LowRankTRN
Code for the paper "Tensor Regression Networks with various Low-Rank Tensor Approximations"
Low Rank Tensor Regression Layer
Example
Inserting TRL to replace fully connected layer is quite simple. Given a convlutional tensor h_conv2, one can add one line code out = ttrl(tf.nn.relu(h_pool2), [1,1,1,10,1], 10). For example, a part of the mnist.py code is given in the following.
# relu layer. input of h_pool to W_conv2 then add bias
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)
# second pooling layer then the size will be batchsize*7*7*32
h_pool2 = max_pool_2x2(h_conv2)
# Low rank Tensor Regression Layer
# ttrl : Tensor Train Regression Layer
# trl : Tucker Regression Layer
# cprl : CP Regression Layer
out = ttrl(tf.nn.relu(h_pool2), [1,1,1,10,1], 10)To change the type of tensor regression layer, change ttrl to trl or cprl (set appropriate ranks as well).
Tensor train regression layer
out = ttrl(tf.nn.relu(h_pool2), [1,1,1,10,1], 10)Tucker regression layer
out = trl(tf.nn.relu(h_pool2), [1,1,1,10], 10)CP regression layer
out = cprl(tf.nn.relu(h_pool2), 5, 10)TRL.py
- Tensor Train Regression Layer
ttrl(x, ranks, n_outputs)
# INPUTS
x : the input tensor
ranks : List. TT rank of the weight tensor W.
n_outputs : Scalar. the size of the row vector of the output matrix.
# OUTPUT
A tensor of size batchsize times the n_outputs- Tucker Regression Layer
trl(x, ranks, n_outputs)
# INPUTS
x : the input tensor
ranks : List. The Tucker rank of the weight tensor W.
n_outputs : Scalar. the size of the row vector of the output matrix.
# OUTPUT
A tensor of size batchsize times the n_outputs- CP Regression Layer
cprl(x, rank, n_outputs)
# INPUTS
x : the input tensor
ranks : Scalar. CP rank of the weight tensor W.
n_outputs : Scalar. the size of the row vector of the output matrix.
# OUTPUT
A tensor of size batchsize times the n_outputs