lib/ccv_convnet.c

ccv_convnet_new

ccv_convnet_t* ccv_convnet_new(int use_cwc_accel, ccv_size_t input, ccv_convnet_layer_param_t params[], int count);

Create a new (deep) convolutional network with specified parameters. ccv only supports convolutional layer (shared weights), max pooling layer, average pooling layer, full connect layer and local response normalization layer.

• use_cwc_accel : whether use CUDA-enabled GPU to accelerate various computations for convolutional network.
• input : the input size of the image, it is not necessarily the input size of the first convolutional layer.
• params[]: the C-array of ccv_convnet_layer_param_t that specifies the parameters for each layer.
• count the size of params[] C-array.

ccv_convnet_layer_param_t

• type : one of following value to specify the network layer type, CCV_CONVNET_CONVOLUTIONAL , CCV_CONVNET_FULL_CONNECT , CCV_CONVNET_MAX_POOL , CCV_CONVNET_AVERAGE_POOL , CCV_CONVNET_LOCAL_RESPONSE_NORM.
• bias : the initialization value for bias if applicable (for convolutional layer and full connect layer).
• sigma : the normal distribution variance for weights if applicable (for convolutional layer and full connect layer).
• input : a ccv_convnet_input_t specifies the input structure.
• output : a ccv_convnet_type_t specifies the output parameters and structure.

ccv_convnet_input_t

• matrix.rows : the number of rows of the input matrix.
• matrix.cols : the number of columns of the input matrix.
• matrix.channels : the number of channels of the input matrix.
• matrix.partition : the number of partitions of the input matrix, it must be dividable by the number of channels (it is partitioned by channels).
• node.count : the number of nodes. You should either use node or matrix to specify the input structure.

ccv_convnet_type_t

• convolutional.count : the number of filters for convolutional layer.
• convolutional.strides : the strides for convolutional filter.
• convolutional.border : the padding border size for the input matrix.
• convolutional.rows : the number of rows for convolutional filter.
• convolutional.cols : the number of columns for convolutional filter.
• convolutional.channels : the number of channels for convolutional filter.
• convolutional.partition : the number of partitions for convolutional filter.
• pool.strides : the strides for pooling layer.
• pool.size : the size for pooling layer.
• pool.border : the padding border size for the input matrix.
• rnorm.size : the size of local response normalization layer.
• rnorm.kappa : as of b[i] = a[i] / (rnorm.kappa + rnorm.alpha * sum(a, i - rnorm.size / 2, i + rnorm.size / 2)) ^ rnorm.beta
• rnorm.alpha : see rnorm.kappa.
• rnorm.beta : see rnorm.kappa.
• full_connect.count : the number of output nodes for full connect layer.

ccv_convnet_verify

int ccv_convnet_verify(ccv_convnet_t* convnet, int output);

Verify the specified parameters make sense as a deep convolutional network. Return 0 if the given deep convolutional network making sense.

• convnet : A deep convolutional network to verify.
• output : The output number of nodes (for the last full connect layer).

ccv_convnet_supervised_train

void ccv_convnet_supervised_train(ccv_convnet_t* convnet, ccv_array_t* categorizeds, ccv_array_t* tests, const char* filename, ccv_convnet_train_param_t params);

Start to train a deep convolutional network with given parameters and data.

• convnet : A deep convolutional network that is initialized.
• categorizeds : An array of images with its category information for training.
• tests : An array of images with its category information for validating.
• filename : The working file to save progress and the trained convolutional network.
• params : The training parameters.

ccv_convnet_train_param_t

• max_epoch : The number of epoch (an epoch sweeps through all the examples) to go through before end the training.
• mini_batch : The number of examples for a batch in stochastic gradient descent.
• iterations : The number of iterations (an iteration is for one batch) before save the progress.
• symmetric : Whether to exploit the symmetric property of the provided examples.
• color_gain : The color variance for data augmentation (0 means no such augmentation).
• layer_params : An C-array of ccv_convnet_layer_train_param_t training parameters for each layer.

ccv_convnet_layer_train_param_t

• dor : The dropout rate for this layer, it is only applicable for full connect layer.
• w : A ccv_convnet_layer_sgd_param_t specifies the stochastic gradient descent update rule for weight, it is only applicable for full connect layer and convolutional layer.
• bias : A ccv_convnet_layer_sgd_param_t specifies the stochastic gradient descent update rule for bias, it is only applicable for full connect layer and convolutional layerweight.

ccv_convnet_layer_sgd_param_t

• learn_rate : new velocity = momentum * old velocity - decay * learn_rate * old value + learn_rate * delta, new value = old value + new velocity
• decay : see learn_rate.
• momentum : see learn_rate.

ccv_convnet_encode

void ccv_convnet_encode(ccv_convnet_t* convnet, ccv_dense_matrix_t **a, ccv_dense_matrix_t** b, int batch);

Use a convolutional network to encode an image into a compact representation.

• convnet : The given convolutional network.
• a : A C-array of input images.
• b : A C-array of output matrix of compact representation.
• batch : The number of input images.

ccv_convnet_classify

void ccv_convnet_classify(ccv_convnet_t* convnet, ccv_dense_matrix_t **a, int symmetric, ccv_array_t** ranks, int tops, int batch);

Use a convolutional network to classify an image into categories.

• convnet : The given convolutional network.
• a : A C-array of input images.
• symmetric : Whether the input is symmetric.
• ranks : A C-array of ccv_array_t contains top categories by the convolutional network.
• tops : The number of top categories return for each image.
• batch : The number of input images.

ccv_convnet_read

ccv_convnet_t* ccv_convnet_read(int use_cwc_accel, const char* filename);

Read a convolutional network that persisted on the disk.

• use_cwc_accel : Use CUDA-enabled GPU acceleration.
• filename : The file on the disk.

ccv_convnet_write

void ccv_convnet_write(ccv_convnet_t* convnet, const char* filename, ccv_convnet_write_param_t params);

Write a convolutional network to a disk.

• convnet : A given convolutional network.
• filename : The file on the disk.
• params : a ccv_convnet_write_param_t to specify the write parameters.

ccv_convnet_write_param_t

• half_precision : Use half precision float point to represent network parameters.

ccv_convnet_compact

void ccv_convnet_compact(ccv_convnet_t* convnet);

Free up temporary resources of a given convolutional network.

• convnet : A convolutional network.

ccv_convnet_free

void ccv_convnet_free(ccv_convnet_t* convnet);

Free up the memory of a given convolutional network.

• convnet : A convolutional network.

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