示例#1
0
def train_net(config):

    # UNPACK CONFIGS
    (flag_para_load, train_filenames, val_filenames, train_labels, val_labels,
     img_mean) = unpack_configs(config)

    # pycuda set up
    drv.init()
    dev = drv.Device(int(config['gpu'][-1]))
    ctx = dev.make_context()

    if flag_para_load:
        #  zmq set up
        sock = zmq.Context().socket(zmq.PAIR)
        sock.connect('tcp://*****:*****@ iter = ', num_iter
                print 'training cost:', cost_ij
                if config['print_train_error']:
                    print 'training error rate:', train_error()

            if flag_para_load and (count < len(minibatch_range)):
                load_send_queue.put('calc_finished')

        ############### Test on Validation Set ##################

        DropoutLayer.SetDropoutOff()

        this_validation_error, this_validation_loss = get_val_error_loss(
            rand_arr,
            shared_x,
            shared_y,
            val_filenames,
            val_labels,
            flag_para_load,
            img_mean,
            batch_size,
            validate_model,
            send_queue=load_send_queue,
            recv_queue=load_recv_queue)

        print('epoch %i: validation loss %f ' % (epoch, this_validation_loss))
        print('epoch %i: validation error %f %%' %
              (epoch, this_validation_error * 100.))
        val_record.append([this_validation_error, this_validation_loss])
        np.save(config['weights_dir'] + 'val_record.npy', val_record)

        DropoutLayer.SetDropoutOn()
        ############################################

        # Adapt Learning Rate
        step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
                                        learning_rate)

        # Save weights
        if epoch % config['snapshot_freq'] == 0:
            save_weights(layers, config['weights_dir'], epoch)
            np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
                    learning_rate.get_value())
            save_momentums(vels, config['weights_dir'], epoch)

    print('Optimization complete.')
示例#2
0
def train_net(config):

    # UNPACK CONFIGS
    (flag_para_load, train_filenames, val_filenames,
     train_labels, val_labels, img_mean) = unpack_configs(config)

    # pycuda set up
    drv.init()
    dev = drv.Device(int(config['gpu'][-1]))
    ctx = dev.make_context()
    
    if flag_para_load:
        #  zmq set up
        sock = zmq.Context().socket(zmq.PAIR)
        sock.connect('tcp://*****:*****@ iter = ', num_iter
                print 'training cost:', cost_ij
                if config['print_train_error']:
                    print 'training error rate:', train_error()

            if flag_para_load and (count < len(minibatch_range)):
                load_send_queue.put('calc_finished')

        ############### Test on Validation Set ##################

        DropoutLayer.SetDropoutOff()

        this_validation_error, this_validation_loss = get_val_error_loss(
            rand_arr, shared_x, shared_y,
            val_filenames, val_labels,
            flag_para_load, img_mean,
            batch_size, validate_model,
            send_queue=load_send_queue, recv_queue=load_recv_queue)


        print('epoch %i: validation loss %f ' %
              (epoch, this_validation_loss))
        print('epoch %i: validation error %f %%' %
              (epoch, this_validation_error * 100.))
        val_record.append([this_validation_error, this_validation_loss])
        np.save(config['weights_dir'] + 'val_record.npy', val_record)

        DropoutLayer.SetDropoutOn()
        ############################################

        # Adapt Learning Rate
        step_idx = adjust_learning_rate(config, epoch, step_idx,
                                        val_record, learning_rate)

        # Save weights
        if epoch % config['snapshot_freq'] == 0:
            save_weights(layers, config['weights_dir'], epoch)
            np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
                       learning_rate.get_value())
            save_momentums(vels, config['weights_dir'], epoch)

    print('Optimization complete.')
示例#3
0
文件: train.py 项目: strin/dropnet
epoch = 0
step_idx = 0
val_record = []

while epoch < config['n_epochs']:
    epoch = epoch + 1

    if config['resume_train'] and epoch == 1:
        load_epoch = config['load_epoch']
        load_weights(layers, config['weights_dir'], load_epoch)
        lr_to_load = np.load(
            config['weights_dir'] + 'lr_' + str(load_epoch) + '.npy')
        val_record = list(
            np.load(config['weights_dir'] + 'val_record.npy'))
        learning_rate.set_value(lr_to_load)
        load_momentums(vels, config['weights_dir'], load_epoch)
        epoch = load_epoch + 1

    n_batches = len(train_set) / batch_size
    for it in range(n_batches):
        num_iter = (epoch - 1) * len(train_set) + it
        print 'epoch', epoch, 'num_iter', num_iter, '/', n_batches

        with Timer('sample minibatch'):
            (batch_x, batch_y) = sample_minibatch(train_set, batch_size)
            shared_x.set_value(batch_x)
            shared_y.set_value(batch_y)

        with Timer('forward-backward pass'):
            cost = train_model()
示例#4
0
文件: train.py 项目: intel/theano
def train_net(config):
    # UNPACK CONFIGS
    (flag_para_load, train_filenames, val_filenames,
     train_labels, val_labels, img_mean) = unpack_configs(config)
    if flag_para_load:
        #  zmq set up
        sock = zmq.Context().socket(zmq.PAIR)
        sock.connect('tcp://*****:*****@ iter = %i" % (num_iter))
                logger.info("training cost: %lf" % (cost_ij))
                if config['print_train_error']:
                    logger.info('training error rate: %lf' % train_error())

            if flag_para_load and (count < len(minibatch_range)):
                load_send_queue.put('calc_finished')

        ############### Test on Validation Set ##################

        #"""
        DropoutLayer.SetDropoutOff()

        result_list = get_val_error_loss(
            rand_arr, shared_x, shared_y,
            val_filenames, val_labels,
            flag_para_load, img_mean,
            batch_size, validate_model,
            send_queue=load_send_queue,
            recv_queue=load_recv_queue,
            flag_top_5=flag_top5)


        logger.info(('epoch %i: validation loss %f ' %
              (epoch, result_list[-1])))

        if flag_top5:
            logger.info(('epoch %i: validation error (top 1) %f %%, (top5) %f %%' %
                (epoch,  result_list[0] * 100., result_list[1] * 100.)))
        else:
            logger.info(('epoch %i: validation error %f %%' %
                (epoch, result_list[0] * 100.)))

        val_record.append(result_list)
        np.save(config['weights_dir'] + 'val_record.npy', val_record)

        DropoutLayer.SetDropoutOn()
        ############################################

        # Adapt Learning Rate
        step_idx = adjust_learning_rate(config, epoch, step_idx,
                                        val_record, learning_rate)

        # Save weights
        if epoch % config['snapshot_freq'] == 0:
            save_weights(layers, config['weights_dir'], epoch)
            np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
                       learning_rate.get_value())
            save_momentums(vels, config['weights_dir'], epoch)
        #"""

    print('Optimization complete.')
示例#5
0
def train_net(config, private_config):

    # UNPACK CONFIGS
    (flag_para_load, flag_datalayer, train_filenames, val_filenames,
     train_labels, val_labels, img_mean) = \
        unpack_configs(config, ext_data=private_config['ext_data'],
                       ext_label=private_config['ext_label'])

    gpu_send_queue = private_config['queue_gpu_send']
    gpu_recv_queue = private_config['queue_gpu_recv']

    # pycuda and zmq set up
    drv.init()
    dev = drv.Device(int(private_config['gpu'][-1]))
    ctx = dev.make_context()

    sock_gpu = zmq.Context().socket(zmq.PAIR)
    if private_config['flag_client']:
        sock_gpu.connect('tcp://*****:*****@ iter = ', num_iter
                    print 'training cost:', cost_ij

                if config['print_train_error']:
                    error_ij = train_error()

                    gpu_send_queue.put(error_ij)
                    that_error = gpu_recv_queue.get()
                    error_ij = (error_ij + that_error) / 2.

                    if private_config['flag_verbose']:
                        print 'training error rate:', error_ij

            if flag_para_load and (count < len(minibatch_range)):
                load_send_queue.put('calc_finished')

        ############### Test on Validation Set ##################

        DropoutLayer.SetDropoutOff()

        this_val_error, this_val_loss = get_val_error_loss(
            rand_arr,
            shared_x,
            shared_y,
            val_filenames,
            val_labels,
            flag_datalayer,
            flag_para_load,
            batch_size,
            validate_model,
            send_queue=load_send_queue,
            recv_queue=load_recv_queue)

        # report validation stats
        gpu_send_queue.put(this_val_error)
        that_val_error = gpu_recv_queue.get()
        this_val_error = (this_val_error + that_val_error) / 2.

        gpu_send_queue.put(this_val_loss)
        that_val_loss = gpu_recv_queue.get()
        this_val_loss = (this_val_loss + that_val_loss) / 2.

        if private_config['flag_verbose']:
            print('epoch %i: validation loss %f ' % (epoch, this_val_loss))
            print('epoch %i: validation error %f %%' %
                  (epoch, this_val_error * 100.))
        val_record.append([this_val_error, this_val_loss])

        if private_config['flag_save']:
            np.save(config['weights_dir'] + 'val_record.npy', val_record)

        DropoutLayer.SetDropoutOn()
        ############################################

        # Adapt Learning Rate
        step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
                                        learning_rate)

        # Save Weights, only one of them will do
        if private_config['flag_save']:
            if epoch % config['snapshot_freq'] == 0:
                save_weights(layers, config['weights_dir'], epoch)
                np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
                        learning_rate.get_value())
                save_momentums(vels, config['weights_dir'], epoch)

    print('Optimization complete.')
示例#6
0
def train_net(config):
    # UNPACK CONFIGS
    (flag_para_load, train_filenames, val_filenames,
     train_labels, val_labels, img_mean) = unpack_configs(config)
    if flag_para_load:
        #  zmq set up
        sock = zmq.Context().socket(zmq.PAIR)
        sock.connect('tcp://*****:*****@iter " + str(count)
            if count == 1:
                s = time.time()
            if count == 20:
                e = time.time()
                print "time per 20 iter:", (e - s)
                logger.info("time per 20 iter: %f" % (e - s)) 
            cost_ij = train_model_wrap(train_model, shared_x,
                                       shared_y, rand_arr, img_mean,
                                       count, minibatch_index,
                                       minibatch_range, batch_size,
                                       train_filenames, train_labels,
                                       flag_para_load,
                                       config['batch_crop_mirror'],
                                       send_queue=load_send_queue,
                                       recv_queue=load_recv_queue)

            if num_iter % config['print_freq'] == 0:
                #print 'training @ iter = ', num_iter
                #print 'training cost:', cost_ij
		logger.info("training @ iter = %i" % (num_iter)) 
		logger.info("training cost: %lf" % (cost_ij)) 
                if config['print_train_error']:
                    logger.info('training error rate: %lf' % train_error())
                    #print 'training error rate:', train_error()

            if flag_para_load and (count < len(minibatch_range)):
                load_send_queue.put('calc_finished')

        ############### Test on Validation Set ##################

        #"""
        DropoutLayer.SetDropoutOff()

        # result_list = [ this_validation_error, this_validation_error_top5, this_validation_loss ]
        # or
        # result_list = [ this_validation_error, this_validation_loss ]
        result_list = get_val_error_loss(
        #this_validation_error, this_validation_loss = get_val_error_loss(
            rand_arr, shared_x, shared_y,
            val_filenames, val_labels,
            flag_para_load, img_mean,
            batch_size, validate_model,
            send_queue=load_send_queue, 
            recv_queue=load_recv_queue,
            flag_top_5=flag_top5)


        logger.info(('epoch %i: validation loss %f ' %
              (epoch, result_list[-1])))
        #print('epoch %i: validation loss %f ' %
        #      (epoch, this_validation_loss))
        if flag_top5:
            logger.info(('epoch %i: validation error (top 1) %f %%, (top5) %f %%' %
                (epoch,  result_list[0] * 100., result_list[1] * 100.)))
        else:
            logger.info(('epoch %i: validation error %f %%' %
                (epoch, result_list[0] * 100.)))
        #print('epoch %i: validation error %f %%' %
        #      (epoch, this_validation_error * 100.))
        val_record.append(result_list)
        #val_record.append([this_validation_error, this_validation_loss])
        np.save(config['weights_dir'] + 'val_record.npy', val_record)

        DropoutLayer.SetDropoutOn()
        ############################################

        # Adapt Learning Rate
        step_idx = adjust_learning_rate(config, epoch, step_idx,
                                        val_record, learning_rate)

        # Save weights
        if epoch % config['snapshot_freq'] == 0:
            save_weights(layers, config['weights_dir'], epoch)
            np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
                       learning_rate.get_value())
            save_momentums(vels, config['weights_dir'], epoch)
        #"""

    print('Optimization complete.')
示例#7
0
def train_net(config, private_config):

    # UNPACK CONFIGS
    (flag_para_load, train_filenames, val_filenames,
     train_labels, val_labels, img_mean) = \
        unpack_configs(config, ext_data=private_config['ext_data'],
                       ext_label=private_config['ext_label'])


    gpu_send_queue = private_config['queue_gpu_send']
    gpu_recv_queue = private_config['queue_gpu_recv']

    # pycuda and zmq set up
    drv.init()
    dev = drv.Device(int(private_config['gpu'][-1]))
    ctx = dev.make_context()

    sock_gpu = zmq.Context().socket(zmq.PAIR)
    if private_config['flag_client']:
        sock_gpu.connect('tcp://*****:*****@ iter = ', num_iter
                    log_iter.write("%d\n" % num_iter)
                    log_iter.flush()
                    print 'training cost:', cost_ij
                    log_err_cost.write("%f\n" % cost_ij)
                    log_err_cost.flush()

                if config['print_train_error']:
                    error_ij = train_error()

                    gpu_send_queue.put(error_ij)
                    that_error = gpu_recv_queue.get()
                    error_ij = (error_ij + that_error) / 2.

                    if private_config['flag_verbose']:
                        print 'training error rate:', error_ij
                        log_err_rate.write("%f\n" % error_ij)
                        log_err_rate.flush()


            if flag_para_load and (count < len(minibatch_range)):
                load_send_queue.put('calc_finished')

            if count%20 == 0:
                e = time.time()
                print "time per 20 iter:", (e - s)
                
        ############### Test on Validation Set ##################

        DropoutLayer.SetDropoutOff()

        this_val_error, this_val_loss = get_val_error_loss(
            rand_arr, shared_x, shared_y,
            val_filenames, val_labels,
            flag_para_load, img_mean,
            batch_size, validate_model,
            send_queue=load_send_queue, recv_queue=load_recv_queue)

        # report validation stats
        gpu_send_queue.put(this_val_error)
        that_val_error = gpu_recv_queue.get()
        this_val_error = (this_val_error + that_val_error) / 2.

        gpu_send_queue.put(this_val_loss)
        that_val_loss = gpu_recv_queue.get()
        this_val_loss = (this_val_loss + that_val_loss) / 2.

        if private_config['flag_verbose']:
            print('epoch %i: validation loss %f ' %
                  (epoch, this_val_loss))
            print('epoch %i: validation error %f %%' %
                  (epoch, this_val_error * 100.))
        val_record.append([this_val_error, this_val_loss])

        if private_config['flag_save']:
            np.save(config['weights_dir'] + 'val_record.npy', val_record)
            np.savetxt(config['weights_dir'] + 'val_record_txt.txt', val_record)

        DropoutLayer.SetDropoutOn()
        ############################################

        # Adapt Learning Rate
        step_idx = adjust_learning_rate(config, epoch, step_idx,
                                        val_record, learning_rate)

        # Save Weights, only one of them will do
        if private_config['flag_save']:
            if epoch % config['snapshot_freq'] == 0:
                save_weights(layers, config['weights_dir'], epoch)
                np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
                        learning_rate.get_value())
                save_momentums(vels, config['weights_dir'], epoch)

    print('Optimization complete.')
示例#8
0
文件: train.py 项目: strin/dropnet
# Start Training Loop
epoch = 0
step_idx = 0
val_record = []

while epoch < config['n_epochs']:
    epoch = epoch + 1

    if config['resume_train'] and epoch == 1:
        load_epoch = config['load_epoch']
        load_weights(layers, config['weights_dir'], load_epoch)
        lr_to_load = np.load(config['weights_dir'] + 'lr_' + str(load_epoch) +
                             '.npy')
        val_record = list(np.load(config['weights_dir'] + 'val_record.npy'))
        learning_rate.set_value(lr_to_load)
        load_momentums(vels, config['weights_dir'], load_epoch)
        epoch = load_epoch + 1

    n_batches = len(train_set) / batch_size
    for it in range(n_batches):
        num_iter = (epoch - 1) * len(train_set) + it
        print 'epoch', epoch, 'num_iter', num_iter, '/', n_batches

        with Timer('sample minibatch'):
            (batch_x, batch_y) = sample_minibatch(train_set, batch_size)
            shared_x.set_value(batch_x)
            shared_y.set_value(batch_y)

        with Timer('forward-backward pass'):
            cost = train_model()