr=args.base_lr * bps.size(),
momentum=args.momentum)
# BytePS: add BytePS Distributed Optimizer.
opt = bps.DistributedOptimizer(opt, compression=compression)
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=opt,
metrics=['accuracy', 'top_k_categorical_accuracy'])
callbacks = [
# BytePS: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
bps.callbacks.BroadcastGlobalVariablesCallback(0),
# BytePS: average metrics among workers at the end of every epoch.
#
# Note: This callback must be in the list before the ReduceLROnPlateau,
# TensorBoard, or other metrics-based callbacks.