org.nd4j.autodiff.samediff.ops.SDNN Maven / Gradle / Ivy
/*******************************************************************************
* Copyright (c) 2015-2019 Skymind, Inc.
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://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.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
package org.nd4j.autodiff.samediff.ops;
import lombok.NonNull;
import org.nd4j.autodiff.samediff.SDVariable;
import org.nd4j.autodiff.samediff.SameDiff;
import org.nd4j.linalg.api.ops.impl.layers.convolution.config.Pooling2DConfig;
import org.nd4j.linalg.api.ops.impl.transforms.Pad;
import org.nd4j.linalg.factory.Nd4j;
import java.util.Arrays;
import java.util.Collections;
import java.util.List;
import static org.nd4j.autodiff.samediff.ops.SDValidation.validateFloatingPoint;
/**
* SameDiff general neural network operations
* Accessible via {@link SameDiff#math()}
* See also {@link SDCNN} (accessible via {@link SameDiff#cnn()} for convolutional neural network ops.
* See also {@link SDRNN} (accessible via {@link SameDiff#rnn()} for recurrent neural network ops.
*
* @author Alex Black
*/
public class SDNN extends SDOps {
public SDNN(SameDiff sameDiff) {
super(sameDiff);
}
/**
* Batch norm operation.
*
* @see #batchNorm(String, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, double, int...)
*/
public SDVariable batchNorm(SDVariable input, SDVariable mean,
SDVariable variance, SDVariable gamma,
SDVariable beta, double epsilon, int... axis) {
return batchNorm(null, input, mean, variance, gamma, beta, true, true, epsilon, axis);
}
/**
* Batch normalization with optional application of gamma/beta args.
* See {@link #batchNorm(String, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, double, int...)}
*/
public SDVariable batchNorm(String name, SDVariable input, SDVariable mean,
SDVariable variance, SDVariable gamma,
SDVariable beta, boolean applyGamma, boolean applyBeta, double epsilon, int... axis) {
validateFloatingPoint("batchNorm", "input", input);
validateFloatingPoint("batchNorm", "mean", mean);
validateFloatingPoint("batchNorm", "variance", variance);
validateFloatingPoint("batchNorm", "gamma", gamma);
validateFloatingPoint("batchNorm", "beta", beta);
SDVariable res = f().batchNorm(input, mean, variance, gamma, beta, applyGamma, applyBeta, epsilon, axis);
return updateVariableNameAndReference(res, name);
}
/**
* Neural network batch normalization operation.
* For details, see https://arxiv.org/abs/1502.03167
*
* @param name Name of the output variable
* @param input Input variable.
* @param mean Mean value. For 1d axis, this should match input.size(axis)
* @param variance Variance value. For 1d axis, this should match input.size(axis)
* @param gamma Gamma value. For 1d axis, this should match input.size(axis)
* @param beta Beta value. For 1d axis, this should match input.size(axis)
* @param epsilon Epsilon constant for numerical stability (to avoid division by 0)
* @param axis For 2d CNN activations: 1 for NCHW format activations, or 3 for NHWC format activations.
* For 3d CNN activations: 1 for NCDHW format, 4 for NDHWC
* For 1d/RNN activations: 1 for NCW format, 2 for NWC
* @return Output variable for batch normalization
*/
public SDVariable batchNorm(String name, SDVariable input, SDVariable mean,
SDVariable variance, SDVariable gamma,
SDVariable beta, double epsilon, int... axis) {
return batchNorm(name, input, mean, variance, gamma, beta, true, true, epsilon, axis);
}
/**
* @see #biasAdd(String, SDVariable, SDVariable, boolean)
*/
public SDVariable biasAdd(SDVariable input, SDVariable bias, boolean nchw) {
return biasAdd(null, input, bias, nchw);
}
/**
* Bias addition operation: a special case of addition, typically used with CNN 4D activations and a 1D bias vector
*
* @param name Name of the output variable
* @param input 4d input variable
* @param bias 1d bias
* @param nchw The format - nchw=true means [minibatch, channels, height, width] format; nchw=false - [minibatch, height, width, channels].
* Unused for 2d inputs
* @return Output variable
*/
public SDVariable biasAdd(String name, SDVariable input, SDVariable bias, boolean nchw) {
validateFloatingPoint("biasAdd", "input", input);
validateFloatingPoint("biasAdd", "bias", bias);
SDVariable ret = f().biasAdd(input, bias, nchw);
return updateVariableNameAndReference(ret, name);
}
/**
* @param input Input
* @param inputRetainProbability Probability of retaining an input (set to 0 with probability 1-p)
* @return
*/
public SDVariable dropout(SDVariable input, double inputRetainProbability) {
return dropout(null, input, inputRetainProbability);
}
/**
* @param input Input
* @param inputRetainProbability Probability of retaining an input (set to 0 with probability 1-p)
* @return
*/
public SDVariable dropout(String name, SDVariable input, double inputRetainProbability) {
validateFloatingPoint("dropout", input);
SDVariable res = f().dropout(input, inputRetainProbability);
return updateVariableNameAndReference(res, name);
}
/**
* Element-wise exponential linear unit (ELU) function:
* out = x if x > 0
* out = a * (exp(x) - 1) if x <= 0
* with constant a = 1.0
*
* See: https://arxiv.org/abs/1511.07289
*
* @param x Input variable
* @return Output variable
*/
public SDVariable elu(SDVariable x) {
return elu(null, x);
}
/**
* Element-wise exponential linear unit (ELU) function:
* out = x if x > 0
* out = a * (exp(x) - 1) if x <= 0
* with constant a = 1.0
*
* See: https://arxiv.org/abs/1511.07289
*
* @param name Output variable name
* @param x Input variable
* @return Output variable
*/
public SDVariable elu(String name, SDVariable x) {
validateFloatingPoint("elu", x);
SDVariable result = f().elu(x);
return updateVariableNameAndReference(result, name);
}
/**
* GELU activation function - Gaussian Error Linear Units
* For more details, see Gaussian Error Linear Units (GELUs) - https://arxiv.org/abs/1606.08415
* This method uses the sigmoid approximation
*
* @param x Input
* @return Output variable - GELU applied to the input
*/
public SDVariable gelu(SDVariable x) {
return gelu(null, x);
}
/**
* GELU activation function - Gaussian Error Linear Units
* For more details, see Gaussian Error Linear Units (GELUs) - https://arxiv.org/abs/1606.08415
* This method uses the sigmoid approximation
*
* @param name Name of the output variable. May be null.
* @param x Input
* @return Output variable - GELU applied to the input
*/
public SDVariable gelu(String name, SDVariable x) {
validateFloatingPoint("gelu", x);
SDVariable ret = f().gelu(x, false); //Defaults to si
return updateVariableNameAndReference(ret, name);
}
/**
* Element-wise hard sigmoid function:
* out[i] = 0 if in[i] <= -2.5
* out[1] = 0.2*in[i]+0.5 if -2.5 < in[i] < 2.5
* out[i] = 1 if in[i] >= 2.5
*
* @param in Input variable
* @return Output variable
*/
public SDVariable hardSigmoid(SDVariable in) {
return hardSigmoid(null, in);
}
/**
* Element-wise hard sigmoid function:
* out[i] = 0 if in[i] <= -2.5
* out[1] = 0.2*in[i]+0.5 if -2.5 < in[i] < 2.5
* out[i] = 1 if in[i] >= 2.5
*
* @param name Name of the output variable
* @param in Input variable
* @return Output variable
*/
public SDVariable hardSigmoid(String name, SDVariable in) {
validateFloatingPoint("hard sigmoid", in);
SDVariable ret = f().hardSigmoid(in);
return updateVariableNameAndReference(ret, name);
}
/**
* Element-wise hard tanh function:
* out[i] = -1 if in[i] <= -1
* out[1] = in[i] if -1 < in[i] < 1
* out[i] = 1 if in[i] >= 1
*
* @param in Input variable
* @return Output variable
*/
public SDVariable hardTanh(SDVariable in) {
return hardTanh(null, in);
}
/**
* Element-wise hard tanh function:
* out[i] = -1 if in[i] <= -1
* out[1] = in[i] if -1 < in[i] < 1
* out[i] = 1 if in[i] >= 1
*
* @param name Output variable name
* @param in Input variable
* @return Output variable
*/
public SDVariable hardTanh(String name, SDVariable in) {
validateFloatingPoint("hard Tanh", in);
SDVariable result = f().hardTanh(in);
return updateVariableNameAndReference(result, name);
}
/**
* Derivative (dOut/dIn) of the element-wise hard Tanh function - {@link #hardTanh(SDVariable)}
*
* @param x Input
* @return Output variable
*/
public SDVariable hardTanhDerivative(SDVariable x) {
return hardTanhDerivative(null, x);
}
/**
* Derivative (dOut/dIn) of the element-wise hard Tanh function - {@link #hardTanh(SDVariable)}
*
* @param name Output variable name
* @param x Input
* @return Output variable
*/
public SDVariable hardTanhDerivative(String name, SDVariable x) {
validateFloatingPoint("hard Tanh derivative", x);
SDVariable result = f().hardTanhDerivative(x);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise leaky ReLU function:
* out = x if x >= 0.0
* out = alpha * x if x < cutoff
* Alpha value is most commonly set to 0.01
*
* @param x Input variable
* @param alpha Cutoff - usually 0.0
* @return Output variable
*/
public SDVariable leakyRelu(SDVariable x, double alpha) {
return leakyRelu(null, x, alpha);
}
/**
* Element-wise leaky ReLU function:
* out = x if x >= 0.0
* out = alpha * x if x < cutoff
* Alpha value is most commonly set to 0.01
*
* @param x Input variable
* @param alpha Cutoff - usually 0.0
* @return Output variable
*/
public SDVariable leakyRelu(String name, SDVariable x, double alpha) {
validateFloatingPoint("leaky ReLU", x);
SDVariable result = f().leakyRelu(x, alpha);
return updateVariableNameAndReference(result, name);
}
/**
* Leaky ReLU derivative: dOut/dIn given input.
* See {@link #leakyRelu(String, SDVariable, double)}
*
* @param x Input variable
* @param alpha Alpha value
* @return Output variable
*/
public SDVariable leakyReluDerivative(String name, SDVariable x, double alpha) {
validateFloatingPoint("leaky ReLU derivative", x);
SDVariable result = f().leakyReluDerivative(x, alpha);
return updateVariableNameAndReference(result, name);
}
/**
* @see #linear(String, SDVariable, SDVariable, SDVariable)
*/
public SDVariable linear(SDVariable input, SDVariable weights, SDVariable bias) {
return linear(null, input, weights, bias);
}
/**
* Linear layer operation: out = mmul(in,w) + bias
* Note that bias array is optional
*
* @param name Name of the output variable
* @param input Input data
* @param weights Weights variable
* @param bias Optional bias variable (may be null)
* @return Output variable
*/
public SDVariable linear(String name, SDVariable input, SDVariable weights, SDVariable bias) {
validateFloatingPoint("linear", "input", input);
validateFloatingPoint("linear", "weights", weights);
validateFloatingPoint("linear", "bias", bias);
SDVariable res = f().xwPlusB(input, weights, bias);
return updateVariableNameAndReference(res, name);
}
/**
* Element-wise sigmoid function: out[i] = log(sigmoid(in[i]))
*
* @param x Input Variable
* @return Output variable
*/
public SDVariable logSigmoid(SDVariable x) {
return logSigmoid(null, x);
}
/**
* Element-wise sigmoid function: out[i] = log(sigmoid(in[i]))
*
* @param name Name of the output variable
* @param x Input Variable
* @return Output variable
*/
public SDVariable logSigmoid(String name, SDVariable x) {
validateFloatingPoint("log sigmoid", x);
SDVariable ret = f().logSigmoid(x);
return updateVariableNameAndReference(ret, name);
}
/**
* Log softmax activation
*
* @param x Input variable
* @return Output variable
*/
public SDVariable logSoftmax(SDVariable x) {
return logSoftmax(null, x);
}
/**
* Log softmax activation
*
* @param name Variable name
* @param x Input variable
* @return Output variable
*/
public SDVariable logSoftmax(String name, SDVariable x) {
validateFloatingPoint("log softmax", x);
SDVariable ret = f().logSoftmax(x);
return updateVariableNameAndReference(ret, name);
}
/**
* Log softmax activation
*
* @param x Input variable
* @return Output variable
*/
public SDVariable logSoftmax(SDVariable x, int dimension) {
return logSoftmax(null, x, dimension);
}
/**
* Log softmax activation
*
* @param name Variable name
* @param x Input variable
* @return Output variable
*/
public SDVariable logSoftmax(String name, SDVariable x, int dimension) {
validateFloatingPoint("log softmax", x);
SDVariable ret = f().logSoftmax(x, dimension);
return updateVariableNameAndReference(ret, name);
}
/**
* Element-wise rectified linear function with specified cutoff:
* out[i] = in[i] if in[i] >= cutoff
* out[i] = 0 otherwise
*
* @param x Input variable
* @param cutoff Cutoff value. Usually 0
* @return Output variable
*/
public SDVariable relu(SDVariable x, double cutoff) {
return relu(null, x, cutoff);
}
/**
* Element-wise rectified linear function with specified cutoff:
* out[i] = in[i] if in[i] >= cutoff
* out[i] = 0 otherwise
*
* @param name Output variable name
* @param x Input variable
* @param cutoff Cutoff value. Usually 0
* @return Output variable
*/
public SDVariable relu(String name, SDVariable x, double cutoff) {
validateFloatingPoint("ReLU", x);
SDVariable result = f().relu(x, cutoff);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise "rectified linear 6" function with specified cutoff:
* out[i] = min(max(in, cutoff), 6)
*
* @param x Input variable
* @param cutoff Cutoff value. Usually 0
* @return Output variable
*/
public SDVariable relu6(SDVariable x, double cutoff) {
return relu6(null, x, cutoff);
}
/**
* Element-wise "rectified linear 6" function with specified cutoff:
* out[i] = min(max(in, cutoff), 6)
*
* @param name Output variable name
* @param x Input variable
* @param cutoff Cutoff value. Usually 0
* @return Output variable
*/
public SDVariable relu6(String name, SDVariable x, double cutoff) {
validateFloatingPoint("ReLU6", x);
SDVariable result = f().relu6(x, cutoff);
return updateVariableNameAndReference(result, name);
}
/**
* @see #reluLayer(String, SDVariable, SDVariable, SDVariable)
*/
public SDVariable reluLayer(SDVariable input, SDVariable weights, SDVariable bias) {
return reluLayer(null, input, weights, bias);
}
/**
* ReLU (Rectified Linear Unit) layer operation: out = relu(mmul(in,w) + bias)
* Note that bias array is optional
*
* @param name Name of the output variable
* @param input Input data
* @param weights Weights variable
* @param bias Optional bias variable (may be null)
* @return Output variable
*/
public SDVariable reluLayer(String name, SDVariable input, SDVariable weights, SDVariable bias) {
validateFloatingPoint("reluLayer", "input", input);
validateFloatingPoint("reluLayer", "weights", weights);
validateFloatingPoint("reluLayer", "bias", bias);
SDVariable res = f().reluLayer(input, weights, bias);
return updateVariableNameAndReference(res, name);
}
/**
* See {@link #prelu(String, SDVariable, SDVariable, int...)}.
*/
public SDVariable prelu(@NonNull SDVariable input, @NonNull SDVariable alpha, @NonNull int... sharedAxes){
return f().prelu(input, alpha, sharedAxes);
}
/**
* PReLU (Parameterized Rectified Linear Unit) operation. Like LeakyReLU with a learnable alpha:
* out[i] = in[i] if in[i] >= 0
* out[i] = in[i] * alpha[i] otherwise
*
* sharedAxes allows you to share learnable parameters along axes.
* For example, if the input has shape [batchSize, channels, height, width]
* and you want each channel to have its own cutoff, use sharedAxes = [2, 3] and an
* alpha with shape [channels].
*
* @param name Name of the output variable
* @param input Input data
* @param alpha The cutoff variable. Note that the batch dimension (the 0th, whether it is batch or not) should not be part of alpha.
* @param sharedAxes Which axes to share cutoff parameters along.
* @return Output variable
*/
public SDVariable prelu(String name, @NonNull SDVariable input, @NonNull SDVariable alpha, @NonNull int... sharedAxes){
SDVariable res = f().prelu(input, alpha, sharedAxes);
return updateVariableNameAndReference(res, name);
}
/**
* Element-wise SeLU function - Scaled exponential Lineal Unit: see Self-Normalizing Neural Networks
*
* out[i] = scale * alpha * (exp(in[i])-1) if in[i]>0, or 0 if in[i] <= 0
* Uses default lcale and alpha values.
*
* @param x Input variable
* @return Output variable
*/
public SDVariable selu(SDVariable x) {
return selu(null, x);
}
/**
* Element-wise SeLU function - Scaled exponential Lineal Unit: see Self-Normalizing Neural Networks
*
* out[i] = scale * alpha * (exp(in[i])-1) if in[i]>0, or 0 if in[i] <= 0
* Uses default lcale and alpha values.
*
* @param name Name of the output variable
* @param x Input variable
* @return Output variable
*/
public SDVariable selu(String name, SDVariable x) {
validateFloatingPoint("selu", x);
SDVariable ret = f().selu(x);
return updateVariableNameAndReference(ret, name);
}
/**
* Element-wise sigmoid function: out[i] = 1.0/(1+exp(-in[i]))
*
* @param x Input Variable
* @return Output variable
*/
public SDVariable sigmoid(SDVariable x) {
return sigmoid(null, x);
}
/**
* Element-wise sigmoid function: out[i] = 1.0/(1+exp(-in[i]))
*
* @param name Output variable name
* @param x Input Variable
* @return Output variable
*/
public SDVariable sigmoid(String name, SDVariable x) {
validateFloatingPoint("sigmoid", x);
SDVariable result = f().sigmoid(x);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise sigmoid function derivative: dL/dIn given input and dL/dOut
*
* @param x Input Variable
* @param wrt Gradient at the output - dL/dOut. Must have same shape as the input
* @return Output variable
*/
public SDVariable sigmoidDerivative(SDVariable x, SDVariable wrt) {
return sigmoidDerivative(null, x, wrt);
}
/**
* Element-wise sigmoid function derivative: dL/dIn given input and dL/dOut
*
* @param name Output variable name
* @param x Input Variable
* @param wrt Gradient at the output - dL/dOut. Must have same shape as the input
* @return Output variable
*/
public SDVariable sigmoidDerivative(String name, SDVariable x, SDVariable wrt) {
validateFloatingPoint("sigmoidDerivative", x);
SDVariable result = f().sigmoidDerivative(x, wrt);
return updateVariableNameAndReference(result, name);
}
/**
* Softmax activation on dimension 1.
*
* @param x Input variable
* @return Output variable
*/
public SDVariable softmax(SDVariable x) {
return softmax(null, x);
}
/**
* Softmax activation on dimension 1.
*
* @param x Input variable
* @return Output variable
*/
public SDVariable softmax(String name, SDVariable x) {
validateFloatingPoint("softmax", x);
SDVariable result = f().softmax(x);
return updateVariableNameAndReference(result, name);
}
/**
* Softmax activation
*
* @param x Input variable
* @return Output variable
*/
public SDVariable softmax(SDVariable x, int dimension) {
return softmax(null, x, dimension);
}
/**
* Softmax activation
*
* @param x Input variable
* @return Output variable
*/
public SDVariable softmax(String name, SDVariable x, int dimension) {
validateFloatingPoint("softmax", x);
SDVariable result = f().softmax(x, dimension);
return updateVariableNameAndReference(result, name);
}
/**
* @param x
* @return
*/
public SDVariable softmaxDerivative(String name, SDVariable x, SDVariable wrt) {
return softmaxDerivative(name, x, wrt, null);
}
public SDVariable softmaxDerivative(String name, SDVariable x, SDVariable wrt, Integer dimension) {
validateFloatingPoint("softmaxDerivative", x);
SDVariable result = f().softmaxDerivative(x, wrt, dimension);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise softplus function: out = log(exp(x) + 1)
*
* @param x Input variable
* @return Output variable
*/
public SDVariable softplus(SDVariable x) {
return softplus(null, x);
}
/**
* Element-wise softplus function: out = log(exp(x) + 1)
*
* @param name Output variable name
* @param x Input variable
* @return Output variable
*/
public SDVariable softplus(String name, SDVariable x) {
validateFloatingPoint("softplus", x);
SDVariable result = f().softplus(x);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise softsign function: out = x / (abs(x) + 1)
*
* @param x Input variable
* @return Output variable
*/
public SDVariable softsign(SDVariable x) {
return softsign(null, x);
}
/**
* Element-wise softsign function: out = x / (abs(x) + 1)
*
* @param name Output variable name
* @param x Input variable
* @return Output variable
*/
public SDVariable softsign(String name, SDVariable x) {
validateFloatingPoint("softsign", x);
SDVariable result = f().softsign(x);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise derivative (dOut/dIn) of the softsign function {@link #softsign(SDVariable)}
*
* @param x Input variable
* @return Output varible
*/
public SDVariable softsignDerivative(SDVariable x) {
return softsignDerivative(null, x);
}
/**
* Element-wise derivative (dOut/dIn) of the softsign function {@link #softsign(SDVariable)}
*
* @param name Output variable name
* @param x Input variable
* @return Output varible
*/
public SDVariable softsignDerivative(String name, SDVariable x) {
validateFloatingPoint("softsignDerivative", x);
SDVariable result = f().softsignDerivative(x);
return updateVariableNameAndReference(result, name);
}
/**
* Element-wise "swish" function: out = x * sigmoid(b*x) with b=1.0
* See: https://arxiv.org/abs/1710.05941
*
* @param x Input variable
* @return Output variable
*/
public SDVariable swish(SDVariable x) {
return swish(null, x);
}
/**
* Element-wise "swish" function: out = x * sigmoid(b*x) with b=1.0
* See: https://arxiv.org/abs/1710.05941
*
* @param name Name of the output variable
* @param x Input variable
* @return Output variable
*/
public SDVariable swish(String name, SDVariable x) {
validateFloatingPoint("swish", x);
SDVariable ret = f().swish(x);
return updateVariableNameAndReference(ret, name);
}
public SDVariable tanh(String name, SDVariable x) {
return sd.math().tanh(name, x);
}
public SDVariable tanh(SDVariable x) {
return sd.math().tanh(x);
}
/**
* Apply Layer Normalization
*
* y = gain * standardize(x) + bias
*
* @return Output variable
*/
public SDVariable layerNorm(SDVariable input, SDVariable gain, SDVariable bias, boolean channelsFirst, int... dimensions) {
return layerNorm(null, input, gain, bias, channelsFirst, dimensions);
}
/**
* Apply Layer Normalization
*
* y = gain * standardize(x) + bias
*
* @param name Name of the output variable
* @param input Input variable
* @param gain gain
* @param bias bias
* @param channelsFirst For 2D input - unused. True for NCHW (minibatch, channels, height, width), false for NHWC data
* @param dimensions Dimensions to perform layer norm over - dimension=1 for 2d/MLP data, dimension=1,2,3 for CNNs
* @return Output variable
*/
public SDVariable layerNorm(String name, SDVariable input, SDVariable gain, SDVariable bias, boolean channelsFirst, int... dimensions) {
validateFloatingPoint("layerNorm", "input", input);
validateFloatingPoint("layerNorm", "gain", gain);
validateFloatingPoint("layerNorm", "bias", bias);
SDVariable result = f().layerNorm(input, gain, bias, channelsFirst, dimensions);
return updateVariableNameAndReference(result, name);
}
/**
* Apply Layer Normalization without bias
*
* y = gain * standardize(x)
*
* @return Output variable
*/
public SDVariable layerNorm(SDVariable input, SDVariable gain, boolean channelsFirst, int... dimensions) {
return layerNorm((String)null, input, gain, channelsFirst, dimensions);
}
/**
* Apply Layer Normalization
*
* y = gain * standardize(x)
*
* @param name Name of the output variable
* @param input Input variable
* @param gain gain
* @return Output variable
*/
public SDVariable layerNorm(String name, SDVariable input, SDVariable gain, boolean channelsFirst, int... dimensions) {
validateFloatingPoint("layerNorm", "input", input);
validateFloatingPoint("layerNorm", "gain", gain);
SDVariable result = f().layerNorm(input, gain, channelsFirst, dimensions);
return updateVariableNameAndReference(result, name);
}
/**
* See {@link #pad(SDVariable, SDVariable, double)}
*/
public SDVariable pad(SDVariable input, int[][] padding, double constant){
return pad(input, sd.constant(Nd4j.createFromArray(padding)), constant);
}
/**
* Perform padding on the given array, where padded values are the specified constant.
* Example:
* Input array:
* [1, 2]
* [3, 4]
* Padding array:
* [2, 0]
* [1, 1]
* Contant = 0
* Result:
* [0, 0, 0, 0]
* [0, 0, 0, 0]
* [0, 1, 2, 0]
* [0, 3, 4, 0]
*
*
*
* @param input Input array to pad
* @param padding Padding array
* @param constant Constant to use for padded values
* @return Padded array
*/
public SDVariable pad(SDVariable input, SDVariable padding, double constant){
return pad(null, input, padding, Pad.Mode.CONSTANT, constant);
}
/**
* As per {@link #pad(SDVariable, SDVariable, double)} but also supports multiple {@link Pad.Mode} modes.
* Example:
* Input array:
* [1, 2]
* [3, 4]
* [5, 6]
* Padding array:
* [2, 0]
* [1, 1]
* Contant = 0
* Result: CONSTANT mode
* [0, 0, 0, 0]
* [0, 0, 0, 0]
* [0, 1, 2, 0]
* [0, 3, 4, 0]
* [0, 5, 6, 0]
*
* Result: SYMMETRIC mode
* [3, 3, 4, 4]
* [1, 1, 2, 2]
* [1, 1, 2, 2]
* [3, 3, 4, 4]
* [5, 5, 6, 6]
*
* Result: REFLECT:
* [6, 5, 6, 0]
* [2, 3, 4, 3]
* [2, 1, 2, 1]
* [4, 3, 4, 3]
* [6, 5, 6, 5]
*
* @param outputName
* @param input
* @param padding
* @param mode
* @param constant
* @return
*/
public SDVariable pad(String outputName, SDVariable input, SDVariable padding, Pad.Mode mode, double constant){
SDVariable out = f().pad(input, padding, mode, constant);
return updateVariableNameAndReference(out, outputName);
}
/**
* This operation performs dot product attention on the given timeseries input with the given queries
* @see #dotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*/
public SDVariable dotProductAttention(SDVariable queries, SDVariable keys, SDVariable values, SDVariable mask, boolean scaled){
return dotProductAttention(null, queries, keys, values, mask, scaled);
}
/**
* This operation performs dot product attention on the given timeseries input with the given queries
* @see #dotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*/
public SDVariable dotProductAttention(String name, SDVariable queries, SDVariable keys, SDVariable values, SDVariable mask, boolean scaled){
final SDVariable result = f().dotProductAttention(queries, keys, values, mask, scaled);
return updateVariableNameAndReference(result, name);
}
/**
* This operation performs dot product attention on the given timeseries input with the given queries
* @see #dotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*/
public List dotProductAttention(SDVariable queries, SDVariable keys, SDVariable values, SDVariable mask, boolean scaled, boolean withWeights){
return dotProductAttention(null, queries, keys, values, mask, scaled, withWeights);
}
/**
* This operation performs dot product attention on the given timeseries input with the given queries
* out = sum(similarity(k_i, q) * v_i)
*
* similarity(k, q) = softmax(k * q) where x * q is the dot product of x and q
*
* Optionally with normalization step:
* similarity(k, q) = softmax(k * q / sqrt(size(q))
*
* See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, p. 4, eq. 1)
*
* Note: This supports multiple queries at once, if only one query is available the queries vector still has to
* be 3D but can have queryCount = 1
*
* Note: keys and values usually is the same array. If you want to use it as the same array, simply pass it for
* both.
*
* Note: Queries, keys and values must either be all rank 3 or all rank 4 arrays. Mixing them doesn't work. The
* output rank will depend on the input rank.
*
* @param queries input 3D array "queries" of shape [batchSize, featureKeys, queryCount]
* or 4D array of shape [batchSize, numHeads, featureKeys, queryCount]
* @param keys input 3D array "keys" of shape [batchSize, featureKeys, timesteps]
* or 4D array of shape [batchSize, numHeads, featureKeys, timesteps]
* @param values input 3D array "values" of shape [batchSize, featureValues, timesteps]
* or 4D array of shape [batchSize, numHeads, featureValues, timesteps]
* @param mask OPTIONAL; array that defines which values should be skipped of shape [batchSize, timesteps]
* @param scaled normalization, false -> do not apply normalization, true -> apply normalization
* @param withWeights return attention weights as well, false -> only one output, true -> two outputs
*
* Output Arrays:
* @return [ Attention result arrays of shape [batchSize, featureValues, queryCount] or [batchSize, numHeads, featureValues, queryCount],
* (optionally) Attention Weights of shape [batchSize, timesteps, queryCount] or [batchSize, numHeads, timesteps, queryCount]]
*/
public List dotProductAttention(String name, SDVariable queries, SDVariable keys, SDVariable values, SDVariable mask, boolean scaled, boolean withWeights){
List result = f().dotProductAttention(queries, keys, values, mask, scaled, withWeights);
if(withWeights){
return Collections.singletonList(updateVariableNameAndReference(result.get(0), name));
}else{
return Arrays.asList(
updateVariableNameAndReference(result.get(0), name),
updateVariableNameAndReference(result.get(1), name+":weights")
);
}
}
/**
* This performs multi-headed dot product attention on the given timeseries input
* @see #multiHeadDotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*/
public SDVariable multiHeadDotProductAttention(SDVariable queries, SDVariable keys, SDVariable values, SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo, SDVariable mask, boolean scaled){
return multiHeadDotProductAttention(null, queries, keys, values, Wq, Wk, Wv, Wo, mask, scaled);
}
/**
* This performs multi-headed dot product attention on the given timeseries input
* @see #multiHeadDotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*/
public SDVariable multiHeadDotProductAttention(String name, SDVariable queries, SDVariable keys, SDVariable values, SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo, SDVariable mask, boolean scaled){
final SDVariable result = f().multiHeadDotProductAttention(queries, keys, values, Wq, Wk, Wv, Wo, mask, scaled);
return updateVariableNameAndReference(result, name);
}
/**
* This performs multi-headed dot product attention on the given timeseries input
* @see #multiHeadDotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*/
public List multiHeadDotProductAttention(SDVariable queries, SDVariable keys, SDVariable values, SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo, SDVariable mask, boolean scaled, boolean withWeights){
return multiHeadDotProductAttention(null, queries, keys, values, Wq, Wk, Wv, Wo, mask, scaled, withWeights);
}
/**
* This performs multi-headed dot product attention on the given timeseries input
* out = concat(head_1, head_2, ..., head_n) * Wo
* head_i = dot_product_attention(Wq_i*q, Wk_i*k, Wv_i*v)
*
* Optionally with normalization when calculating the attention for each head.
*
* See also "Attention is all you need" (https://arxiv.org/abs/1706.03762, pp. 4,5, "3.2.2 Multi-Head Attention")
*
* This makes use of dot_product_attention OP support for rank 4 inputs.
* @see #dotProductAttention(String, SDVariable, SDVariable, SDVariable, SDVariable, boolean, boolean)
*
* @param queries input 3D array "queries" of shape [batchSize, featureKeys, queryCount]
* @param keys input 3D array "keys" of shape [batchSize, featureKeys, timesteps]
* @param values input 3D array "values" of shape [batchSize, featureValues, timesteps]
* @param Wq input query projection weights of shape [numHeads, projectedKeys, featureKeys]
* @param Wk input key projection weights of shape [numHeads, projectedKeys, featureKeys]
* @param Wv: input value projection weights of shape [numHeads, projectedValues, featureValues]
* @param Wo: output projection weights of shape [numHeads * projectedValues, outSize]
* @param mask OPTIONAL; array that defines which values should be skipped of shape [batchSize, timesteps]
* @param scaled normalization, false -> do not apply normalization, true -> apply normalization
* @param withWeights return attention weights as well, false -> only one output, true -> two outputs
*
* Output Arrays:
* @return [ Attention result arrays of shape [batchSize, outSize, queryCount]
* (optionally) Attention Weights of shape [batchSize, numHeads, timesteps, queryCount]
*/
public List multiHeadDotProductAttention(String name, SDVariable queries, SDVariable keys, SDVariable values, SDVariable Wq, SDVariable Wk, SDVariable Wv, SDVariable Wo, SDVariable mask, boolean scaled, boolean withWeights){
List result = f().multiHeadDotProductAttention(queries, keys, values, Wq, Wk, Wv, Wo, mask, scaled, withWeights);
if(withWeights){
return Collections.singletonList(updateVariableNameAndReference(result.get(0), name));
}else{
return Arrays.asList(
updateVariableNameAndReference(result.get(0), name),
updateVariableNameAndReference(result.get(1), name+":weights")
);
}
}
/**
* Max pooling on the input and outputs both max values and indices
*
* @param name Name of the output variable
* @param x input array
* @return output array and argmax array
*/
public SDVariable[] maxPoolWithArgmax(String[] names, SDVariable x, Pooling2DConfig pooling2DConfig) {
SDVariable[] res = f().maxPoolWithArgmaxs(x, pooling2DConfig);
return sd.updateVariableNamesAndReferences(res, names);
}
/**
* Batch normalization
*
* @param name Name of the output variable
* @param x 4D array
* @param scale vector for scaling factor of normalized x
* @param offset vector to shift to the normalized x
* @param dataFormat integer scalar - data format
* @param isTraining boolean scalar - is training mode
* @return y: 4D array
* batch_mean: vector
* batch_var: vector
*/
public SDVariable[] fusedBatchNorm(String[] names, SDVariable x, SDVariable scale, SDVariable offset,
SDVariable dataFormat, SDVariable isTraining) {
SDVariable[] res = f().fusedBatchNorm(x,scale,offset,dataFormat,isTraining);
return sd.updateVariableNamesAndReferences(res, names);
}
}