+++ /dev/null
-# Embedding
-
-This application contains embedding layer example with two example model.
-One with single input, another assuming there are two inputs(using split layer)
-
-## Example Model Structure
-
-There are two structure ready for the example
-
-1. Linear Embedding structure
- (Input: 10:1:1:4 )
- (Embedding: 10:1:4:8 )
- (Flatten: 10:1:1:32)
- (FullyConnected: 10:1:1:1 )
-
-2. Splitted data and seperate embedding structure
- (Input: 10:1:2:2 )
- (split: 10:1:1:1, 10:1:1:1 )
- (Embedding1: 10:1:1:8 ), (Embedding2: 10:1:1:8 )
- (concat: 10:2:1:8 ),
- (Flatten: 10:1:1:16),
- (FullyConnected: 10:1:1:1 )
-
-## How to run a train epoch
-
-Once you compile, with `meson`, please file an issue if you have a problem running the example.
-
-```bash
-export ${res}
-$ cd ${build_dir}
-$ meson test app_embedding -v #this is for the first model structure
-$ meson test app_embedding_split -v #this is for the second model structure
-```
-
-Expected output is as below...
-
-```bash
-#1/100 - Training Loss: 0.692463 >> [ Accuracy: 100% - Validation Loss : 0.690833 ]
-...
-#100/100 - Training Loss: 0.535373 >> [ Accuracy: 100% - Validation Loss : 0.53367 ]
-```
+++ /dev/null
-res_path = meson.current_source_dir() / '..' / 'res'
-run_command('cp', '-lr', res_path, nntr_app_resdir / 'Embedding')
-
-e = executable('nntrainer_embedding',
- 'main.cpp',
- dependencies: [iniparser_dep, nntrainer_ccapi_dep, gtest_dep, nntrainer_dep],
- install: get_option('install-app'),
- install_dir: application_install_dir
-)
-
-test('app_embedding', e, args: ['train',
- nntr_app_resdir / 'Embedding' / 'Embedding.ini',
- nntr_app_resdir / 'Embedding' / 'embedding_input.txt']
-)
-
-# test split example
-test('app_embedding_split', e, args: ['train',
- nntr_app_resdir / 'Embedding' / 'Embedding_split.ini',
- nntr_app_resdir / 'Embedding' / 'embedding_input.txt']
-)
+++ /dev/null
-# Model Section : Model
-[Model]
-Type = NeuralNetwork
-Epochs = 100 # Epochs
-Loss = cross # Loss function : mse (mean squared error)
- # cross ( cross entropy )
-Save_Path = "embedding_model_split.bin" # model path to save / read
-batch_size = 10 # batch size
-
-[Optimizer]
-Type = adam
-beta1 = 0.9
-beta2 = 0.999
-Learning_rate = 0.001
-epsilon = 1e-7
-
-[embedding]
-Type = embedding
-in_dim = 15
-out_dim = 8
-in_length = 4
-
-[flatten]
-Type = flatten
-input_layers = embedding
-
-[outputlayer]
-Type = fully_connected
-input_layers = flatten
-Unit = 1
-Bias_initializer = zeros
-Activation = sigmoid
+++ /dev/null
-# Model Section : Model
-[Model]
-Type = NeuralNetwork
-Epochs = 100 # Epochs
-Loss = cross # Loss function : mse (mean squared error)
- # cross ( cross entropy )
-Save_Path = "embedding_model_split.bin" # model path to save / read
-batch_size = 10 # batch size
-
-[Optimizer]
-Type = adam
-beta1 = 0.9
-beta2 = 0.999
-Learning_rate = 0.001
-epsilon = 1e-7
-
-[input]
-Type = input
-input_shape = 1:2:2 # channel:height:width
-
-[split]
-Type = split
-split_dimension = 2 # split by height
-
-[embedding1]
-input_layers = split
-Type = embedding
-in_dim = 15
-out_dim = 8
-in_length = 1
-
-[embedding2]
-input_layers = split
-Type = embedding
-in_dim = 15
-out_dim = 8
-in_length = 1
-
-[concat]
-input_layers = embedding1, embedding2
-Type = concat
-
-[flatten]
-Type = flatten
-
-[outputlayer]
-Type = fully_connected
-input_layers = flatten
-Unit = 1
-Bias_initializer = zeros
-Activation = sigmoid
+++ /dev/null
-6 2 0 0 1
-3 1 0 0 1
-7 4 0 0 1
-8 1 0 0 1
-9 0 0 0 1
-10 0 0 0 0
-5 4 0 0 0
-11 3 0 0 0
-5 1 0 0 0
-12 13 2 14 0
--- /dev/null
+# Product Ratings
+
+This application contains a simple embedding-based model that predicts ratings given a user and a product.
+
+## The Model Structure
+
+ (Input: 1:2:2 ) # user_id, product_id
+ (split: 1:1:1, 1:1:1 )
+ (user_embed: 1:1:5 ), (product_embed: 1:1:5 )
+ (concat: 2:1:5 ),
+ (Flatten: 1:1:10),
+ (FullyConnected: 1:1:128),
+ (Relu: 1:1:128),
+ (FullyConnected: 1:1:32),
+ (Relu: 1:1:32),
+ (FullyConnected: 1:1:1 ) # predicted ratings
+
+## Input Data Format
+
+Input data should be formatted as below
+
+```
+(int) (int) (float)
+(int) (int) (float)
+(int) (int) (float)
+(int) (int) (float)
+```
+Each represents user_id, product_id and ratings, respectively.
+
+Example, `product_ratings.txt` contains fake ratings on 5 userid X 5 products
+
+## How to run a train epoch
+
+Once you compile, with `meson`, you can run with `meson test nntrainer_product_ratings`.
+Please file an issue if you have a problem running the example.
+
+```bash
+$ meson test nntrainer_product_ratings -v -c ${build_dir}
+```
+
+Expected output is as below...
+
+```bash
+#1/100 - Training Loss: 0.692463 >> [ Accuracy: 100% - Validation Loss : 0.690833 ]
+...
+#100/100 - Training Loss: 0.535373 >> [ Accuracy: 100% - Validation Loss : 0.53367 ]
+```
LOCAL_LDFLAGS += -fexceptions
LOCAL_MODULE_TAGS := optional
LOCAL_ARM_MODE := arm
-LOCAL_MODULE := nntrainer_embedding
+LOCAL_MODULE := nntrainer_product_ratings
LOCAL_LDLIBS := -llog -landroid
LOCAL_SRC_FILES := main.cpp
std::string data_file;
-const unsigned int total_train_data_size = 10;
+const unsigned int total_train_data_size = 25;
unsigned int train_count = 0;
const unsigned int batch_size = 10;
-const unsigned int feature_size = 4;
+const unsigned int feature_size = 2;
-const unsigned int total_val_data_size = 10;
+const unsigned int total_val_data_size = 25;
bool training = false;
F.putback(c);
- if (!std::getline(F, temp))
+ if (!std::getline(F, temp)) {
return false;
+ }
std::istringstream buffer(temp);
float x;
return 0;
}
- for (unsigned int i = train_count; i < train_count + batch_size; ++i) {
-
- std::vector<float> o;
- std::vector<float> l;
- o.resize(feature_size);
- l.resize(1);
+ std::vector<float> o;
+ std::vector<float> l;
+ o.resize(feature_size);
+ l.resize(1);
+ for (unsigned int i = train_count; i < train_count + batch_size; ++i) {
if (!getData(dataFile, o, l, i)) {
return -1;
- };
+ }
for (unsigned int j = 0; j < feature_size; ++j)
outVec[0][count * feature_size + j] = o[j];
* back propagation of NN
* @param[in] arg 1 : train / inference
* @param[in] arg 2 : configuration file path
- * @param[in] arg 3 : resource path (dataset.txt or testset.txt)
+ * @param[in] arg 3 : resource path (data) with below format
+ * (int) (int) (float) #first data
+ * ...
+ * in each row represents user id, product id, rating (0 to 10)
*/
int main(int argc, char *argv[]) {
if (argc < 4) {
--- /dev/null
+res_path = meson.current_source_dir() / '..' / 'res'
+run_command('cp', '-lr', res_path, nntr_app_resdir / 'ProductRatings')
+
+e = executable('nntrainer_product_ratings',
+ 'main.cpp',
+ dependencies: [iniparser_dep, nntrainer_ccapi_dep, gtest_dep, nntrainer_dep],
+ install: get_option('install-app'),
+ install_dir: application_install_dir
+)
+
+# test split example
+test('nntrainer_product_ratings', e, args: ['train',
+ nntr_app_resdir / 'ProductRatings' / 'product_ratings_model.ini',
+ nntr_app_resdir / 'ProductRatings' / 'sample_product_ratings.txt']
+)
--- /dev/null
+# Model Section : Model
+[Model]
+Type = NeuralNetwork
+Epochs = 100 # Epochs
+Loss = mse # Loss function : mse (mean squared error)
+ # cross ( cross entropy )
+# Save_Path = "product_ratings_model.bin" # enable this to save result
+batch_size = 20 # batch size
+
+[Optimizer]
+Type = adam
+beta1 = 0.9
+beta2 = 0.999
+Learning_rate = 0.001
+epsilon = 1e-7
+
+[input]
+Type = input
+input_shape = 1:1:2 # channel:height:width
+
+[split]
+Type = split
+split_dimension = 3 # split by width
+
+[user_embed]
+input_layers = split
+Type = embedding
+in_dim = 6 # in dim must be more than len(set(user ids)) + 1
+out_dim = 5
+in_length = 1
+
+[product_embed]
+input_layers = split
+Type = embedding
+in_dim = 6 # in dim must be more than len(set(product ids)) + 1
+out_dim = 5
+in_length = 1
+
+[concat]
+input_layers = user_embed, product_embed
+Type = concat
+
+[flatten]
+Type = flatten
+
+[fc1]
+Type = fully_connected
+unit = 128
+activation = relu
+
+[fc2]
+Type = fully_connected
+unit = 32
+activation = relu
+
+[outputlayer]
+Type = fully_connected
+Unit = 1
+Bias_initializer = zeros
--- /dev/null
+1 1 5
+1 2 3
+1 3 4
+1 4 1
+1 5 0
+2 1 4
+2 2 1
+2 3 1
+2 4 1
+2 5 2
+3 1 2
+3 2 3
+3 3 4
+3 4 9
+3 5 10
+4 1 10
+4 2 9
+4 3 8
+4 4 4
+4 5 2
+5 1 1
+5 2 3
+5 3 5
+5 4 7
+5 5 9
subdir('TransferLearning/CIFAR_Classification/jni')
subdir('TransferLearning/Draw_Classification/jni')
subdir('Custom')
-subdir('Embedding/jni')
+subdir('ProductRatings/jni')
if get_option('enable-tflite-backbone')
subdir('SimpleShot')
try {
forwarding(true);
backwarding(iter++);
- } catch (...) {
+ } catch (std::exception &e) {
data_buffer->clear(nntrainer::BufferType::BUF_TRAIN);
- ml_loge("Error: training error in #%d/%d.", epoch_idx, epochs);
+ ml_loge("Error: training error in #%d/%d. %s", epoch_idx, epochs,
+ e.what());
throw;
}
std::cout << "#" << epoch_idx << "/" << epochs;
projects / platform / core / ml / nntrainer.git / commitdiff