Mazecreator / ufcnn

Implementation of Undecimated Fully Convolutional Neural Network for time series modeling

ufcnn

Implementation of Undecimated Fully Convolutional Neural Network for time series modeling. See the paper.

This algorithm learns a sequence-to-sequnce predictor in the form of a multi-layer neural network composed exclusively of convolutional layers.

In order to learn information on wide range of time scales, the convolution filters are gradually dilated on each new level, i.e. zeros are implicitly inserted between their values. This approach is different from a more conventional approach of downsampling-upsampling layer pairs. It has an advantage of providing the translation equivariance --- the desired property in time series modeling.

You are advised to read this excellent note for details about the dilated convolution.

Installation

Clone the repository and append the path to PYTHONPATH.

Dependencies

During the development the following setup was used:

• numpy 1.11.0
• tensorflow built from the latest source
• Should work in Python 2 and 3

Example

The examples shows how to create and train the model.

import tensorflow as tf
from ufcnn import construct_ufcnn, mse_loss
from ufcnn.datasets import generate_tracking

# Generate data.
X_train, Y_train = generate_tracking(200, 500)
X_test, Y_test = generate_tracking(20, 500)

# Create the network.
x, y_hat, *_ = construct_ufcnn(n_outputs=2, n_levels=2)

# Create a placeholder for truth sequences.
y = tf.placeholder(tf.float32, shape=(None, None, 2))

# Define the MSE loss and RMSProp optimizer over it.
loss = mse_loss(y_hat, y)
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001)
train_step = optimizer.minimize(loss)

# Run several epochs of optimization.
session = tf.Session()
session.run(tf.initialize_all_variables())

print("{:^7}{:^7}".format("Epoch", "Loss"))

batch_size = 20
n_batch = X_train.shape[0] // batch_size
n_epochs = 20

for epoch in range(n_epochs):
    if epoch % 5 == 0:
        mse = session.run(loss, feed_dict={x: X_test, y: Y_test})
        print("{:^7}{:^7.2f}".format(epoch, mse))
    for b in range(n_batch):
        X_batch = X_train[b * batch_size : (b + 1) * batch_size]
        Y_batch = Y_train[b * batch_size : (b + 1) * batch_size]
        session.run(train_step, feed_dict={x: X_batch, y: Y_batch})

mse = session.run(loss, feed_dict={x: X_test, y: Y_test})
print("{:^7}{:^7.2f}".format(n_epochs , mse))

Output:

 Epoch  Loss  
   0    51.32 
   5    23.35 
  10    20.45 
  15    14.08 
  20    8.97  

We see that the optimizer progresses reasonably fast and we can expect some predicting power if we train the network long enough.