Pengenalan Cepat Menggambar Doodle: Cara Mencari Teman R, C ++, dan Neural Grids

 if (!DBI::dbExistsTable(con, "doodles")) { DBI::dbCreateTable( con = con, name = "doodles", fields = c( "countrycode" = "char(2)", "drawing" = "text", "key_id" = "bigint", "recognized" = "bool", "timestamp" = "timestamp", "word" = "text" ) ) } if (!DBI::dbExistsTable(con, "upload_log")) { DBI::dbCreateTable( con = con, name = "upload_log", fields = c( "id" = "serial", "file_name" = "text UNIQUE", "uploaded" = "bool DEFAULT false" ) ) } 

 #' @title     #' #' @description #'  CSV-  ZIP-       #' #' @param con      ( `MonetDBEmbeddedConnection`). #' @param tablename     . #' @oaram zipfile   ZIP-. #' @oaram filename    ZIP-. #' @param preprocess  ,      . #'     `data` ( `data.table`). #' #' @return `TRUE`. #' upload_file <- function(con, tablename, zipfile, filename, preprocess = NULL) { #   checkmate::assert_class(con, "MonetDBEmbeddedConnection") checkmate::assert_string(tablename) checkmate::assert_string(filename) checkmate::assert_true(DBI::dbExistsTable(con, tablename)) checkmate::assert_file_exists(zipfile, access = "r", extension = "zip") checkmate::assert_function(preprocess, args = c("data"), null.ok = TRUE) #   path <- file.path(tempdir(), filename) unzip(zipfile, files = filename, exdir = tempdir(), junkpaths = TRUE, unzip = getOption("unzip")) on.exit(unlink(file.path(path))) #    if (!is.null(preprocess)) { .data <- data.table::fread(file = path) .data <- preprocess(data = .data) data.table::fwrite(x = .data, file = path, append = FALSE) rm(.data) } #      CSV sql <- sprintf( "COPY OFFSET 2 INTO %s FROM '%s' USING DELIMITERS ',','\\n','\"' NULL AS '' BEST EFFORT", tablename, path ) #     DBI::dbExecute(con, sql) #         DBI::dbExecute(con, sprintf("INSERT INTO upload_log(file_name, uploaded) VALUES('%s', true)", filename)) return(invisible(TRUE)) } 

 #     files <- unzip(zipfile, list = TRUE)$Name #  ,       to_skip <- DBI::dbGetQuery(con, "SELECT file_name FROM upload_log")[[1L]] files <- setdiff(files, to_skip) if (length(files) > 0L) { #   tictoc::tic() #   pb <- txtProgressBar(min = 0L, max = length(files), style = 3) for (i in seq_along(files)) { upload_file(con = con, tablename = "doodles", zipfile = zipfile, filename = files[i]) setTxtProgressBar(pb, i) } close(pb) #   tictoc::toc() } # 526.141 sec elapsed -  SSD->SSD # 558.879 sec elapsed -  USB->SSD 

 message("Generate lables") invisible(DBI::dbExecute(con, "ALTER TABLE doodles ADD label_int int")) invisible(DBI::dbExecute(con, "UPDATE doodles SET label_int = dense_rank() OVER (ORDER BY word) - 1")) message("Generate row numbers") invisible(DBI::dbExecute(con, "ALTER TABLE doodles ADD id serial")) invisible(DBI::dbExecute(con, "CREATE ORDERED INDEX doodles_id_ord_idx ON doodles(id)")) 

 library(ggplot2) set.seed(0) #     con <- DBI::dbConnect(MonetDBLite::MonetDBLite(), Sys.getenv("DBDIR")) #        prep_sql <- function(batch_size) { sql <- sprintf("PREPARE SELECT id FROM doodles WHERE id IN (%s)", paste(rep("?", batch_size), collapse = ",")) res <- DBI::dbSendQuery(con, sql) return(res) } #     fetch_data <- function(rs, batch_size) { ids <- sample(seq_len(n), batch_size) res <- DBI::dbFetch(DBI::dbBind(rs, as.list(ids))) return(res) } #   res_bench <- bench::press( batch_size = 2^(4:10), { rs <- prep_sql(batch_size) bench::mark( fetch_data(rs, batch_size), min_iterations = 50L ) } ) #   cols <- c("batch_size", "min", "median", "max", "itr/sec", "total_time", "n_itr") res_bench[, cols] # batch_size min median max `itr/sec` total_time n_itr # <dbl> <bch:tm> <bch:tm> <bch:tm> <dbl> <bch:tm> <int> # 1 16 23.6ms 54.02ms 93.43ms 18.8 2.6s 49 # 2 32 38ms 84.83ms 151.55ms 11.4 4.29s 49 # 3 64 63.3ms 175.54ms 248.94ms 5.85 8.54s 50 # 4 128 83.2ms 341.52ms 496.24ms 3.00 16.69s 50 # 5 256 232.8ms 653.21ms 847.44ms 1.58 31.66s 50 # 6 512 784.6ms 1.41s 1.98s 0.740 1.1m 49 # 7 1024 681.7ms 2.72s 4.06s 0.377 2.16m 49 ggplot(res_bench, aes(x = factor(batch_size), y = median, group = 1)) + geom_point() + geom_line() + ylab("median time, s") + theme_minimal() DBI::dbDisconnect(con, shutdown = TRUE) 

 r_process_json_str <- function(json, line.width = 3, color = TRUE, scale = 1) { #  JSON coords <- jsonlite::fromJSON(json, simplifyMatrix = FALSE) tmp <- tempfile() #       on.exit(unlink(tmp)) png(filename = tmp, width = 256 * scale, height = 256 * scale, pointsize = 1) #   plot.new() #    plot.window(xlim = c(256 * scale, 0), ylim = c(256 * scale, 0)) #   cols <- if (color) rainbow(length(coords)) else "#000000" for (i in seq_along(coords)) { lines(x = coords[[i]][[1]] * scale, y = coords[[i]][[2]] * scale, col = cols[i], lwd = line.width) } dev.off() #    3-   res <- png::readPNG(tmp) return(res) } r_process_json_vector <- function(x, ...) { res <- lapply(x, r_process_json_str, ...) #  3-     4-    res <- do.call(abind::abind, c(res, along = 0)) return(res) } 

https://thecoatlessprofessor.com/programming/unofficial-rcpp-api-documentation

https://docs.opencv.org/4.0.1/d7/dbd/group__imgproc.html

https://xtensor.readthedocs.io/en/latest/

https://xtensor.readthedocs.io/en/latest/file_loading.html#loading-json-data-into-xtensor

https://cran.r-project.org/web/packages/RcppThread/vignettes/RcppThread-vignette.pdf

 Rcpp::registerPlugin("opencv", function() { #    pkg_config_name <- c("opencv", "opencv4") #    pkg-config pkg_config_bin <- Sys.which("pkg-config") #      checkmate::assert_file_exists(pkg_config_bin, access = "x") #     OpenCV  pkg-config check <- sapply(pkg_config_name, function(pkg) system(paste(pkg_config_bin, pkg))) if (all(check != 0)) { stop("OpenCV config for the pkg-config not found", call. = FALSE) } pkg_config_name <- pkg_config_name[check == 0] list(env = list( PKG_CXXFLAGS = system(paste(pkg_config_bin, "--cflags", pkg_config_name), intern = TRUE), PKG_LIBS = system(paste(pkg_config_bin, "--libs", pkg_config_name), intern = TRUE) )) }) 

 // [[Rcpp::plugins(cpp14)]] // [[Rcpp::plugins(opencv)]] // [[Rcpp::depends(xtensor)]] // [[Rcpp::depends(RcppThread)]] #include <xtensor/xjson.hpp> #include <xtensor/xadapt.hpp> #include <xtensor/xview.hpp> #include <xtensor-r/rtensor.hpp> #include <opencv2/core/core.hpp> #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp> #include <Rcpp.h> #include <RcppThread.h> //    using RcppThread::parallelFor; using json = nlohmann::json; using points = xt::xtensor<double,2>; //   JSON   using strokes = std::vector<points>; //   JSON   using xtensor3d = xt::xtensor<double, 3>; //      using xtensor4d = xt::xtensor<double, 4>; //      using rtensor3d = xt::rtensor<double, 3>; //     R using rtensor4d = xt::rtensor<double, 4>; //     R //   //     const static int SIZE = 256; //   // . https://en.wikipedia.org/wiki/Pixel_connectivity#2-dimensional const static int LINE_TYPE = cv::LINE_4; //     const static int LINE_WIDTH = 3; //   // https://docs.opencv.org/3.1.0/da/d54/group__imgproc__transform.html#ga5bb5a1fea74ea38e1a5445ca803ff121 const static int RESIZE_TYPE = cv::INTER_LINEAR; //    OpenCV-   template <typename T, int NCH, typename XT=xt::xtensor<T,3,xt::layout_type::column_major>> XT to_xt(const cv::Mat_<cv::Vec<T, NCH>>& src) { //    std::vector<int> shape = {src.rows, src.cols, NCH}; //      size_t size = src.total() * NCH; //  cv::Mat  xt::xtensor XT res = xt::adapt((T*) src.data, size, xt::no_ownership(), shape); return res; } //  JSON     strokes parse_json(const std::string& x) { auto j = json::parse(x); //      if (!j.is_array()) { throw std::runtime_error("'x' must be JSON array."); } strokes res; res.reserve(j.size()); for (const auto& a: j) { //      2-  if (!a.is_array() || a.size() != 2) { throw std::runtime_error("'x' must include only 2d arrays."); } //    auto p = a.get<points>(); res.push_back(p); } return res; } //   //  HSV cv::Mat ocv_draw_lines(const strokes& x, bool color = true) { //    auto stype = color ? CV_8UC3 : CV_8UC1; //    auto dtype = color ? CV_32FC3 : CV_32FC1; auto bg = color ? cv::Scalar(0, 0, 255) : cv::Scalar(255); auto col = color ? cv::Scalar(0, 255, 220) : cv::Scalar(0); cv::Mat img = cv::Mat(SIZE, SIZE, stype, bg); //   size_t n = x.size(); for (const auto& s: x) { //     size_t n_points = s.shape()[1]; for (size_t i = 0; i < n_points - 1; ++i) { //    cv::Point from(s(0, i), s(1, i)); //    cv::Point to(s(0, i + 1), s(1, i + 1)); //   cv::line(img, from, to, col, LINE_WIDTH, LINE_TYPE); } if (color) { //    col[0] += 180 / n; } } if (color) { //     RGB cv::cvtColor(img, img, cv::COLOR_HSV2RGB); } //     float32   [0, 1] img.convertTo(img, dtype, 1 / 255.0); return img; } //  JSON       xtensor3d process(const std::string& x, double scale = 1.0, bool color = true) { auto p = parse_json(x); auto img = ocv_draw_lines(p, color); if (scale != 1) { cv::Mat out; cv::resize(img, out, cv::Size(), scale, scale, RESIZE_TYPE); cv::swap(img, out); out.release(); } xtensor3d arr = color ? to_xt<double,3>(img) : to_xt<double,1>(img); return arr; } // [[Rcpp::export]] rtensor3d cpp_process_json_str(const std::string& x, double scale = 1.0, bool color = true) { xtensor3d res = process(x, scale, color); return res; } // [[Rcpp::export]] rtensor4d cpp_process_json_vector(const std::vector<std::string>& x, double scale = 1.0, bool color = false) { size_t n = x.size(); size_t dim = floor(SIZE * scale); size_t channels = color ? 3 : 1; xtensor4d res({n, dim, dim, channels}); parallelFor(0, n, [&x, &res, scale, color](int i) { xtensor3d tmp = process(x[i], scale, color); auto view = xt::view(res, i, xt::all(), xt::all(), xt::all()); view = tmp; }); return res; } 

 res_bench <- bench::mark( r_process_json_str(tmp_data[4, drawing], scale = 0.5), cpp_process_json_str(tmp_data[4, drawing], scale = 0.5), check = FALSE, min_iterations = 100 ) #   cols <- c("expression", "min", "median", "max", "itr/sec", "total_time", "n_itr") res_bench[, cols] # expression min median max `itr/sec` total_time n_itr # <chr> <bch:tm> <bch:tm> <bch:tm> <dbl> <bch:tm> <int> # 1 r_process_json_str 3.49ms 3.55ms 4.47ms 273. 490ms 134 # 2 cpp_process_json_str 1.94ms 2.02ms 5.32ms 489. 497ms 243 library(ggplot2) #   res_bench <- bench::press( batch_size = 2^(4:10), { .data <- tmp_data[sample(seq_len(.N), batch_size), drawing] bench::mark( r_process_json_vector(.data, scale = 0.5), cpp_process_json_vector(.data, scale = 0.5), min_iterations = 50, check = FALSE ) } ) res_bench[, cols] # expression batch_size min median max `itr/sec` total_time n_itr # <chr> <dbl> <bch:tm> <bch:tm> <bch:tm> <dbl> <bch:tm> <int> # 1 r 16 50.61ms 53.34ms 54.82ms 19.1 471.13ms 9 # 2 cpp 16 4.46ms 5.39ms 7.78ms 192. 474.09ms 91 # 3 r 32 105.7ms 109.74ms 212.26ms 7.69 6.5s 50 # 4 cpp 32 7.76ms 10.97ms 15.23ms 95.6 522.78ms 50 # 5 r 64 211.41ms 226.18ms 332.65ms 3.85 12.99s 50 # 6 cpp 64 25.09ms 27.34ms 32.04ms 36.0 1.39s 50 # 7 r 128 534.5ms 627.92ms 659.08ms 1.61 31.03s 50 # 8 cpp 128 56.37ms 58.46ms 66.03ms 16.9 2.95s 50 # 9 r 256 1.15s 1.18s 1.29s 0.851 58.78s 50 # 10 cpp 256 114.97ms 117.39ms 130.09ms 8.45 5.92s 50 # 11 r 512 2.09s 2.15s 2.32s 0.463 1.8m 50 # 12 cpp 512 230.81ms 235.6ms 261.99ms 4.18 11.97s 50 # 13 r 1024 4s 4.22s 4.4s 0.238 3.5m 50 # 14 cpp 1024 410.48ms 431.43ms 462.44ms 2.33 21.45s 50 ggplot(res_bench, aes(x = factor(batch_size), y = median, group = expression, color = expression)) + geom_point() + geom_line() + ylab("median time, s") + theme_minimal() + scale_color_discrete(name = "", labels = c("cpp", "r")) + theme(legend.position = "bottom") 

 train_generator <- function(db_connection = con, samples_index, num_classes = 340, batch_size = 32, scale = 1, color = FALSE, imagenet_preproc = FALSE) { #   checkmate::assert_class(con, "DBIConnection") checkmate::assert_integerish(samples_index) checkmate::assert_count(num_classes) checkmate::assert_count(batch_size) checkmate::assert_number(scale, lower = 0.001, upper = 5) checkmate::assert_flag(color) checkmate::assert_flag(imagenet_preproc) # ,          dt <- data.table::data.table(id = sample(samples_index)) #    dt[, batch := (.I - 1L) %/% batch_size + 1L] #       dt <- dt[, if (.N == batch_size) .SD, keyby = batch] #   i <- 1 #   max_i <- dt[, max(batch)] #     sql <- sprintf( "PREPARE SELECT drawing, label_int FROM doodles WHERE id IN (%s)", paste(rep("?", batch_size), collapse = ",") ) res <- DBI::dbSendQuery(con, sql) #  keras::to_categorical to_categorical <- function(x, num) { n <- length(x) m <- numeric(n * num) m[x * n + seq_len(n)] <- 1 dim(m) <- c(n, num) return(m) } #  function() { #    if (i > max_i) { dt[, id := sample(id)] data.table::setkey(dt, batch) #   i <<- 1 max_i <<- dt[, max(batch)] } # ID    batch_ind <- dt[batch == i, id] #   batch <- DBI::dbFetch(DBI::dbBind(res, as.list(batch_ind)), n = -1) #   i <<- i + 1 #  JSON    batch_x <- cpp_process_json_vector(batch$drawing, scale = scale, color = color) if (imagenet_preproc) { #  c  [0, 1]   [-1, 1] batch_x <- (batch_x - 0.5) * 2 } batch_y <- to_categorical(batch$label_int, num_classes) result <- list(batch_x, batch_y) return(result) } } 

 library(Rcpp) library(keras) library(ggplot2) source("utils/rcpp.R") source("utils/keras_iterator.R") con <- DBI::dbConnect(drv = MonetDBLite::MonetDBLite(), Sys.getenv("DBDIR")) ind <- seq_len(DBI::dbGetQuery(con, "SELECT count(*) FROM doodles")[[1L]]) num_classes <- DBI::dbGetQuery(con, "SELECT max(label_int) + 1 FROM doodles")[[1L]] #     train_ind <- sample(ind, floor(length(ind) * 0.995)) #     val_ind <- ind[-train_ind] rm(ind) #   scale <- 0.5 #   res_bench <- bench::press( batch_size = 2^(4:10), { it1 <- train_generator( db_connection = con, samples_index = train_ind, num_classes = num_classes, batch_size = batch_size, scale = scale ) bench::mark( it1(), min_iterations = 50L ) } ) #   cols <- c("batch_size", "min", "median", "max", "itr/sec", "total_time", "n_itr") res_bench[, cols] # batch_size min median max `itr/sec` total_time n_itr # <dbl> <bch:tm> <bch:tm> <bch:tm> <dbl> <bch:tm> <int> # 1 16 25ms 64.36ms 92.2ms 15.9 3.09s 49 # 2 32 48.4ms 118.13ms 197.24ms 8.17 5.88s 48 # 3 64 69.3ms 117.93ms 181.14ms 8.57 5.83s 50 # 4 128 157.2ms 240.74ms 503.87ms 3.85 12.71s 49 # 5 256 359.3ms 613.52ms 988.73ms 1.54 30.5s 47 # 6 512 884.7ms 1.53s 2.07s 0.674 1.11m 45 # 7 1024 2.7s 3.83s 5.47s 0.261 2.81m 44 ggplot(res_bench, aes(x = factor(batch_size), y = median, group = 1)) + geom_point() + geom_line() + ylab("median time, s") + theme_minimal() DBI::dbDisconnect(con, shutdown = TRUE) 

 test_generator <- function(dt, batch_size = 32, scale = 1, color = FALSE, imagenet_preproc = FALSE) { #   checkmate::assert_data_table(dt) checkmate::assert_count(batch_size) checkmate::assert_number(scale, lower = 0.001, upper = 5) checkmate::assert_flag(color) checkmate::assert_flag(imagenet_preproc) #    dt[, batch := (.I - 1L) %/% batch_size + 1L] data.table::setkey(dt, batch) i <- 1 max_i <- dt[, max(batch)] #  function() { batch_x <- cpp_process_json_vector(dt[batch == i, drawing], scale = scale, color = color) if (imagenet_preproc) { #  c  [0, 1]   [-1, 1] batch_x <- (batch_x - 0.5) * 2 } result <- list(batch_x) i <<- i + 1 return(result) } } 

 library(keras) top_3_categorical_accuracy <- custom_metric( name = "top_3_categorical_accuracy", metric_fn = function(y_true, y_pred) { metric_top_k_categorical_accuracy(y_true, y_pred, k = 3) } ) layer_sep_conv_bn <- function(object, filters, alpha = 1, depth_multiplier = 1, strides = c(2, 2)) { # NB! depth_multiplier != resolution multiplier # https://github.com/keras-team/keras/issues/10349 layer_depthwise_conv_2d( object = object, kernel_size = c(3, 3), strides = strides, padding = "same", depth_multiplier = depth_multiplier ) %>% layer_batch_normalization() %>% layer_activation_relu() %>% layer_conv_2d( filters = filters * alpha, kernel_size = c(1, 1), strides = c(1, 1) ) %>% layer_batch_normalization() %>% layer_activation_relu() } get_mobilenet_v1 <- function(input_shape = c(224, 224, 1), num_classes = 340, alpha = 1, depth_multiplier = 1, optimizer = optimizer_adam(lr = 0.002), loss = "categorical_crossentropy", metrics = c("categorical_crossentropy", top_3_categorical_accuracy)) { inputs <- layer_input(shape = input_shape) outputs <- inputs %>% layer_conv_2d(filters = 32, kernel_size = c(3, 3), strides = c(2, 2), padding = "same") %>% layer_batch_normalization() %>% layer_activation_relu() %>% layer_sep_conv_bn(filters = 64, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 128, strides = c(2, 2)) %>% layer_sep_conv_bn(filters = 128, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 256, strides = c(2, 2)) %>% layer_sep_conv_bn(filters = 256, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 512, strides = c(2, 2)) %>% layer_sep_conv_bn(filters = 512, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 512, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 512, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 512, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 512, strides = c(1, 1)) %>% layer_sep_conv_bn(filters = 1024, strides = c(2, 2)) %>% layer_sep_conv_bn(filters = 1024, strides = c(1, 1)) %>% layer_global_average_pooling_2d() %>% layer_dense(units = num_classes) %>% layer_activation_softmax() model <- keras_model( inputs = inputs, outputs = outputs ) model %>% compile( optimizer = optimizer, loss = loss, metrics = metrics ) return(model) } 

 get_model <- function(name = "mobilenet_v2", input_shape = NULL, weights = "imagenet", pooling = "avg", num_classes = NULL, optimizer = keras::optimizer_adam(lr = 0.002), loss = "categorical_crossentropy", metrics = NULL, color = TRUE, compile = FALSE) { #   checkmate::assert_string(name) checkmate::assert_integerish(input_shape, lower = 1, upper = 256, len = 3) checkmate::assert_count(num_classes) checkmate::assert_flag(color) checkmate::assert_flag(compile) #     keras model_fun <- get0(paste0("application_", name), envir = asNamespace("keras")) #      if (is.null(model_fun)) { stop("Model ", shQuote(name), " not found.", call. = FALSE) } base_model <- model_fun( input_shape = input_shape, include_top = FALSE, weights = weights, pooling = pooling ) #    ,    if (!color) { base_model_conf <- keras::get_config(base_model) base_model_conf$layers[[1]]$config$batch_input_shape[[4]] <- 1L base_model <- keras::from_config(base_model_conf) } predictions <- keras::get_layer(base_model, "global_average_pooling2d_1")$output predictions <- keras::layer_dense(predictions, units = num_classes, activation = "softmax") model <- keras::keras_model( inputs = base_model$input, outputs = predictions ) if (compile) { keras::compile( object = model, optimizer = optimizer, loss = loss, metrics = metrics ) } return(model) } 

 FROM tensorflow/tensorflow:1.12.0-gpu MAINTAINER Artem Klevtsov <aaklevtsov@gmail.com> SHELL ["/bin/bash", "-c"] ARG LOCALE="en_US.UTF-8" ARG APT_PKG="libopencv-dev r-base r-base-dev littler" ARG R_BIN_PKG="futile.logger checkmate data.table rcpp rapidjsonr dbi keras jsonlite curl digest remotes" ARG R_SRC_PKG="xtensor RcppThread docopt MonetDBLite" ARG PY_PIP_PKG="keras" ARG DIRS="/db /app /app/data /app/models /app/logs" RUN source /etc/os-release && \ echo "deb https://cloud.r-project.org/bin/linux/ubuntu ${UBUNTU_CODENAME}-cran35/" > /etc/apt/sources.list.d/cran35.list && \ apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E084DAB9 && \ add-apt-repository -y ppa:marutter/c2d4u3.5 && \ add-apt-repository -y ppa:timsc/opencv-3.4 && \ apt-get update && \ apt-get install -y locales && \ locale-gen ${LOCALE} && \ apt-get install -y --no-install-recommends ${APT_PKG} && \ ln -s /usr/lib/R/site-library/littler/examples/install.r /usr/local/bin/install.r && \ ln -s /usr/lib/R/site-library/littler/examples/install2.r /usr/local/bin/install2.r && \ ln -s /usr/lib/R/site-library/littler/examples/installGithub.r /usr/local/bin/installGithub.r && \ echo 'options(Ncpus = parallel::detectCores())' >> /etc/R/Rprofile.site && \ echo 'options(repos = c(CRAN = "https://cloud.r-project.org"))' >> /etc/R/Rprofile.site && \ apt-get install -y $(printf "r-cran-%s " ${R_BIN_PKG}) && \ install.r ${R_SRC_PKG} && \ pip install ${PY_PIP_PKG} && \ mkdir -p ${DIRS} && \ chmod 777 ${DIRS} && \ rm -rf /tmp/downloaded_packages/ /tmp/*.rds && \ rm -rf /var/lib/apt/lists/* COPY utils /app/utils COPY src /app/src COPY tests /app/tests COPY bin/*.R /app/ ENV DBDIR="/db" ENV CUDA_HOME="/usr/local/cuda" ENV PATH="/app:${PATH}" WORKDIR /app VOLUME /db VOLUME /app CMD bash 

 #!/bin/sh DBDIR=${PWD}/db LOGSDIR=${PWD}/logs MODELDIR=${PWD}/models DATADIR=${PWD}/data ARGS="--runtime=nvidia --rm -v ${DBDIR}:/db -v ${LOGSDIR}:/app/logs -v ${MODELDIR}:/app/models -v ${DATADIR}:/app/data" if [ -z "$1" ]; then CMD="Rscript /app/train_nn.R" elif [ "$1" = "bash" ]; then ARGS="${ARGS} -ti" else CMD="Rscript /app/train_nn.R $@" fi docker run ${ARGS} doodles-tf ${CMD} 

 #     log_file_tmpl <- file.path("logs", sprintf( "%s_%d_%dch_%s.csv", model_name, dim_size, channels, format(Sys.time(), "%Y%m%d%H%M%OS") )) #     model_file_tmpl <- file.path("models", sprintf( "%s_%d_%dch_{epoch:02d}_{val_loss:.2f}.h5", model_name, dim_size, channels )) callbacks_list <- list( keras::callback_csv_logger( filename = log_file_tmpl ), keras::callback_early_stopping( monitor = "val_loss", min_delta = 1e-4, patience = 8, verbose = 1, mode = "min" ), keras::callback_reduce_lr_on_plateau( monitor = "val_loss", factor = 0.5, #  lr  2  patience = 4, verbose = 1, min_delta = 1e-4, mode = "min" ), keras::callback_model_checkpoint( filepath = model_file_tmpl, monitor = "val_loss", save_best_only = FALSE, save_weights_only = FALSE, mode = "min" ) )