/
fm-binclass.dml
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/
fm-binclass.dml
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#-------------------------------------------------------------
#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
#-------------------------------------------------------------
/*
* Factorization Machines for binary classification.
*/
# Imports
source("nn/optim/adam.dml") as adam
source("nn/layers/fm.dml") as fm
source("nn/layers/log_loss.dml") as log_loss
source("nn/layers/sigmoid.dml") as sigmoid
source("nn/layers/l2_reg.dml") as l2_reg
source("nn/layers/cross_entropy_loss.dml") as cross_entropy_loss
train = function(matrix[double] X, matrix[double] y, matrix[double] X_val, matrix[double] y_val, int epochs)
return (matrix[double] w0, matrix[double] W, matrix[double] V, double loss) {
/*
* Trains the FM model.
*
* Inputs:
* - X : n examples with d features, of shape (n, d).
* - y : label corresponds to n examples
* - lambda : regularization (5e-04)
*
* Outputs:
* - w0, W, V : updated model parameters.
* - loss : computed loss with log_loss.
*
* input propagation through layers
* fm::init -> adam::init -> fm::forward -> sigmoid::forward -> log_loss::forward \
* adam::update <- fm::backward <- sigmoid::backward <- log_loss::backward <-
*/
n = nrow(X);
d = ncol(X);
k = 2; # factorization dimensionality, only(=2) possible for now.
# 1.initialize fm core
[w0, W, V] = fm::init(d, k);
# 2.initialize adam optimizer
## Default values for some parameters
lr = 0.001;
beta1 = 0.9; # [0, 1)
beta2 = 0.999; # [0, 1)
epsilon = 0.00000001;
t = 0;
# [mX, vX] = adam::init(X); # to optimize input.
[mw0, vw0] = adam::init(w0);
[mW, vW] = adam::init(W);
[mV, vV] = adam::init(V);
# Regularization
lambda = 5e-04
# Optimize
print("Starting optimization")
batch_size = 10
iters = ceil(1000 / batch_size)
N = n;
for (e in 1:epochs) {
for (i in 1:iters) {
# Get the next batch
beg = ((i-1) * batch_size) %% N + 1
end = min(N, beg + batch_size - 1)
X_batch = X[beg:end,]
y_batch = y[beg:end,]
# 3.Send inputs through fm::forward
y_res = fm::forward(X_batch, w0, W, V);
# 4.Send the above result through sigmoid::forward
sfy = sigmoid::forward(y_res);
# 5.Send the above result through log_loss::forward
loss = log_loss::forward(sfy, y_batch);
# Compute loss & accuracy for training & validation data every 100 iterations.
if (i %% 100 == 0) {
# Compute training loss & accuracy
loss_data = log_loss::forward(sfy, y_batch);
loss_reg_w0 = l2_reg::forward(w0, lambda);
loss_reg_W = l2_reg::forward(W, lambda);
loss_reg_V = l2_reg::forward(V, lambda);
accuracy = mean((sfy<0.5) == (y_batch<0.5));
loss = loss_data + loss_reg_w0 + loss_reg_W + loss_reg_V;
# Compute validation loss & accuracy
probs_val = predict(X_val, w0, W, V)
loss_val = log_loss::forward(probs_val, y_val)
accuracy_val = mean((probs_val<0.5) == (y_val<0.5))
# Output results
print("Epoch: " + e + ", Iter: " + i + ", Train Loss: " + loss + ", Train Accuracy: "
+ accuracy + ", Val Loss: " + loss_val + ", Val Accuracy: " + accuracy_val)
}
# 6.Send the result of sigmoid::forward and the correct labels y to log_loss::backward
dsfy = log_loss::backward(sfy, y_batch);
# 7.Send the above result through sigmoid::backward
dy = sigmoid::backward(dsfy, y_res);
# 8.Send the above result through fm::backward
[dw0, dW, dV] = fm::backward(dy, X_batch, w0, W, V);
# 9. update the timestep
t = e * i - 1;
# 10.Call adam::update for all parameters
# Incase we want to optimize inputs (X) also, as in deep dream.
#[X, mX, vX] = adam::update(X, dX, lr, beta1, beta2, epsilon, t, mX, vX);
[w0, mw0, vw0] = adam::update(w0, dw0, lr, beta1, beta2, epsilon, t, mw0, vw0);
[W, mW, vW] = adam::update(W, dW, lr, beta1, beta2, epsilon, t, mW, vW );
[V, mV, vV] = adam::update(V, dV, lr, beta1, beta2, epsilon, t, mV, vV );
}
}
}
predict = function(matrix[double] X, matrix[double] w0, matrix[double] W, matrix[double] V)
return (matrix[double] out) {
/*
* Computes the predictions for the given inputs.
*
* Inputs:
* - X : n examples with d features, of shape (n, d).
* - w0, W, V : trained model parameters.
*
* Outputs:
* - out : target vector, y.
*/
# 1.initialize fm core
#[w0, W, V] = fm::init(d, k);
# 2.Send inputs through fm::forward
y = fm::forward(X, w0, W, V);
# 3.Send the above result through sigmoid::forward
out = sigmoid::forward(y);
# 4.Send the above result through log_loss::forward
# loss = log_loss::forward(out);
}
eval = function(matrix[double] probs, matrix[double] y)
return (double loss, double accuracy) {
/**
* Computes loss and accuracy.
*/
# 1. compute log loss
loss = log_loss::forward(probs, y);
# 2. compute accuracy
accuracy = mean( (probs<0.5) == (y<0.5) )
}