hex.deeplearning.Neurons Maven / Gradle / Ivy
package hex.deeplearning;
import hex.DataInfo;
import hex.Distribution;
import water.*;
import water.util.ArrayUtils;
import water.util.Log;
import water.util.MathUtils;
import java.nio.ByteBuffer;
import java.util.*;
/**
* This class implements the concept of a Neuron layer in a Neural Network
* During training, every MRTask F/J thread is expected to create these neurons for every map call (Cheap to make).
* These Neurons are NOT sent over the wire.
* The weights connecting the neurons are in a separate class (DeepLearningModel.DeepLearningModelInfo), and will be shared per node.
*/
public abstract class Neurons {
short _k; //number of parallel channels
int[] _maxIncoming; //index of largest incoming signal (out of k channels)
Distribution _dist;
protected int units;
/**
* Constructor of a Neuron Layer
* @param units How many neurons are in this layer?
*/
Neurons(int units) {
this.units = units;
}
/**
* Print the status of this neuron layer
* @return populated String
*/
@Override
public String toString() {
String s = this.getClass().getSimpleName();
s += "\nNumber of Neurons: " + units;
s += "\nParameters:\n" + params.toString();
if (_dropout != null) s += "\nDropout:\n" + _dropout.toString();
return s;
}
/**
* Parameters (deep-cloned() from the user input, can be modified here, e.g. learning rate decay)
*/
protected transient DeepLearningParameters params;
protected transient int _index; //which hidden layer it is
/**
* Layer state (one per neuron): activity, error
*/
public transient Storage.DenseVector _origa;
public transient Storage.DenseVector _a;
public transient Storage.DenseVector _e;
/**
* References for feed-forward connectivity
*/
public Neurons _previous;
public Neurons _input;
DeepLearningModelInfo _minfo; //reference to shared model info
public Storage.DenseRowMatrix _w;
public Storage.DenseRowMatrix _wEA; //weights for elastic averaging
public Storage.DenseVector _b;
public Storage.DenseVector _bEA; //bias for elastic averaging
/**
* References for momentum training
*/
Storage.DenseRowMatrix _wm;
Storage.DenseVector _bm;
/**
* References for ADADELTA
*/
Storage.DenseRowMatrix _ada_dx_g;
Storage.DenseVector _bias_ada_dx_g;
/**
* For Dropout training
*/
protected Dropout _dropout;
/**
* Helper to shortcut bprop
*/
private boolean _shortcut = false;
public Storage.DenseVector _avg_a;
/**
* Helper to check sanity of Neuron layers
* @param training whether training or testing is done
*/
void sanityCheck(boolean training) {
if (this instanceof Input) {
assert(_previous == null);
} else {
assert(_previous != null);
if (_minfo.has_momenta()) {
assert(_wm != null);
assert(_bm != null);
assert(_ada_dx_g == null);
}
if (_minfo.adaDelta()) {
if (params._rho == 0) throw new IllegalArgumentException("rho must be > 0 if epsilon is >0.");
if (params._epsilon == 0) throw new IllegalArgumentException("epsilon must be > 0 if rho is >0.");
assert(_minfo.adaDelta());
assert(_bias_ada_dx_g != null);
assert(_wm == null);
assert(_bm == null);
}
if (this instanceof MaxoutDropout || this instanceof TanhDropout || this instanceof RectifierDropout) {
assert (!training || _dropout != null);
}
}
}
/**
* Initialization of the parameters and connectivity of a Neuron layer
* @param neurons Array of all neuron layers, to establish feed-forward connectivity
* @param index Which layer am I?
* @param p User-given parameters (Job parental object hierarchy is not used)
* @param minfo Model information (weights/biases and their momenta)
* @param training Whether training is done or just testing (no need for dropout)
*/
public final void init(Neurons[] neurons, int index, DeepLearningParameters p, final DeepLearningModelInfo minfo, boolean training) {
_index = index-1;
params = (DeepLearningParameters)p.clone();
params._hidden_dropout_ratios = minfo.get_params()._hidden_dropout_ratios;
params._rate *= Math.pow(params._rate_decay, index-1);
params._distribution = minfo.get_params()._distribution;
_dist = new Distribution(params._distribution, params._tweedie_power);
_a = new Storage.DenseVector(units);
if (!(this instanceof Input)) {
_e = new Storage.DenseVector(units);
} else if (params._autoencoder && params._input_dropout_ratio > 0) {
_origa = new Storage.DenseVector(units);
}
if (training && (this instanceof MaxoutDropout || this instanceof TanhDropout
|| this instanceof RectifierDropout || this instanceof Input) ) {
_dropout = this instanceof Input ?
(params._input_dropout_ratio==0 ? null : new Dropout(units, params._input_dropout_ratio)) //input dropout
: new Dropout(units, params._hidden_dropout_ratios[_index]); //hidden dropout
}
if (!(this instanceof Input)) {
_previous = neurons[_index]; //incoming neurons
_minfo = minfo;
_w = minfo.get_weights(_index); //incoming weights
_b = minfo.get_biases(_index); //bias for this layer (starting at hidden layer)
if(params._autoencoder && params._sparsity_beta > 0 && _index < params._hidden.length) {
_avg_a = minfo.get_avg_activations(_index);
}
if (minfo.has_momenta()) {
_wm = minfo.get_weights_momenta(_index); //incoming weights
_bm = minfo.get_biases_momenta(_index); //bias for this layer (starting at hidden layer)
}
if (minfo.adaDelta()) {
_ada_dx_g = minfo.get_ada_dx_g(_index);
_bias_ada_dx_g = minfo.get_biases_ada_dx_g(_index);
}
_shortcut = (params._fast_mode || (
// not doing fast mode, but also don't have anything else to update (neither momentum nor ADADELTA history), and no L1/L2
!params._adaptive_rate && !_minfo.has_momenta() && params._l1 == 0.0 && params._l2 == 0.0));
}
sanityCheck(training);
}
/**
* Forward propagation
* @param seed For seeding the RNG inside (for dropout)
* @param training Whether training is done or just testing (no need for dropout)
*/
protected abstract void fprop(long seed, boolean training);
/**
* Back propagation of error terms stored in _e (for non-final layers)
*/
protected abstract void bprop();
/**
* Back-propagate gradient in output layer
*/
final protected void bpropOutputLayer() {
assert(_index == params._hidden.length);
final int rows = _a.size();
float m = _minfo.adaDelta() ? 0 : momentum();
float r = _minfo.adaDelta() ? 0 : rate(_minfo.get_processed_total()) * (1f - m);
for( int row = 0; row < rows; row++ ) {
final double g = _e.raw()[row];
bprop(row, g, r, m);
}
}
/**
* Accumulation of reconstruction errors for a generic Neurons class
* (This is only used for AutoEncoders)
*/
protected void setOutputLayerGradient(double ignored) {
assert (_minfo.get_params()._autoencoder && _index == _minfo.get_params()._hidden.length);
final int rows = _a.size();
for (int row = 0; row < rows; row++) {
_e.set(row, autoEncoderGradient(row));
}
}
/**
* Backpropagation: w -= rate * dE/dw, where dE/dw = dE/dy * dy/dnet * dnet/dw
* This method adds the dnet/dw = activation term per unit
* @param row row index (update weights feeding to this neuron)
* @param partial_grad partial derivative dE/dnet = dE/dy * dy/net
* @param rate learning rate
* @param momentum momentum factor (needed only if ADADELTA isn't used)
*/
final void bprop(final int row, final double partial_grad, final float rate, final float momentum) {
if (_shortcut && partial_grad == 0f) return;
final float rho = (float)params._rho;
final float eps = (float)params._epsilon;
final float l1 = (float)params._l1;
final float l2 = (float)params._l2;
final float max_w2 = params._max_w2;
final boolean have_momenta = _minfo.has_momenta();
final boolean have_ada = _minfo.adaDelta();
final boolean nesterov = params._nesterov_accelerated_gradient;
final boolean update_prev = _previous._e != null;
final boolean fast_mode = params._fast_mode;
final int cols = _previous._a.size();
double avg_grad2 = 0;
final int idx = row * cols;
for( int col = 0; col < cols; col++ ) {
int w = idx + col;
// for Maxout, return the "winning" linear index into the matrix
if (_k != 0) w = _k * w + _maxIncoming[row];
final double weight = _w.raw()[w];
if( update_prev ) _previous._e.add(col, partial_grad * weight); // propagate the error dE/dnet to the previous layer, via connecting weights
final double previous_a = _previous._a.get(col);
if (fast_mode && previous_a == 0) continue;
//this is the actual gradient dE/dw
double grad = partial_grad * previous_a - Math.signum(weight) * l1 - weight * l2;
if (_wEA !=null) {
grad -= params._elastic_averaging_regularization * (_w.raw()[w] -_wEA.raw()[w]);
// Log.info("weight: my: " + _w.raw()[w] + ", consensus: " + _wEA.raw()[w] + ", delta: " + (_w.raw()[w] -_wEA.raw()[w]) + ", relative delta: " + (_w.raw()[w] -_wEA.raw()[w])/_w.raw()[w]);
}
// store the gradient (grad is the negative gradient)
if (DeepLearningModelInfo.gradientCheck != null)
DeepLearningModelInfo.gradientCheck.apply(_index, row, col, -grad);
if (have_ada) {
final double grad2 = grad*grad;
avg_grad2 += grad2;
float brate = computeAdaDeltaRateForWeight(grad, w, _ada_dx_g, rho, eps);
_w.raw()[w] += brate * grad;
} else {
if (!nesterov) {
final double delta = rate * grad;
_w.raw()[w] += delta;
if( have_momenta ) {
_w.raw()[w] += momentum * _wm.raw()[w];
_wm.raw()[w] = (float)delta;
}
} else {
double tmp = grad;
if( have_momenta ) {
_wm.raw()[w] *= momentum;
_wm.raw()[w] += tmp;
tmp = _wm.raw()[w];
}
_w.raw()[w] += rate * tmp;
}
}
}
if (max_w2 != Float.POSITIVE_INFINITY)
rescale_weights(_w, row, max_w2);
if (have_ada) avg_grad2 /= cols;
update_bias(_b, _bEA, _bm, row, partial_grad, avg_grad2, rate, momentum);
}
private void rescale_weights(final Storage.DenseRowMatrix w, final int row, final float max_w2) {
final int cols = _previous._a.size();
int start;
int end;
if (_k != 0) {
start = _k * (row*cols ) + _maxIncoming[row];
end = _k * (row*cols + (cols-1)) + _maxIncoming[row];
} else {
start = row * cols;
end = row * cols + cols;
}
float r2 = MathUtils.sumSquares(w.raw(), start, end);
// float r2 = MathUtils.approxSumSquares(w.raw(), idx, idx + cols);
if( r2 > max_w2) {
final float scale = MathUtils.approxSqrt(max_w2 / r2);
for( int c = start; c < end; c++ )
w.raw()[c] *= scale;
}
}
/**
* Helper to compute the reconstruction error for auto-encoders (part of the gradient computation)
* @param row neuron index
* @return difference between the output (auto-encoder output layer activation) and the target (input layer activation)
*/
protected double autoEncoderGradient(int row) {
assert (_minfo.get_params()._autoencoder && _index == _minfo.get_params()._hidden.length);
final double t = _input._origa != null ? _input._origa.get(row) : _input._a.get(row);
final double y = _a.get(row);
return _dist.gradient(t, y);
}
/**
* Compute learning rate with AdaDelta, specialized for DenseRowMatrix
* http://www.matthewzeiler.com/pubs/googleTR2012/googleTR2012.pdf
* @param grad gradient
* @param w neuron index
* @param ada_dx_g Matrix holding helper values (2 floats per weight)
* @param rho hyper-parameter #1
* @param eps hyper-parameter #2
* @return learning rate
*/
private static float computeAdaDeltaRateForWeight(final double grad, final int w,
final Storage.DenseRowMatrix ada_dx_g,
final float rho, final float eps) {
final double grad2 = grad*grad;
ada_dx_g.raw()[2*w+1] = (float)(rho * ada_dx_g.raw()[2*w+1] + (1 - rho) * grad2);
final float rate = MathUtils.approxSqrt((ada_dx_g.raw()[2 * w] + eps) / (ada_dx_g.raw()[2 * w + 1] + eps));
ada_dx_g.raw()[2*w ] = (float)(rho * ada_dx_g.raw()[2*w] + (1 - rho) * rate * rate * grad2);
return rate;
}
/**
* Compute learning rate with AdaDelta, specialized for DenseVector (Bias)
* @param grad2 squared gradient
* @param row neuron index
* @param bias_ada_dx_g Matrix holding helper values (2 floats per weight)
* @param rho hyper-parameter #1
* @param eps hyper-parameter #2
* @return learning rate
*/
private static double computeAdaDeltaRateForBias(final double grad2, final int row,
final Storage.DenseVector bias_ada_dx_g,
final float rho, final float eps) {
bias_ada_dx_g.raw()[2*row+1] = rho * bias_ada_dx_g.raw()[2*row+1] + (1f - rho) * grad2;
final double rate = MathUtils.approxSqrt((bias_ada_dx_g.raw()[2 * row] + eps) / (bias_ada_dx_g.raw()[2 * row + 1] + eps));
bias_ada_dx_g.raw()[2*row] = rho * bias_ada_dx_g.raw()[2*row ] + (1f - rho) * rate * rate * grad2;
return rate;
}
/**
* Helper to enforce learning rule to satisfy sparsity constraint:
* Computes the (rolling) average activation for each (hidden) neuron.
*/
void compute_sparsity() {
if (_avg_a != null) {
for (int row = 0; row < _avg_a.size(); row++) {
_avg_a.set(row, 0.999 * (_avg_a.get(row)) + 0.001 * (_a.get(row)));
}
}
}
/**
* Helper to update the bias values
* @param _b bias vector
* @param _bEA elastic average bias vector
* @param _bm bias momentum vector
* @param row index of the neuron for which we back-propagate
* @param partial_grad partial derivative dE/dnet = dE/dy * dy/net
* @param avg_grad2 average squared gradient for this neuron's incoming weights (only for ADADELTA)
* @param rate learning rate
* @param momentum momentum factor (needed only if ADADELTA isn't used)
*/
private void update_bias(final Storage.DenseVector _b, final Storage.DenseVector _bEA, final Storage.DenseVector _bm, final int row,
double partial_grad, final double avg_grad2, double rate, final double momentum) {
final boolean have_momenta = _minfo.has_momenta();
final boolean have_ada = _minfo.adaDelta();
final float l1 = (float)params._l1;
final float l2 = (float)params._l2;
final int b = _k != 0 ? _k*row+_maxIncoming[row] : row;
final double bias = _b.get(b);
partial_grad -= Math.signum(bias) * l1 + bias * l2;
if (_bEA != null) partial_grad -= (bias - _bEA.get(b)) * params._elastic_averaging_regularization;
if (have_ada) {
final float rho = (float)params._rho;
final float eps = (float)params._epsilon;
rate = computeAdaDeltaRateForBias(avg_grad2, b, _bias_ada_dx_g, rho, eps);
}
if (!params._nesterov_accelerated_gradient) {
final double delta = rate * partial_grad;
_b.add(b, delta);
if (have_momenta) {
_b.add(b, momentum * _bm.get(b));
_bm.set(b, delta);
}
} else {
double d = partial_grad;
if (have_momenta) {
_bm.set(b, _bm.get(b) * momentum);
_bm.add(b, d);
d = _bm.get(b);
}
_b.add(b, rate * d);
}
//update for sparsity constraint
if (params._autoencoder && params._sparsity_beta > 0 && !(this instanceof Output) && !(this instanceof Input) && (_index != params._hidden.length)) {
_b.add(b, -(rate * params._sparsity_beta * (_avg_a.raw()[b] - params._average_activation)));
}
if (Double.isInfinite(_b.get(b))) _minfo.setUnstable();
}
/**
* The learning rate
* @param n The number of training samples seen so far (for rate_annealing greater than 0)
* @return Learning rate
*/
public float rate(double n) {
return (float)(params._rate / (1 + params._rate_annealing * n));
}
protected float momentum() {
return momentum(-1);
}
/**
* The momentum - real number in [0, 1)
* Can be a linear ramp from momentum_start to momentum_stable, over momentum_ramp training samples
* @param n The number of training samples seen so far
* @return momentum
*/
final public float momentum(double n) {
double m = params._momentum_start;
if( params._momentum_ramp > 0 ) {
final double num = n != -1 ? _minfo.get_processed_total() : n;
if( num >= params._momentum_ramp)
m = params._momentum_stable;
else
m += (params._momentum_stable - params._momentum_start) * num / params._momentum_ramp;
}
return (float)m;
}
/**
* Input layer of the Neural Network
* This layer is different from other layers as it has no incoming weights,
* but instead gets its activation values from the training points.
*/
public static class Input extends Neurons {
private DataInfo _dinfo; //training data
Input(int units, final DataInfo d) {
super(units);
_dinfo = d;
_a = new Storage.DenseVector(units);
}
@Override protected void bprop() { throw new UnsupportedOperationException(); }
@Override protected void fprop(long seed, boolean training) { throw new UnsupportedOperationException(); }
/**
* One of two methods to set layer input values. This one is for raw double data, e.g. for scoring
* @param seed For seeding the RNG inside (for input dropout)
* @param data Data (training columns and responses) to extract the training columns
* from to be mapped into the input neuron layer
*/
public void setInput(long seed, final double[] data) {
// Log.info("Data: " + ArrayUtils.toString(data));
assert(_dinfo != null);
double [] nums = MemoryManager.malloc8d(_dinfo._nums); // a bit wasteful - reallocated each time
int [] cats = MemoryManager.malloc4(_dinfo._cats); // a bit wasteful - reallocated each time
int i = 0, ncats = 0;
for(; i < _dinfo._cats; ++i){
assert(_dinfo._catMissing[i] != 0); //we now *always* have a categorical level for NAs, just in case.
if (Double.isNaN(data[i])) {
cats[ncats] = (_dinfo._catOffsets[i+1]-1); //use the extra level for NAs made during training
} else {
int c = (int)data[i];
if (_dinfo._useAllFactorLevels)
cats[ncats] = c + _dinfo._catOffsets[i];
else if (c!=0)
cats[ncats] = c + _dinfo._catOffsets[i] - 1;
// If factor level in test set was not seen by training, then turn it into an NA
if (cats[ncats] >= _dinfo._catOffsets[i+1]) {
cats[ncats] = (_dinfo._catOffsets[i+1]-1);
}
}
ncats++;
}
final int n = data.length - (_dinfo._weights ? 1 : 0) - (_dinfo._offset ? 1 : 0);
for(;i < n;++i){
double d = data[i];
if(_dinfo._normMul != null) d = (d - _dinfo._normSub[i-_dinfo._cats])*_dinfo._normMul[i-_dinfo._cats];
nums[i-_dinfo._cats] = d; //can be NaN for missing numerical data
}
setInput(seed, null, nums, ncats, cats);
}
/**
* The second method used to set input layer values. This one is used directly by FrameTask.processRow() and by the method above.
* @param seed For seeding the RNG inside (for input dropout)
* @param nums Array containing numerical values, can be NaN
* @param numcat Number of horizontalized categorical non-zero values (i.e., those not being the first factor of a class)
* @param cats Array of indices, the first numcat values are the input layer unit (==column) indices for the non-zero categorical values
* (This allows this array to be re-usable by the caller, without re-allocating each time)
*/
public void setInput(long seed, final int[] numIds, final double[] nums, final int numcat, final int[] cats) {
Arrays.fill(_a.raw(), 0f);
// random projection from fullN down to max_categorical_features
if (params._max_categorical_features < _dinfo.fullN() - _dinfo._nums) {
assert(nums.length == _dinfo._nums);
final int M = nums.length + params._max_categorical_features;
// final boolean random_projection = false;
// final boolean hash_trick = true;
// if (random_projection) {
// final int N = _dinfo.fullN();
// assert (_a.size() == M);
//
// // sparse random projection
// for (int i = 0; i < M; ++i) {
// for (int c = 0; c < numcat; ++c) {
// int j = cats[c];
// Random rng = RandomUtils.getRNG(params._seed + i * N + j);
// double val = 0;
// final float rnd = rng.nextFloat();
// if (rnd < 1. / 6.) val = Math.sqrt(3);
// if (rnd > 5. / 6.) val = -Math.sqrt(3);
// _a.add(i, 1f * val);
// }
// Random rng = RandomUtils.getRNG(params._seed + i*N + _dinfo.numStart());
// for (int n = 0; n < nums.length; ++n) {
// double val = 0;
// final float rnd = rng.nextFloat();
// if (rnd < 1. / 6.) val = Math.sqrt(3);
// if (rnd > 5. / 6.) val = - Math.sqrt(3);
// _a.set(i, (Double.isNaN(nums[n]) ? 0f /*Always do MeanImputation during scoring*/ : nums[n]) * val);
// }
// }
// } else if (hash_trick) {
// Use hash trick for categorical features
assert (_a.size() == M);
// hash N-nums.length down to M-nums.length = cM (#categorical slots - always use all numerical features)
final int cM = params._max_categorical_features;
assert (_a.size() == M);
MurmurHash murmur = MurmurHash.getInstance();
for (int i = 0; i < numcat; ++i) {
ByteBuffer buf = ByteBuffer.allocate(4);
int hashval = murmur.hash(buf.putInt(cats[i]).array(), 4, (int)params._seed); // turn horizontalized categorical integer into another integer, based on seed
_a.add(Math.abs(hashval % cM), 1f); // restrict to limited range
}
for (int i = 0; i < nums.length; ++i)
_a.set(cM + i, Double.isNaN(nums[i]) ? 0f /*Always do MeanImputation during scoring*/ : nums[i]);
// }
} else {
assert(_a.size() == _dinfo.fullN());
for (int i = 0; i < numcat; ++i) _a.set(cats[i], 1f); // one-hot encode categoricals
if (numIds != null) {
//sparse
for (int i = 0; i < numIds.length; ++i)
_a.set(numIds[i], Double.isNaN(nums[i]) ? 0f /*Always do MeanImputation during scoring*/ : nums[i]);
} else {
//dense
for (int i = 0; i < nums.length; ++i)
_a.set(_dinfo.numStart() + i, Double.isNaN(nums[i]) ? 0f /*Always do MeanImputation during scoring*/ : nums[i]);
}
}
// Input Dropout
if (_dropout == null) return;
if (params._autoencoder && params._input_dropout_ratio > 0) {
// copy input into _origa -- needed for reconstruction error
System.arraycopy(_a.raw(), 0, _origa.raw(), 0, _a.raw().length);
}
seed += params._seed + 0x1337B4BE;
_dropout.randomlySparsifyActivation(_a, seed);
}
}
/**
* Tanh neurons - most common, most stable
*/
public static class Tanh extends Neurons {
public Tanh(int units) { super(units); }
@Override protected void fprop(long seed, boolean training) {
gemv(_a, _w, _previous._a, _b, _dropout != null ? _dropout.bits() : null);
final int rows = _a.size();
for( int row = 0; row < rows; row++ )
_a.set(row, 1. - 2. / (1. + Math.exp(2*_a.get(row)))); //evals faster than tanh(x), but is slightly less numerically stable - OK
compute_sparsity();
}
// Computing partial derivative g = dE/dnet = dE/dy * dy/dnet, where dE/dy is the backpropagated error
// dy/dnet = (1 - a^2) for y(net) = tanh(net)
@Override protected void bprop() {
assert (_index < _minfo.get_params()._hidden.length);
float m = _minfo.adaDelta() ? 0 : momentum();
float r = _minfo.adaDelta() ? 0 : rate(_minfo.get_processed_total()) * (1f - m);
final int rows = _a.size();
for (int row = 0; row < rows; row++) {
double g = _e.get(row) * (1 - _a.get(row) * _a.get(row));
bprop(row, g, r, m);
}
}
}
/**
* Tanh neurons with dropout
*/
public static class TanhDropout extends Tanh {
public TanhDropout(int units) { super(units); }
@Override protected void fprop(long seed, boolean training) {
if (training) {
seed += params._seed + 0xDA7A6000;
_dropout.fillBytes(seed);
super.fprop(seed, true);
}
else {
super.fprop(seed, false);
ArrayUtils.mult(_a.raw(), 1-params._hidden_dropout_ratios[_index]);
}
}
}
/**
* Maxout neurons (picks the max out of the k activation_j = sum(A_ij*x_i) + b_j)
* Requires k times the model parameters (weights/biases) as a "normal" neuron
*/
public static class Maxout extends Neurons {
public Maxout(short k, int units) { super(units);
_k = k;
_maxIncoming=new int[units];
if (_k!=2) throw H2O.unimpl("Maxout is currently hardcoded for 2 channels. Trivial to enable k > 2 though.");
}
@Override protected void fprop(long seed, boolean training) {
assert(_b.size() == _a.size() * _k);
assert(_w.size() == _a.size() * _previous._a.size() * _k);
final int rows = _a.size();
double[] channel = new double[_k];
for( int row = 0; row < rows; row++ ) {
_a.set(row, 0);
if( !training || _dropout == null || _dropout.unit_active(row) ) {
final int cols = _previous._a.size();
// For each neuron in the previous layer, there's k channels
// Each channel has its own weight and bias values
// The channel leading to the highest incoming value (W*x + b) is the "winner" and will activate this neuron
short maxK = 0;
for( short k = 0; k < _k; k++ ) {
channel[k] = 0;
for( int col = 0; col < cols; col++ ) {
channel[k] += _w.raw()[_k*(row * cols + col) + k] * _previous._a.get(col);
}
channel[k] += _b.raw()[_k*row+k];
if (channel[k] > channel[maxK]) maxK=k;
}
_maxIncoming[row] = maxK;
_a.set(row, channel[maxK]);
}
}
compute_sparsity();
}
@Override protected void bprop() {
assert(_index != params._hidden.length);
float m = _minfo.adaDelta() ? 0 : momentum();
float r = _minfo.adaDelta() ? 0 : rate(_minfo.get_processed_total()) * (1f - m);
final int rows = _a.size();
for (int row = 0; row < rows; row++) {
double g = _e.get(row);
bprop(row, g, r, m);
}
}
}
/**
* Maxout neurons with dropout
*/
public static class MaxoutDropout extends Maxout {
public MaxoutDropout(short k, int units) { super(k,units); }
@Override protected void fprop(long seed, boolean training) {
if (training) {
seed += params._seed + 0x51C8D00D;
_dropout.fillBytes(seed);
super.fprop(seed, true);
}
else {
super.fprop(seed, false);
ArrayUtils.mult(_a.raw(), 1-params._hidden_dropout_ratios[_index]);
}
}
}
/**
* Rectifier linear unit (ReLU) neurons
*/
public static class Rectifier extends Neurons {
public Rectifier(int units) { super(units); }
@Override protected void fprop(long seed, boolean training) {
gemv(_a, _w, _previous._a, _b, _dropout != null ? _dropout.bits() : null);
final int rows = _a.size();
for( int row = 0; row < rows; row++ ) {
_a.set(row, 0.5f* (_a.get(row) + Math.abs(_a.get(row)))); //faster than max(a, 0)
// _a.set(row, Math.max(_a.get(row), 0f));
compute_sparsity();
}
}
@Override protected void bprop() {
assert (_index < _minfo.get_params()._hidden.length);
float m = _minfo.adaDelta() ? 0 : momentum();
float r = _minfo.adaDelta() ? 0 : rate(_minfo.get_processed_total()) * (1f - m);
final int rows = _a.size();
for (int row = 0; row < rows; row++) {
//(d/dx)(max(0,x)) = 1 if x > 0, otherwise 0
double g = _a.get(row) > 0f ? _e.get(row) : 0f;
bprop(row, g, r, m);
}
}
}
/**
* Rectifier linear unit (ReLU) neurons with dropout
*/
public static class RectifierDropout extends Rectifier {
public RectifierDropout(int units) { super(units); }
@Override protected void fprop(long seed, boolean training) {
if (training) {
seed += params._seed + 0x3C71F1ED;
_dropout.fillBytes(seed);
super.fprop(seed, true);
}
else {
super.fprop(seed, false);
ArrayUtils.mult(_a.raw(), 1-params._hidden_dropout_ratios[_index]);
}
}
}
/**
* Abstract class for Output neurons
*/
public static abstract class Output extends Neurons {
Output(int units) { super(units); }
protected void bprop() { throw new UnsupportedOperationException(); }
}
/**
* Output neurons for classification - Softmax
*/
public static class Softmax extends Output {
public Softmax(int units) { super(units); }
protected void fprop(long seed, boolean training) {
gemv(_a, _w, _previous._a, _b, null);
final double max = ArrayUtils.maxValue(_a.raw());
double scaling = 0;
final int rows = _a.size();
for( int row = 0; row < rows; row++ ) {
_a.set(row, Math.exp(_a.get(row) - max));
scaling += _a.get(row);
}
for( int row = 0; row < rows; row++ ) {
_a.raw()[row] /= scaling;
}
}
/**
* Part of backpropagation for classification
* Update every weight as follows: w += -rate * dE/dw
* Compute dE/dw via chain rule: dE/dw = dE/dy * dy/dnet * dnet/dw, where net = sum(xi*wi)+b and y = activation function
* @param target actual class label (integer)
*/
@Override protected void setOutputLayerGradient(double target) {
assert(target == (int)target);
double g; //partial derivative dE/dy * dy/dnet
final int rows = _a.size();
for( int row = 0; row < rows; row++ ) {
final double t = (row == (int)target ? 1 : 0);
final double y = _a.get(row);
//dy/dnet = derivative of softmax = (1-y)*y
switch(params._loss) {
case Automatic:
case CrossEntropy:
//nothing else needed, -dCE/dy * dy/dnet = target - y
//cf. http://www.stanford.edu/group/pdplab/pdphandbook/handbookch6.html
g = t - y;
break;
case Absolute:
g = (2 * t - 1) * (1f - y) * y; //-dL/dy = 2*t-1
break;
case Quadratic:
//-dMSE/dy = target-y
g = (t - y) * (1f - y) * y;
break;
case Huber:
if (t == 0) {
if (y < 0.5) {
g = -4 * y; //L=2*y^2 for y<0.5
} else {
g = -2; //L=2*y-0.5 for y>=0.5
}
} else {
if (y > 0.5) {
g = 4 * (1 - y); //L=2*(1-y)^2 for y<0.5
} else {
g = 2; //L=2*(1-y)-0.5 for y>=0.5
}
}
g *= (1f - y) * y;
break;
default:
throw H2O.unimpl();
}
_e.set(row, g);
}
}
}
/**
* Output neurons for regression - Linear units
*/
public static class Linear extends Output {
public Linear() {
super(1);
}
protected void fprop(long seed, boolean training) {
gemv(_a, _w, _previous._a, _b, _dropout != null ? _dropout.bits() : null);
}
/**
* Backpropagation for regression
* @param target floating-point target value
*/
@Override protected void setOutputLayerGradient(double target) {
final int row = 0;
final double y = _a.get(row);
double g = _dist.gradient(target, y); //y is in link space
_e.set(row, g);
}
}
/**
* Mat-Vec Plus Add (with optional row dropout)
* @param res = a*x+y (pre-allocated, will be overwritten)
* @param a matrix of size rows x cols
* @param x vector of length cols
* @param y vector of length rows
* @param row_bits if not null, check bits of this byte[] to determine whether a row is used or not
*/
static void gemv_naive(final double[] res, final float[] a, final double[] x, final double[] y, byte[] row_bits) {
final int cols = x.length;
final int rows = y.length;
assert(res.length == rows);
for(int row = 0; row© 2015 - 2025 Weber Informatics LLC | Privacy Policy