How to use the anna.util.Preprocessor function in anna

To help you get started, we’ve selected a few anna examples, based on popular ways it is used in public projects.

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ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / tfd / cnn_ad / train.py View on Github

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
                                     flip=True, gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor_train.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor_val.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / ck_plus_six_class / cnn_ad / train.py View on Github

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
                                     flip=True, gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor_train.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor_val.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / ck_plus / cnn_ad / train.py View on Github

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)


# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
                                     flip=True, gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor_train.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor_val.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / tfd / cnn_a / train.py View on Github

X_test *= 2.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
                                     flip=True, gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor_train.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor_val.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / tfd / cnn_d / train.py View on Github

X_test = numpy.float32(X_test)
X_test /= 255.0
X_test *= 2.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer3(filter_size=5, num_channels=1)
module_list = [normer]
preprocessor = util.Preprocessor(module_list)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / tfd / cnn_ad / train.py View on Github

X_test *= 2.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
                                     flip=True, gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor_train.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor_val.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / ck_plus / cnn_a / train.py View on Github

X_test *= 2.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Do data augmentation (crops, flips, rotations, scales, intensity)
data_augmenter = util.DataAugmenter2(crop_shape=(96, 96),
                                     flip=True, gray_on=True)
normer = util.Normer3(filter_size=5, num_channels=1)
module_list_train = [data_augmenter, normer]
module_list_val = [normer]
preprocessor_train = util.Preprocessor(module_list_train)
preprocessor_val = util.Preprocessor(module_list_val)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor_train.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor_val.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / ck_plus / cnn / train.py View on Github

X_test /= 255.0
X_test *= 2.0
y_test = test_data_container.y

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
test_dataset = supervised_dataset.SupervisedDataset(X_test, y_test)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
test_iterator = test_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer3(filter_size=5, num_channels=1)
module_list = [normer]
preprocessor = util.Preprocessor(module_list)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_test_batch, y_test_batch = test_iterator.next()
        x_test_batch = preprocessor.run(x_test_batch)
        test_accuracy = model.eval(x_test_batch, y_test_batch)
        monitor.stop_test(1-test_accuracy)

ifp-uiuc / do-neural-networks-learn-faus-iccvw-2015 / tfd / cnn / train.py View on Github

X_test = numpy.float32(X_test)
X_test /= 255.0
X_test *= 2.0

train_dataset = supervised_dataset.SupervisedDataset(X_train, y_train)
val_dataset = supervised_dataset.SupervisedDataset(X_val, y_val)
train_iterator = train_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)
val_iterator = val_dataset.iterator(
    mode='random_uniform', batch_size=64, num_batches=31000)

# Create object to local contrast normalize a batch.
# Note: Every batch must be normalized before use.
normer = util.Normer3(filter_size=5, num_channels=1)
module_list = [normer]
preprocessor = util.Preprocessor(module_list)

print('Training Model')
for x_batch, y_batch in train_iterator:
    x_batch = preprocessor.run(x_batch)
    monitor.start()
    log_prob, accuracy = model.train(x_batch, y_batch)
    monitor.stop(1-accuracy)

    if monitor.test:
        monitor.start()
        x_val_batch, y_val_batch = val_iterator.next()
        x_val_batch = preprocessor.run(x_val_batch)
        val_accuracy = model.eval(x_val_batch, y_val_batch)
        monitor.stop_test(1-val_accuracy)

How to use the anna.util.Preprocessor function in anna

To help you get started, we’ve selected a few anna examples, based on popular ways it is used in public projects.

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