PODNN is a method lies in the intersection of deep learning and ensmeble methods. It makes efficient use of deep neural networks in an ensmble setting. It consists of a number of parallel deep neural networks that are made parallel together. Each parallel sub-layer is followed by an orthogonalization sub-layer. These parallel part of the architecture is then followed by aggregation layer and meta part. Architecutre of PODNN is shown in the following figure.
For further detailes about PODNN, please refer to the [paper](https://www.sciencedirect.com/science/article/pii/S0893608021000824).
The main promise of PODNN stems from the enforced diversity mechanism caused by orthogonalization sub-layers. The embeded diversity mechanism make effective use of a number of deep models since each deep model will provide a considerable deal of contributation to the final prections.
The implementation of PODNN is available in both Tensorflow and Pytorch.
import podnn_torch
import torch
from torch import nn
from sklearn.preprocessing import StandardScaler
from sklearn import datasets
x = torch.from_numpy(datasets.load_breast_cancer().data).float()
y = torch.from_numpy(datasets.load_breast_cancer().target.reshape(-1,1)).float()
unit_model_1 = torch.nn.Sequential(
nn.Linear(in_features=30, out_features=12),
nn.ReLU(),
nn.Linear(in_features=12,out_features=6),
nn.BatchNorm1d(num_features=6)
)
unit_model_2 = torch.nn.Sequential(
nn.Linear(in_features=6, out_features=4),
)
model = torch.nn.Sequential(
podnn_torch.InputLayer(n_models=6),
podnn_torch.ParallelLayer(unit_model_1),
podnn_torch.OrthogonalLayer1D(),
podnn_torch.ParallelLayer(unit_model_2),
podnn_torch.AggregationLayer(stride=2,input_dim=4),
nn.Linear(in_features=podnn_torch.agg_out_dim,out_features=1),
torch.nn.Sigmoid()
)
criterion = torch.nn.BCELoss(reduction='mean')
optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
epochs = 500
for t in range(epochs):
y_pred = model(x)
loss = criterion(y_pred, y)
print(t, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()import podnn_tensorflow
import tensorflow as tf
from tensorflow.keras import Model
from sklearn import datasets
tf.random.set_seed(3)
x = tf.convert_to_tensor(datasets.load_breast_cancer().data)
y = tf.convert_to_tensor(datasets.load_breast_cancer().target.reshape(-1,1))
unit_model_1 = [
tf.keras.layers.Dense(12),
tf.keras.layers.ReLU(),
tf.keras.layers.Dense(6),
tf.keras.layers.BatchNormalization()
]
unit_model_2 = [
tf.keras.layers.Dense(4)
]
class podnnModel(Model):
def __init__(self):
super(podnnModel, self).__init__()
pass
def build(self,input_shape):
self.InputLayer = podnn_tensorflow.InputLayer(n_models=4)
self.ParallelLayer1 = podnn_tensorflow.ParallelLayer(unit_model_1)
self.OrthogonalLayer = podnn_tensorflow.OrthogonalLayer1D()
self.ParallelLayer2 = podnn_tensorflow.ParallelLayer(unit_model_2)
self.AggregationLayer = podnn_tensorflow.AggregationLayer(stride=2, input_dim=4)
self.DenseLayer = tf.keras.layers.Dense(1, activation='sigmoid')
def call(self,x):
x = self.InputLayer(x)
x = self.ParallelLayer1(x)
x = self.OrthogonalLayer(x)
x = self.ParallelLayer2(x)
x = self.AggregationLayer(x)
x = self.DenseLayer(x)
return x
loss_object = tf.keras.losses.BinaryCrossentropy()
optimizer = tf.keras.optimizers.Adam(learning_rate=0.01)
train_loss = tf.keras.metrics.Mean()
train_accuracy = tf.keras.metrics.BinaryAccuracy(name='train_accuracy')
model = podnnModel()
@tf.function
def train_step(x, labels):
with tf.GradientTape() as tape:
predictions = model(x)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
train_accuracy(labels, tf.squeeze(predictions))
epochs = 500
for i in range(epochs):
train_loss.reset_states()
train_accuracy.reset_states()
train_step(x, y)
print('train loss='+str(train_loss.result()))
print('train accuracy=' + str(train_accuracy.result()))Convolutional PODNN extends the idea of orthogonalization to the output of convulutional filters in an ensemble of Convolutional deep neural network. Architecutre of PODNN is shown in the following figure.
import podnn_torch
import torch
from torch import nn
from torchvision import datasets,transforms
batch_size=64
train_kwargs = {'batch_size': batch_size}
test_kwargs = {'batch_size': batch_size}
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.0001,), (1,))
])
dataset1 = datasets.MNIST('../data', train=True, download=True,
transform=transform)
dataset2 = datasets.MNIST('../data', train=False,
transform=transform)
train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs)
test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)
unit_model_1 = torch.nn.Sequential(
nn.Conv2d(1, 32, 3, 1),
nn.ReLU(),
nn.BatchNorm2d(32),
)
unit_model_2 = torch.nn.Sequential(
nn.Conv2d(32,16,3,1),
nn.ReLU(),
nn.MaxPool2d(2),
nn.Flatten(1),
nn.Linear(in_features=2304,out_features=128),
nn.ReLU()
)
model = torch.nn.Sequential(
podnn_torch.InputLayer(n_models=4),
podnn_torch.ParallelLayer(unit_model_1),
podnn_torch.OrthogonalLayer2D(),
podnn_torch.ParallelLayer(unit_model_2),
podnn_torch.AggregationLayer(stride=2,input_dim=128,output_dim=10),
nn.Linear(in_features=20,out_features=10),
torch.nn.Softmax()
)
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
epochs = 10
for t in range(epochs):
for x, y in train_loader:
y_pred = model(x)
loss = criterion(y_pred, y)
print(t, loss.item())
optimizer.zero_grad()
loss.backward()
optimizer.step()import podnn_tensorflow
import tensorflow as tf
import numpy as np
from tensorflow.keras import layers,Model
tf.random.set_seed(3)
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0
x_train = tf.convert_to_tensor(np.expand_dims(x_train,axis=3))
x_test = tf.convert_to_tensor(np.expand_dims(x_test,axis=3))
batch_size=64
im_height = 32
im_width = 32
train_ds = tf.data.Dataset.from_tensor_slices(
(x_train, y_train)).shuffle(1000).batch(batch_size)
unit_model_1 = [
tf.keras.layers.Conv2D(32, 3, input_shape=[im_height,im_width,1]),
tf.keras.layers.BatchNormalization()
]
unit_model_2 = [
tf.keras.layers.Conv2D(16, 3, input_shape=[26,26,32]),
tf.keras.layers.MaxPooling2D(2),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128,activation='relu')
]
class podnnModel(Model):
def __init__(self):
super(podnnModel, self).__init__()
pass
def build(self,input_shape):
self.InputLayer = podnn_tensorflow.InputLayer(n_models=4)
self.ParallelLayer = podnn_tensorflow.ParallelLayer(unit_model_1)
self.OrthogonalLayer = podnn_tensorflow.OrthogonalLayer2D()
self.ParallelLayer2 = podnn_tensorflow.ParallelLayer(unit_model_2)
self.AggregationLayer = podnn_tensorflow.AggregationLayer(stride=2, output_dim=10)
self.DenseLayer = layers.Dense(10, activation='softmax')
def call(self,x):
x = self.InputLayer(x)
x = self.ParallelLayer(x)
x = self.OrthogonalLayer(x)
x = self.ParallelLayer2(x)
x = self.AggregationLayer(x)
x = self.DenseLayer(x)
return x
model = podnnModel()
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
optimizer = tf.keras.optimizers.Adam()
train_loss = tf.keras.metrics.Mean()
@tf.function
def train_step(x, labels):
with tf.GradientTape() as tape:
predictions = model(x)
loss = loss_object(labels, predictions)
gradients = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(gradients, model.trainable_variables))
train_loss(loss)
epochs = 10
for epoch in range(epochs):
train_loss.reset_states()
for images, labels in train_ds:
train_step(images, labels)
print('train loss=' + str(train_loss.result()))
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