v1.1.0

• Squared Hinge Loss error function added
• Local contrast subtractive layer hessian and updater implementations added both to CPU and GPU backends
• Maxout layer added with CPU and GPU backends implemented
• Added tester functionality for rgb_to_you_convert layer in CUDA backend
• Learning rate decay functionality for tail iterations is added
• Fixed:
  • Functionality bug in L2 incoming weights regularizer
  • Functionality bug for rectangular local contrast subtractive
  • Recovered snapshot_invalid functionality

v1.0.7

• supervised_data_reader now naturaly inherits unsupervised_data_reader: code is simplified
• supervised_transformed_output_data_reader and unsupervised_transformed_input_data_reader added
• Stats for readers (max, min, avg, std_dev) implemented
• normalize_data_transformer added
• Regression output type added
• Convolutional 3D layer implemented in CUDA backend
• Max subsampling 3D layer implemented in CUDA backend

v1.0.6

• Dropout support is extended to all layers
• Data transformers simplified; removed deterministic mode of noise
• Added sanity check for mse in order to drop ANNs with broken weights during training
• Fixed plain (CPU) backend for rectangular convolutional and subsampling layers
• CUDA exceptions now go with filename and line number
• Minor fixes and improvements

v1.0.5

• Regularization "Upper bound on L2 norm of the incoming weight vector for each output neuron" added
• ROC-type result now works fine for multi-class output types
• rotate_band data and noise_data_transformer transformers added
• Dropout is now done per input neuron instead of per input feature map - more robust option
• Minor fixes

v1.0.4

• Rectified linear, soft rectified linear and softmax layes with CPU and GPU backends implemented
• On the fly distortion
• ann_snapshot command (weights visualization)
• Minor improvements and bug-fixes

v1.0.3

• Ability to validate and test with multiple samples per entry (averaging results)
• Max Subsampling layer in CUDA backend (2D only)
• Flipping image option added to the toolset
• Additional constructor with fixed seed for random generator
• preparing_data command split into preparing_training_data and preparing_testing_data
• A couple of minor bug-fixes

v1.0.2

Performance tuning for Kepler GK110

v1.0.1

Full support for data with input neurons having 'float' data type

v1.0.0

Initial version