mirror of
https://github.com/ClickHouse/ClickHouse.git
synced 2024-11-21 15:12:02 +00:00
adam is default now
This commit is contained in:
Alexander Kozhikhov
parent
7c54bb0956
commit
52007c96d9
@ -45,11 +45,11 @@ namespace
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/// Such default parameters were picked because they did good on some tests,
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/// though it still requires to fit parameters to achieve better result
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auto learning_rate = Float64(0.01);
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auto l2_reg_coef = Float64(0.1);
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auto learning_rate = Float64(1.0);
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auto l2_reg_coef = Float64(0.5);
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UInt64 batch_size = 15;
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std::string weights_updater_name = "SGD";
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std::string weights_updater_name = "Adam";
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std::unique_ptr<IGradientComputer> gradient_computer;
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if (!parameters.empty())
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@ -126,7 +126,7 @@ void LinearModelData::update_state()
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if (batch_size == 0)
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return;
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weights_updater->update(batch_size, weights, bias, gradient_batch);
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weights_updater->update(batch_size, weights, bias, learning_rate, gradient_batch);
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batch_size = 0;
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++iter_num;
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gradient_batch.assign(gradient_batch.size(), Float64{0.0});
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@ -211,7 +211,7 @@ void LinearModelData::add(const IColumn ** columns, size_t row_num)
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/// Here we have columns + 1 as first column corresponds to target value, and others - to features
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weights_updater->add_to_batch(
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gradient_batch, *gradient_computer, weights, bias, learning_rate, l2_reg_coef, target, columns + 1, row_num);
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gradient_batch, *gradient_computer, weights, bias, l2_reg_coef, target, columns + 1, row_num);
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++batch_size;
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if (batch_size == batch_capacity)
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@ -256,7 +256,7 @@ void Adam::merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac)
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beta2_powered_ *= adam_rhs.beta2_powered_;
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}
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void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
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void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
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{
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if (average_gradient.empty())
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{
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@ -267,7 +267,6 @@ void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & b
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average_squared_gradient.resize(batch_gradient.size(), Float64{0.0});
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}
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/// batch_gradient already includes learning_rate - bad for squared gradient
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for (size_t i = 0; i != average_gradient.size(); ++i)
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{
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Float64 normed_gradient = batch_gradient[i] / batch_size;
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@ -278,10 +277,10 @@ void Adam::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & b
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for (size_t i = 0; i < weights.size(); ++i)
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{
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weights[i] += average_gradient[i] /
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weights[i] += (learning_rate * average_gradient[i]) /
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((1 - beta1_powered_) * (sqrt(average_squared_gradient[i] / (1 - beta2_powered_)) + eps_));
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}
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bias += average_gradient[weights.size()] /
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bias += (learning_rate * average_gradient[weights.size()]) /
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((1 - beta1_powered_) * (sqrt(average_squared_gradient[weights.size()] / (1 - beta2_powered_)) + eps_));
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beta1_powered_ *= beta1_;
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@ -293,7 +292,6 @@ void Adam::add_to_batch(
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IGradientComputer & gradient_computer,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -304,7 +302,7 @@ void Adam::add_to_batch(
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average_gradient.resize(batch_gradient.size(), Float64{0.0});
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average_squared_gradient.resize(batch_gradient.size(), Float64{0.0});
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}
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gradient_computer.compute(batch_gradient, weights, bias, learning_rate, l2_reg_coef, target, columns, row_num);
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gradient_computer.compute(batch_gradient, weights, bias, l2_reg_coef, target, columns, row_num);
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}
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void Nesterov::read(ReadBuffer & buf)
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@ -329,7 +327,7 @@ void Nesterov::merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac
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}
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}
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void Nesterov::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
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void Nesterov::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
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{
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if (accumulated_gradient.empty())
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{
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@ -338,7 +336,7 @@ void Nesterov::update(UInt64 batch_size, std::vector<Float64> & weights, Float64
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for (size_t i = 0; i < batch_gradient.size(); ++i)
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{
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accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + batch_gradient[i] / batch_size;
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accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + (learning_rate * batch_gradient[i]) / batch_size;
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}
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for (size_t i = 0; i < weights.size(); ++i)
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{
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@ -352,7 +350,6 @@ void Nesterov::add_to_batch(
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IGradientComputer & gradient_computer,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -370,7 +367,7 @@ void Nesterov::add_to_batch(
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}
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auto shifted_bias = bias + accumulated_gradient[weights.size()] * alpha_;
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gradient_computer.compute(batch_gradient, shifted_weights, shifted_bias, learning_rate, l2_reg_coef, target, columns, row_num);
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gradient_computer.compute(batch_gradient, shifted_weights, shifted_bias, l2_reg_coef, target, columns, row_num);
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}
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void Momentum::read(ReadBuffer & buf)
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@ -392,7 +389,7 @@ void Momentum::merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac
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}
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}
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void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
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void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
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{
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/// batch_size is already checked to be greater than 0
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if (accumulated_gradient.empty())
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@ -402,7 +399,7 @@ void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64
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for (size_t i = 0; i < batch_gradient.size(); ++i)
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{
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accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + batch_gradient[i] / batch_size;
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accumulated_gradient[i] = accumulated_gradient[i] * alpha_ + (learning_rate * batch_gradient[i]) / batch_size;
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}
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for (size_t i = 0; i < weights.size(); ++i)
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{
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@ -412,14 +409,14 @@ void Momentum::update(UInt64 batch_size, std::vector<Float64> & weights, Float64
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}
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void StochasticGradientDescent::update(
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UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient)
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UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient)
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{
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/// batch_size is already checked to be greater than 0
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for (size_t i = 0; i < weights.size(); ++i)
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{
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weights[i] += batch_gradient[i] / batch_size;
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weights[i] += (learning_rate * batch_gradient[i]) / batch_size;
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}
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bias += batch_gradient[weights.size()] / batch_size;
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bias += (learning_rate * batch_gradient[weights.size()]) / batch_size;
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}
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void IWeightsUpdater::add_to_batch(
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@ -427,13 +424,12 @@ void IWeightsUpdater::add_to_batch(
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IGradientComputer & gradient_computer,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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size_t row_num)
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{
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gradient_computer.compute(batch_gradient, weights, bias, learning_rate, l2_reg_coef, target, columns, row_num);
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gradient_computer.compute(batch_gradient, weights, bias, l2_reg_coef, target, columns, row_num);
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}
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/// Gradient computers
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@ -479,7 +475,6 @@ void LogisticRegression::compute(
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std::vector<Float64> & batch_gradient,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -494,11 +489,11 @@ void LogisticRegression::compute(
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derivative *= target;
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derivative = exp(derivative);
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batch_gradient[weights.size()] += learning_rate * target / (derivative + 1);
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batch_gradient[weights.size()] += target / (derivative + 1);
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for (size_t i = 0; i < weights.size(); ++i)
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{
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auto value = (*columns[i]).getFloat64(row_num);
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batch_gradient[i] += learning_rate * target * value / (derivative + 1) - 2 * learning_rate * l2_reg_coef * weights[i];
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batch_gradient[i] += target * value / (derivative + 1) - 2 * l2_reg_coef * weights[i];
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}
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}
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@ -551,7 +546,6 @@ void LinearRegression::compute(
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std::vector<Float64> & batch_gradient,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -563,13 +557,13 @@ void LinearRegression::compute(
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auto value = (*columns[i]).getFloat64(row_num);
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derivative -= weights[i] * value;
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}
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derivative *= (2 * learning_rate);
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derivative *= 2;
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batch_gradient[weights.size()] += derivative;
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for (size_t i = 0; i < weights.size(); ++i)
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{
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auto value = (*columns[i]).getFloat64(row_num);
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batch_gradient[i] += derivative * value - 2 * learning_rate * l2_reg_coef * weights[i];
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batch_gradient[i] += derivative * value - 2 * l2_reg_coef * weights[i];
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}
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}
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@ -33,7 +33,6 @@ public:
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std::vector<Float64> & batch_gradient,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -60,7 +59,6 @@ public:
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std::vector<Float64> & batch_gradient,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -87,7 +85,6 @@ public:
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std::vector<Float64> & batch_gradient,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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@ -120,14 +117,18 @@ public:
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IGradientComputer & gradient_computer,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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size_t row_num);
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/// Updates current weights according to the gradient from the last mini-batch
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virtual void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & gradient) = 0;
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virtual void update(
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UInt64 batch_size,
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std::vector<Float64> & weights,
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Float64 & bias,
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Float64 learning_rate,
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const std::vector<Float64> & gradient) = 0;
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/// Used during the merge of two states
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virtual void merge(const IWeightsUpdater &, Float64, Float64) {}
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@ -143,7 +144,7 @@ public:
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class StochasticGradientDescent : public IWeightsUpdater
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{
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public:
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
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};
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@ -154,7 +155,7 @@ public:
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Momentum(Float64 alpha) : alpha_(alpha) {}
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
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virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override;
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@ -180,13 +181,12 @@ public:
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IGradientComputer & gradient_computer,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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size_t row_num) override;
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
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virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override;
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@ -195,7 +195,7 @@ public:
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void read(ReadBuffer & buf) override;
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private:
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Float64 alpha_{0.1};
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const Float64 alpha_ = 0.9;
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std::vector<Float64> accumulated_gradient;
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};
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@ -214,13 +214,12 @@ public:
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IGradientComputer & gradient_computer,
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const std::vector<Float64> & weights,
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Float64 bias,
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Float64 learning_rate,
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Float64 l2_reg_coef,
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Float64 target,
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const IColumn ** columns,
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size_t row_num) override;
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, const std::vector<Float64> & batch_gradient) override;
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void update(UInt64 batch_size, std::vector<Float64> & weights, Float64 & bias, Float64 learning_rate, const std::vector<Float64> & batch_gradient) override;
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virtual void merge(const IWeightsUpdater & rhs, Float64 frac, Float64 rhs_frac) override;
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@ -235,7 +234,7 @@ private:
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const Float64 eps_ = 0.000001;
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Float64 beta1_powered_;
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Float64 beta2_powered_;
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std::vector<Float64> average_gradient;
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std::vector<Float64> average_squared_gradient;
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};
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@ -16,7 +16,7 @@ select ans < -61.374 and ans > -61.375 from
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(with (select state from remote('127.0.0.1', currentDatabase(), model)) as model select evalMLMethod(model, predict1, predict2) as ans from remote('127.0.0.1', currentDatabase(), defaults));
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SELECT 0 < ans[1] and ans[1] < 0.15 and 0.95 < ans[2] and ans[2] < 1.0 and 0 < ans[3] and ans[3] < 0.05 FROM
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(SELECT stochasticLinearRegression(0.000001, 0.01, 100)(number, rand() % 100, number) AS ans FROM numbers(1000));
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(SELECT stochasticLinearRegression(0.000001, 0.01, 100, 'SGD')(number, rand() % 100, number) AS ans FROM numbers(1000));
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DROP TABLE model;
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DROP TABLE defaults;
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@ -11,43 +11,43 @@
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0.6542885368159769
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0.6542885368159769
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0.6542885368159769
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0.8444267125384497
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0.968375124847465
|
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@ -60,18 +60,18 @@
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@ -97,18 +97,18 @@
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@ -146,43 +146,43 @@
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@ -196,18 +196,18 @@
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@ -220,19 +220,19 @@
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@ -257,31 +257,31 @@
|
||||
0.6656871036437929
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@ -307,18 +307,18 @@
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@ -344,18 +344,18 @@
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||||
|
||||
File diff suppressed because one or more lines are too long
Loading…
Reference in New Issue
Block a user