matlab codes of a convolutional neural network

@myCNN
@myCNN\accumulate_ddeltas.m
@myCNN\adapt.m
@myCNN\adapt_deltas.m
@myCNN\adapt_LM.m
@myCNN\adapt_net.m
@myCNN\add_layer.m
@myCNN\average_ddeltas.m
@myCNN\backbackpropagate.m
@myCNN\backbackpropagate_C_layer.m
@myCNN\backbackpropagate_F_layer.m
@myCNN\backbackpropagate_M_layer.m
@myCNN\backbackpropagate_S_layer.m
@myCNN\backpropagate.m
@myCNN\backpropagate_C_layer.m
@myCNN\backpropagate_F_layer.m
@myCNN\backpropagate_M_layer.m
@myCNN\backpropagate_S_layer.m
@myCNN\compute_learning_rates.m
@myCNN\display.m
@myCNN\forget_ddeltas.m
@myCNN\forget_deltas.m
@myCNN\forget_derivatives.m
@myCNN\forget_second_derivatives.m
@myCNN\get_diag_Hessian.m
@myCNN\get_gradient.m
@myCNN\get_performance.m
@myCNN\get_trainable_parameters.m
@myCNN\init_net.m
@myCNN\load_lenet_from_lush_data.m
@myCNN\myCNN.m
@myCNN\private
@myCNN\private\create_lenet_structure_from_lush_data.m
@myCNN\private\draw_plots.m
@myCNN\private\dsquash.m
@myCNN\private\get_data_dir_on_host.m
@myCNN\private\get_projects_dir_on_host.m
@myCNN\private\host.m
@myCNN\private\log_it.m
@myCNN\private\oversample2.m
@myCNN\private\prepare_lenet_test_set.m
@myCNN\private\read_idx_data.m
@myCNN\private\read_lush_array.m
@myCNN\private\soft_max.m
@myCNN\private\squash.m
@myCNN\private\squash_and_dsquash.m
@myCNN\private\subsample2.m
@myCNN\private\unfold.m
@myCNN\private\unfold2.m
@myCNN\propagate.m
@myCNN\propagate_C_layer.m
@myCNN\propagate_F_layer.m
@myCNN\propagate_M_layer.m
@myCNN\propagate_one_sample.m
@myCNN\propagate_S_layer.m
@myCNN\set_FM.m
@myCNN\set_global_learning_rate.m
@myCNN\set_momentum.m
@myCNN\sim.m
@myCNN\subsref.m
@myCNN\tag2ind.m
@myCNN\train_LM.m
@myCNN\update_stat.m
@single
@single\qdsquash.m
@single\qdsquash_from_squash.m
@single\qsquash.m
@single\qsquash_and_dsquash.m
@single\qtanh.m
ChangeLog
contents.m
demo_myCNN.m
example_LeNet5_SDLM_training.m
example_myLeNet5_SDLM_training.m
example_nonscalar_output_CNN.m
get_MNIST_data.m
mnist2matlab.m
newLeNet5.m
newMyLeNet5.m
myLeNet5-example.mat

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