Feature/neural nets factory and cookbook

doc/cookbook/source/examples/neural_nets/convolutional_net_classification.rst

@@ -0,0 +1,48 @@
+===============================================
+Convolutional Neural Network for Classification
+===============================================
+
+Convolutional neural network  is a class of deep, feed-forward artificial neural networks, most commonly applied to analyzing visual imagery. 
+The network is a directed acyclic graph composed of an input layer, a few hidden layers can be convolutional layers, pooling layers, fully connected layers or normalization layers and a softmax output layer.
+To compute the pre-nonlinearity input to some unit :math:`x_{ij}^{l}` in any layer, we can sum up the contributions from the previous layer cells:
+
+.. math::
+
+    x_{ij}^{l}=\sum_{a=0}^{m-1}\sum_{b=0}^{m-1}w_{ab}y_{(i+1)(j+1)}^{l-1}
+
+where :math:`x_{ij}^{l}` is the input to a neuron in layer :math:`l`, :math:`y_{ij}^{l}` is output of the neuron and :math:`w_{ij}` is it's weight.
+See chapter 9 in :cite:`Goodfellow-et-al-2016-Book` for a detailed introduction.
+
+-------
+Example
+-------
+
+Imagine we have files with training and test data. We create CDenseFeatures (here 64 bit floats aka RealFeatures) and :sgclass:`CMulticlassLabels` as
+
+.. sgexample:: convolutional_net_classification.sg:create_features
+
+We create a :sgclass:`CNeuralNetwork` instance and randomly initialize the network parameters by sampling from a gaussian distribution. We also set appropriate parameters like regularization coefficient, dropout probabilities, learning rate, etc. as shown below. More parameters can be found in the documentation of :sgclass:`CNeuralNetwork`.
+
+.. sgexample:: convolutional_net_classification.sg:set_parameters
+
+.. sgexample:: convolutional_net_classification.sg:create_instance
+
+We create instances of :sgclass:`CNeuralInputLayer`, :sgclass:`CNeuralConvolutionalLayer` and :sgclass:`NeuralSoftmaxLayer` which are building blocks of :sgclass:`CNeuralNetwork`
+
+.. sgexample:: convolutional_net_classification.sg:add_layers
+
+We train the model and apply it to some test data.
+
+.. sgexample:: convolutional_net_classification.sg:train_and_apply
+
+Finally, we compute accuracy.
+
+.. sgexample:: convolutional_net_classification.sg:evaluate_accuracy
+
+----------
+References
+----------
+:wiki:`Convolutional_neural_network`
+
+.. bibliography:: ../../references.bib
+    :filter: docname in docnames

doc/cookbook/source/examples/neural_nets/feedforward_net_classification.rst

@@ -22,17 +22,14 @@ Imagine we have files with training and test data. We create CDenseFeatures (her
 
 .. sgexample:: feedforward_net_classification.sg:create_features
 
-We create instances of :sgclass:`CNeuralInputLayer`, :sgclass:`CNeuralLinearLayer` and :sgclass:`NeuralSoftmaxLayer` which are building blocks of :sgclass:`CNeuralNetwork`
-
-.. sgexample:: feedforward_net_classification.sg:add_layers
+We create a :sgclass:`CNeuralNetwork` instance by using the above layers and randomly initialize the network parameters by sampling from a gaussian distribution.We set appropriate parameters like regularization coefficient, dropout probabilities, learning rate, etc. as shown below. More parameters can be found in the documentation of :sgclass:`CNeuralNetwork`.
 
-We create a :sgclass:`CNeuralNetwork` instance by using the above layers and randomly initialize the network parameters by sampling from a gaussian distribution.
 
 .. sgexample:: feedforward_net_classification.sg:create_instance
 
-Before training, we need to set appropriate parameters like regularization coefficient, dropout probabilities, learning rate, etc. as shown below. More parameters can be found in the documentation of :sgclass:`CNeuralNetwork`.
+We create instances of :sgclass:`CNeuralInputLayer`, :sgclass:`CNeuralLinearLayer` and :sgclass:`NeuralSoftmaxLayer` which are building blocks of :sgclass:`CNeuralNetwork`
 
-.. sgexample:: feedforward_net_classification.sg:set_parameters
+.. sgexample:: feedforward_net_classification.sg:add_layers
 
 We train the model and apply it to some test data.
 

doc/cookbook/source/examples/neural_nets/feedforward_net_regression.rst

@@ -12,17 +12,13 @@ Imagine we have files with training and test data. We create CDenseFeatures (her
 
 .. sgexample:: feedforward_net_regression.sg:create_features
 
-We create instances of :sgclass:`CNeuralLayers` and add an input layer, hidden layer and output layer which are building blocks of :sgclass:`CNeuralNetwork`
-
-.. sgexample:: feedforward_net_regression.sg:add_layers
-
-We create a :sgclass:`CNeuralNetwork` instance by using the above layers and randomly initialize the network parameters by sampling from a gaussian distribution.
+We create a :sgclass:`CNeuralNetwork` instance by using the above layers and randomly initialize the network parameters by sampling from a gaussian distribution. We set appropriate parameters like regularization coefficient, number of epochs, learning rate, etc. as shown below. More parameters can be found in the documentation of :sgclass:`CNeuralNetwork`.
 
 .. sgexample:: feedforward_net_regression.sg:create_instance
 
-Before training, we need to set appropriate parameters like regularization coefficient, number of epochs, learning rate, etc. as shown below. More parameters can be found in the documentation of :sgclass:`CNeuralNetwork`.
+We create instances of :sgclass:`CNeuralLayers` and add an input layer, hidden layer and output layer which are building blocks of :sgclass:`CNeuralNetwork`
 
-.. sgexample:: feedforward_net_regression.sg:set_parameters
+.. sgexample:: feedforward_net_regression.sg:add_layers
 
 We train the model and apply it to test data.
 

examples/meta/src/neural_nets/convolutional_net_classification.sg

@@ -0,0 +1,41 @@
+File f_feats_train = csv_file("../../data/mnist_3class_256d_features_train.dat")
+File f_feats_test = csv_file("../../data/mnist_3class_256d_features_test.dat")
+File f_labels_train = csv_file("../../data/mnist_3class_256d_labels_train.dat")
+File f_labels_test = csv_file("../../data/mnist_3class_256d_labels_test.dat")
+
+Math:init_random(10)
+
+#![create_features]
+Features features_train = features(f_feats_train)
+Features features_test = features(f_feats_test)
+Labels labels_train = labels(f_labels_train)
+Labels labels_test = labels(f_labels_test)
+#![create_features]
+
+#![create_instance]
+Machine network = machine("NeuralNetwork", labels=labels_train, auto_quick_initialize=True, max_num_epochs=4, epsilon=0.0, optimization_method=enum ENNOptimizationMethod.NNOM_GRADIENT_DESCENT, gd_learning_rate=0.01, gd_mini_batch_size=3, max_norm=1.0, dropout_input=0.5)
+#![create_instance]
+
+#![add_layers]
+NeuralLayer input = layer("NeuralInputLayer", width=16, height=16, num_neurons=256) 
+network.add("layers", input)
+NeuralLayer conv1 = layer("NeuralConvolutionalLayer", num_maps=3, radius_x=2, radius_y=2, pooling_width=2, pooling_height=2, stride_x=1, stride_y=1)
+network.add("layers", conv1)
+NeuralLayer conv2 = layer("NeuralConvolutionalLayer", num_maps=3, radius_x=2, radius_y=2, pooling_width=2, pooling_height=2, stride_x=1, stride_y=1)
+network.add("layers", conv2)
+NeuralLayer softmax = layer("NeuralSoftmaxLayer", num_neurons=3)
+network.add("layers", softmax)
+#![add_layers]
+
+#![train_and_apply]
+network.train(features_train)
+Labels labels_predict = network.apply(features_test)
+#![train_and_apply]
+
+#![evaluate_accuracy]
+Evaluation eval = evaluation("MulticlassAccuracy")
+real accuracy = eval.evaluate(labels_predict, labels_test)
+#![evaluate_accuracy]
+
+# additional integration testing variables
+RealVector output = labels_predict.get_real_vector("labels")

examples/meta/src/neural_nets/feedforward_net_classification.sg

@@ -1,55 +1,44 @@
-CSVFile f_feats_train("../../data/classifier_binary_2d_nonlinear_features_train.dat")
-CSVFile f_feats_test("../../data/classifier_binary_2d_nonlinear_features_test.dat")
-CSVFile f_labels_train("../../data/classifier_binary_2d_nonlinear_labels_train.dat")
-CSVFile f_labels_test("../../data/classifier_binary_2d_nonlinear_labels_test.dat")
+File f_feats_train = csv_file("../../data/classifier_binary_2d_nonlinear_features_train.dat")
+File f_feats_test = csv_file("../../data/classifier_binary_2d_nonlinear_features_test.dat")
+File f_labels_train = csv_file("../../data/classifier_binary_2d_nonlinear_labels_train.dat")
+File f_labels_test = csv_file("../../data/classifier_binary_2d_nonlinear_labels_test.dat")
 
 Math:init_random(1)
 
 #![create_features]
 Features features_train = features(f_feats_train)
 Features features_test = features(f_feats_test)
-BinaryLabels labels_train(f_labels_train)
-BinaryLabels labels_test(f_labels_test)
+Labels labels_train = labels(f_labels_train)
+Labels labels_test = labels(f_labels_test)
 #![create_features]
 
-#![add_layers]
-int num_feats = features_train.get_int("num_features")
-NeuralLayers layers() 
-layers.input(num_feats)
-layers.rectified_linear(10)
-layers.softmax(2)
-DynamicObjectArray all_layers = layers.done()
-#![add_layers]
-
 #![create_instance]
-NeuralNetwork network(all_layers)
-network.quick_connect()
-network.initialize_neural_network()
+int num_feats = features_train.get_int("num_features")
+Machine network = machine("NeuralNetwork", labels=labels_train, auto_quick_initialize=True, l2_coefficient=0.01, dropout_hidden=0.5, max_num_epochs=50, gd_mini_batch_size=num_feats, gd_learning_rate=0.1, gd_momentum=0.9)
 #![create_instance]
 
-#![set_parameters]
-network.set_l2_coefficient(0.01)
-network.set_dropout_hidden(0.5)
-network.set_max_num_epochs(50)
-network.set_gd_mini_batch_size(num_feats)
-network.set_gd_learning_rate(0.1)
-network.set_gd_momentum(0.9)
-#![set_parameters]
+#![add_layers]
+NeuralLayer input = layer("NeuralInputLayer", num_neurons=num_feats)
+network.add("layers", input)
+NeuralLayer relu = layer("NeuralRectifiedLinearLayer", num_neurons=10)
+network.add("layers", relu)
+NeuralLayer softmax = layer("NeuralSoftmaxLayer", num_neurons=2)
+network.add("layers", softmax)
+#![add_layers]
 
 #![train_and_apply]
-network.set_labels(labels_train)
 network.train(features_train)
-BinaryLabels labels_predict = network.apply_binary(features_test)
+Labels labels_predict = network.apply(features_test)
 #![train_and_apply]
 
 #![get_params]
-RealVector parameters = network.get_parameters()
+RealVector parameters = network.get_real_vector("params")
 #![get_params]
 
 #![evaluate_accuracy]
-AccuracyMeasure am()
-real accuracy = am.evaluate(labels_predict, labels_test)
+Evaluation eval = evaluation("AccuracyMeasure")
+real accuracy = eval.evaluate(labels_predict, labels_test)
 #![evaluate_accuracy]
 
 # additional integration testing variables
-RealVector output = labels_predict.get_labels()
+RealVector output = labels_predict.get_real_vector("labels")

examples/meta/src/neural_nets/feedforward_net_regression.sg

@@ -1,54 +1,44 @@
-CSVFile f_feats_train("../../data/regression_1d_sinc_features_train_with_9d_noise.dat")
-CSVFile f_feats_test("../../data/regression_1d_sinc_features_test_with_9d_noise.dat")
-CSVFile f_labels_train("../../data/regression_1d_sinc_labels_train.dat")
-CSVFile f_labels_test("../../data/regression_1d_sinc_labels_test.dat")
+File f_feats_train = csv_file("../../data/regression_1d_sinc_features_train_with_9d_noise.dat")
+File f_feats_test = csv_file("../../data/regression_1d_sinc_features_test_with_9d_noise.dat")
+File f_labels_train = csv_file("../../data/regression_1d_sinc_labels_train.dat")
+File f_labels_test = csv_file("../../data/regression_1d_sinc_labels_test.dat")
 
 Math:init_random(1)
 
 #![create_features]
 Features features_train = features(f_feats_train)
 Features features_test = features(f_feats_test)
-RegressionLabels labels_train(f_labels_train)
-RegressionLabels labels_test(f_labels_test)
+Labels labels_train = labels(f_labels_train)
+Labels labels_test = labels(f_labels_test)
 #![create_features]
 
-#![add_layers]
-int dimensions = features_train.get_int("num_features")
-NeuralLayers layers() 
-layers.input(dimensions)
-layers.rectified_linear(20)
-layers.linear(1)
-DynamicObjectArray all_layers = layers.done()
-#![add_layers]
-
 #![create_instance]
-NeuralNetwork network(all_layers)
-network.quick_connect()
-network.initialize_neural_network()
+int num_feats = features_train.get_int("num_features")
+Machine network = machine("NeuralNetwork", labels=labels_train, auto_quick_initialize=True, l2_coefficient=0.1, epsilon=0.0, max_num_epochs=40, gd_learning_rate=0.1, gd_momentum=0.9)
 #![create_instance]
 
-#![set_parameters]
-network.set_l2_coefficient(0.1)
-network.set_max_num_epochs(40)
-network.set_epsilon(0.0)
-network.set_gd_learning_rate(0.1)
-network.set_gd_momentum(0.9)
-#![set_parameters]
+#![add_layers]
+NeuralLayer input = layer("NeuralInputLayer", num_neurons=num_feats)
+network.add("layers", input)
+NeuralLayer relu = layer("NeuralRectifiedLinearLayer", num_neurons=20)
+network.add("layers", relu)
+NeuralLayer linear = layer("NeuralLinearLayer", num_neurons=1)
+network.add("layers", linear)
+#![add_layers]
 
 #![train_and_apply]
-network.set_labels(labels_train)
 network.train(features_train)
-RegressionLabels labels_predict = network.apply_regression(features_test)
+Labels labels_predict = network.apply(features_test)
 #![train_and_apply]
 
 #![get_params]
-RealVector parameters = network.get_parameters()
+RealVector parameters = network.get_real_vector("params")
 #![get_params]
 
-#![evaluate_error]
-MeanSquaredError err()
-real mse = err.evaluate(labels_predict, labels_test)
-#![evaluate_error]
+#![evaluate_accuracy]
+Evaluation eval = evaluation("MeanSquaredError")
+real mse = eval.evaluate(labels_predict, labels_test)
+#![evaluate_accuracy]
 
 # additional integration testing variables
-RealVector output = labels_predict.get_labels()
+RealVector output = labels_predict.get_real_vector("labels")

src/interfaces/swig/shogun.i

@@ -256,6 +256,7 @@ PUT_ADD(CECOCDecoder)
 PUT_ADD(CMulticlassStrategy)
 PUT_ADD(CCombinationRule)
 PUT_ADD(CDifferentiableFunction)
+PUT_ADD(CNeuralLayer)
 
 %template(kernel) kernel<float64_t, float64_t>;
 %template(features) features<float64_t>;

src/shogun/base/base_types.h

@@ -19,7 +19,7 @@ namespace shogun
 	class CECOCEncoder;
 	class CECOCDecoder;
 	class CMulticlassStrategy;
-
+	class CNeuralLayer;
 	// type trait to enable certain methods only for shogun base types
 	template <class T>
 	struct is_sg_base
@@ -31,7 +31,8 @@ namespace shogun
 	                    std::is_same<CLabels, T>::value ||
 	                    std::is_same<CECOCEncoder, T>::value ||
 	                    std::is_same<CECOCDecoder, T>::value ||
-	                    std::is_same<CMulticlassStrategy, T>::value>
+	                    std::is_same<CMulticlassStrategy, T>::value ||
+	                    std::is_same<CNeuralLayer, T>::value>
 	{
 	};
 }