'train', batch_size=args.batch_size,
rng=rng) # initialize our rngs using the argument set seed
val_data = data_providers.EMNISTDataProvider(
'valid', batch_size=args.batch_size,
rng=rng) # initialize our rngs using the argument set seed
test_data = data_providers.EMNISTDataProvider(
'test', batch_size=args.batch_size,
rng=rng) # initialize our rngs using the argument set seed
custom_conv_net = ConvolutionalNetwork( # initialize our network object, in this case a ConvNet
input_shape=(args.batch_size, args.image_num_channels,
args.image_height, args.image_width),
dim_reduction_type=args.dim_reduction_type,
num_output_classes=train_data.num_classes,
num_filters=args.num_filters,
num_layers=args.num_layers,
use_bias=False)
conv_experiment = ExperimentBuilder(
network_model=custom_conv_net,
experiment_name=args.experiment_name,
num_epochs=args.num_epochs,
weight_decay_coefficient=args.weight_decay_coefficient,
use_gpu=args.use_gpu,
continue_from_epoch=args.continue_from_epoch,
train_data=train_data,
val_data=val_data,
test_data=test_data) # build an experiment object
experiment_metrics, test_metrics = conv_experiment.run_experiment(
) # run experiment and return experiment metrics
custom_conv_net = ConvolutionalNetwork( # initialize our network object, in this case a ConvNet
input_shape=(args.batch_size, args.image_num_channels, args.image_height, args.image_width),
dim_reduction_type=args.dim_reduction_type,
num_output_classes=train_data.num_classes, num_filters=num_filters,kernel_size = args.kernel_size, num_layers=args.num_layers, dropout=args.dropout_rate,args=args,use_bias=False)
print("definicion convolutional network ok")
conv_experiment = ExperimentBuilder(network_model=custom_conv_net,
experiment_name=args.experiment_name,
num_epochs=args.num_epochs,
weight_decay_coefficient=args.weight_decay_coefficient,
use_gpu=args.use_gpu,
continue_from_epoch=args.continue_from_epoch,
train_data=train_data, val_data=val_data,
test_data=test_data, batch_size = args.batch_size,
training_instances = args.training_instances,
test_instances = args.test_instances,
val_instances = args.val_instances,
image_height = args.image_height,
image_width=args.image_width,
eps_smooth = args.eps_smooth,
num_classes = train_data.num_classes,
loss_function=args.loss_function,
use_cluster = args.use_cluster,
gpu_id=args.gpu_id,
q_ = args.q_parameter,
consider_manual = args.consider_manual,
args = args) # build an experiment object
experiment_metrics, test_metrics = conv_experiment.run_experiment() # run experiment and return experiment metrics