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* add cgan R demo scripts
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require("imager") | ||
require("dplyr") | ||
require("readr") | ||
require("mxnet") | ||
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source("iterators.R") | ||
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###################################################### | ||
### Data import and preperation | ||
### First download MNIST train data at Kaggle: | ||
### https://www.kaggle.com/c/digit-recognizer/data | ||
###################################################### | ||
train <- read_csv('data/train.csv') | ||
train<- data.matrix(train) | ||
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train_data <- train[,-1] | ||
train_data <- t(train_data/255*2-1) | ||
train_label <- as.integer(train[,1]) | ||
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dim(train_data) <- c(28, 28, 1, ncol(train_data)) | ||
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################################################## | ||
#### Model parameters | ||
################################################## | ||
random_dim<- 96 | ||
gen_features<- 96 | ||
dis_features<- 32 | ||
image_depth = 1 | ||
fix_gamma<- T | ||
no_bias<- T | ||
eps<- 1e-5 + 1e-12 | ||
batch_size<- 64 | ||
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################################################## | ||
#### Generator Symbol | ||
################################################## | ||
data = mx.symbol.Variable('data') | ||
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gen_rand<- mx.symbol.normal(loc=0, scale=1, shape=c(1, 1, random_dim, batch_size), name="gen_rand") | ||
gen_concat<- mx.symbol.Concat(data = list(data, gen_rand), num.args = 2, name="gen_concat") | ||
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g1 = mx.symbol.Deconvolution(gen_concat, name='g1', kernel=c(4,4), num_filter=gen_features*4, no_bias=T) | ||
gbn1 = mx.symbol.BatchNorm(g1, name='gbn1', fix_gamma=fix_gamma, eps=eps) | ||
gact1 = mx.symbol.Activation(gbn1, name='gact1', act_type='relu') | ||
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g2 = mx.symbol.Deconvolution(gact1, name='g2', kernel=c(3,3), stride=c(2,2), pad=c(1,1), num_filter=gen_features*2, no_bias=no_bias) | ||
gbn2 = mx.symbol.BatchNorm(g2, name='gbn2', fix_gamma=fix_gamma, eps=eps) | ||
gact2 = mx.symbol.Activation(gbn2, name='gact2', act_type='relu') | ||
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g3 = mx.symbol.Deconvolution(gact2, name='g3', kernel=c(4,4), stride=c(2,2), pad=c(1,1), num_filter=gen_features, no_bias=no_bias) | ||
gbn3 = mx.symbol.BatchNorm(g3, name='gbn3', fix_gamma=fix_gamma, eps=eps) | ||
gact3 = mx.symbol.Activation(gbn3, name='gact3', act_type='relu') | ||
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g4 = mx.symbol.Deconvolution(gact3, name='g4', kernel=c(4,4), stride=c(2,2), pad=c(1,1), num_filter=image_depth, no_bias=no_bias) | ||
G_sym = mx.symbol.Activation(g4, name='G_sym', act_type='tanh') | ||
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################################################## | ||
#### Discriminator Symbol | ||
################################################## | ||
data = mx.symbol.Variable('data') | ||
dis_digit = mx.symbol.Variable('digit') | ||
label = mx.symbol.Variable('label') | ||
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dis_digit<- mx.symbol.Reshape(data=dis_digit, shape=c(1,1,10,batch_size), name="digit_reshape") | ||
dis_digit<- mx.symbol.broadcast_to(data=dis_digit, shape=c(28,28,10, batch_size), name="digit_broadcast") | ||
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data_concat <- mx.symbol.Concat(list(data, dis_digit), num.args = 2, dim = 1, name='dflat_concat') | ||
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d1 = mx.symbol.Convolution(data=data_concat, name='d1', kernel=c(3,3), stride=c(1,1), pad=c(0,0), num_filter=24, no_bias=no_bias) | ||
dbn1 = mx.symbol.BatchNorm(d1, name='dbn1', fix_gamma=fix_gamma, eps=eps) | ||
dact1 = mx.symbol.LeakyReLU(dbn1, name='dact1', act_type='elu', slope=0.25) | ||
pool1 <- mx.symbol.Pooling(data=dact1, name="pool1", pool_type="max", kernel=c(2,2), stride=c(2,2), pad=c(0,0)) | ||
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d2 = mx.symbol.Convolution(pool1, name='d2', kernel=c(3,3), stride=c(2,2), pad=c(0,0), num_filter=32, no_bias=no_bias) | ||
dbn2 = mx.symbol.BatchNorm(d2, name='dbn2', fix_gamma=fix_gamma, eps=eps) | ||
dact2 = mx.symbol.LeakyReLU(dbn2, name='dact2', act_type='elu', slope=0.25) | ||
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d3 = mx.symbol.Convolution(dact2, name='d3', kernel=c(3,3), stride=c(1,1), pad=c(0,0), num_filter=64, no_bias=no_bias) | ||
dbn3 = mx.symbol.BatchNorm(d3, name='dbn3', fix_gamma=fix_gamma, eps=eps) | ||
dact3 = mx.symbol.LeakyReLU(dbn3, name='dact3', act_type='elu', slope=0.25) | ||
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d4 = mx.symbol.Convolution(dact2, name='d3', kernel=c(4,4), stride=c(1,1), pad=c(0,0), num_filter=64, no_bias=no_bias) | ||
dbn4 = mx.symbol.BatchNorm(d4, name='dbn4', fix_gamma=fix_gamma, eps=eps) | ||
dact4 = mx.symbol.LeakyReLU(dbn4, name='dact4', act_type='elu', slope=0.25) | ||
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# pool4 <- mx.symbol.Pooling(data=dact3, name="pool4", pool_type="avg", kernel=c(4,4), stride=c(1,1), pad=c(0,0)) | ||
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dflat = mx.symbol.Flatten(dact4, name="dflat") | ||
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dfc <- mx.symbol.FullyConnected(data=dflat, name="dfc", num_hidden=1, no_bias=F) | ||
D_sym = mx.symbol.LogisticRegressionOutput(data=dfc, label=label, name='D_sym') | ||
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######################## | ||
### Graph | ||
######################## | ||
input_shape_G<- c(1, 1, 10, batch_size) | ||
input_shape_D<- c(28, 28, 1, batch_size) | ||
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graph.viz(G_sym, type = "graph", direction = "LR") | ||
graph.viz(D_sym, type = "graph", direction = "LR") | ||
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##################################################### | ||
### Training module for GAN | ||
##################################################### | ||
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devices<- mx.cpu() | ||
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data_shape_G<- c(1, 1, 10, batch_size) | ||
data_shape_D<- c(28, 28, 1, batch_size) | ||
digit_shape_D<- c(10, batch_size) | ||
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mx.metric.binacc <- mx.metric.custom("binacc", function(label, pred) { | ||
res <- mean(label==round(pred)) | ||
return(res) | ||
}) | ||
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mx.metric.logloss <- mx.metric.custom("logloss", function(label, pred) { | ||
res <- mean(label*log(pred)+(1-label)*log(1-pred)) | ||
return(res) | ||
}) | ||
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############################################## | ||
### Define iterators | ||
iter_G<- G_iterator(batch_size = batch_size) | ||
iter_D<- D_iterator(batch_size = batch_size) | ||
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exec_G<- mx.simple.bind(symbol = G_sym, data=data_shape_G, ctx = devices, grad.req = "write") | ||
exec_D<- mx.simple.bind(symbol = D_sym, data=data_shape_D, digit=digit_shape_D, ctx = devices, grad.req = "write") | ||
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### initialize parameters - To Do - personalise each layer | ||
initializer<- mx.init.Xavier(rnd_type = "gaussian", factor_type = "avg", magnitude = 3) | ||
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arg_param_ini_G<- mx.init.create(initializer = initializer, shape.array = mx.symbol.infer.shape(G_sym, data=data_shape_G)$arg.shapes, ctx = mx.cpu()) | ||
aux_param_ini_G<- mx.init.create(initializer = initializer, shape.array = mx.symbol.infer.shape(G_sym, data=data_shape_G)$aux.shapes, ctx = mx.cpu()) | ||
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arg_param_ini_D<- mx.init.create(initializer = initializer, shape.array = mx.symbol.infer.shape(D_sym, data=data_shape_D, digit=digit_shape_D)$arg.shapes, ctx = mx.cpu()) | ||
aux_param_ini_D<- mx.init.create(initializer = initializer, shape.array = mx.symbol.infer.shape(D_sym, data=data_shape_D, digit=digit_shape_D)$aux.shapes, ctx = mx.cpu()) | ||
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mx.exec.update.arg.arrays(exec_G, arg_param_ini_G, match.name=TRUE) | ||
mx.exec.update.aux.arrays(exec_G, aux_param_ini_G, match.name=TRUE) | ||
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mx.exec.update.arg.arrays(exec_D, arg_param_ini_D, match.name=TRUE) | ||
mx.exec.update.aux.arrays(exec_D, aux_param_ini_D, match.name=TRUE) | ||
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input_names_G <- mxnet:::mx.model.check.arguments(G_sym) | ||
input_names_D <- mxnet:::mx.model.check.arguments(D_sym) | ||
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################################################### | ||
#initialize optimizers | ||
optimizer_G<-mx.opt.create(name = "adadelta", | ||
rho=0.92, | ||
epsilon = 1e-6, | ||
wd=0, | ||
rescale.grad=1/batch_size, | ||
clip_gradient=1) | ||
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updater_G<- mx.opt.get.updater(optimizer = optimizer_G, weights = exec_G$ref.arg.arrays) | ||
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optimizer_D<-mx.opt.create(name = "adadelta", | ||
rho=0.92, | ||
epsilon = 1e-6, | ||
wd=0, | ||
rescale.grad=1/batch_size, | ||
clip_gradient=1) | ||
updater_D<- mx.opt.get.updater(optimizer = optimizer_D, weights = exec_D$ref.arg.arrays) | ||
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#################################### | ||
#initialize metric | ||
metric_G<- mx.metric.binacc | ||
metric_G_value<- metric_G$init() | ||
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metric_D<- mx.metric.binacc | ||
metric_D_value<- metric_D$init() | ||
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iteration<- 1 | ||
iter_G$reset() | ||
iter_D$reset() | ||
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for (iteration in 1:2400) { | ||
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iter_G$iter.next() | ||
iter_D$iter.next() | ||
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### Random input to Generator to produce fake sample | ||
G_values <- iter_G$value() | ||
G_data <- G_values[input_names_G] | ||
mx.exec.update.arg.arrays(exec_G, arg.arrays = G_data, match.name=TRUE) | ||
mx.exec.forward(exec_G, is.train=T) | ||
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### Feed Discriminator with Concatenated Generator images and real images | ||
### Random input to Generator | ||
D_data_fake <- exec_G$ref.outputs$G_sym_output | ||
D_digit_fake <- G_values$data %>% mx.nd.Reshape(shape=c(-1, batch_size)) | ||
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D_values <- iter_D$value() | ||
D_data_real <- D_values$data | ||
D_digit_real <- D_values$digit | ||
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### Train loop on fake | ||
mx.exec.update.arg.arrays(exec_D, arg.arrays = list(data=D_data_fake, digit=D_digit_fake, label=mx.nd.array(rep(0, batch_size))), match.name=TRUE) | ||
mx.exec.forward(exec_D, is.train=T) | ||
mx.exec.backward(exec_D) | ||
update_args_D<- updater_D(weight = exec_D$ref.arg.arrays, grad = exec_D$ref.grad.arrays) | ||
mx.exec.update.arg.arrays(exec_D, update_args_D, skip.null=TRUE) | ||
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metric_D_value <- metric_D$update(label = mx.nd.array(rep(0, batch_size)), exec_D$ref.outputs[["D_sym_output"]], metric_D_value) | ||
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### Train loop on real | ||
mx.exec.update.arg.arrays(exec_D, arg.arrays = list(data=D_data_real, digit=D_digit_real, label=mx.nd.array(rep(1, batch_size))), match.name=TRUE) | ||
mx.exec.forward(exec_D, is.train=T) | ||
mx.exec.backward(exec_D) | ||
update_args_D<- updater_D(weight = exec_D$ref.arg.arrays, grad = exec_D$ref.grad.arrays) | ||
mx.exec.update.arg.arrays(exec_D, update_args_D, skip.null=TRUE) | ||
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metric_D_value <- metric_D$update(mx.nd.array(rep(1, batch_size)), exec_D$ref.outputs[["D_sym_output"]], metric_D_value) | ||
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### Update Generator weights - use a seperate executor for writing data gradients | ||
exec_D_back<- mxnet:::mx.symbol.bind(symbol = D_sym, arg.arrays = exec_D$arg.arrays, aux.arrays = exec_D$aux.arrays, grad.reqs = rep("write", length(exec_D$arg.arrays)), ctx = devices) | ||
mx.exec.update.arg.arrays(exec_D_back, arg.arrays = list(data=D_data_fake, digit=D_digit_fake, label=mx.nd.array(rep(1, batch_size))), match.name=TRUE) | ||
mx.exec.forward(exec_D_back, is.train=T) | ||
mx.exec.backward(exec_D_back) | ||
D_grads<- exec_D_back$ref.grad.arrays$data | ||
mx.exec.backward(exec_G, out_grads=D_grads) | ||
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update_args_G<- updater_G(weight = exec_G$ref.arg.arrays, grad = exec_G$ref.grad.arrays) | ||
mx.exec.update.arg.arrays(exec_G, update_args_G, skip.null=TRUE) | ||
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### Update metrics | ||
#metric_G_value <- metric_G$update(values[[label_name]], exec_G$ref.outputs[[output_name]], metric_G_value) | ||
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if (iteration %% 25==0){ | ||
D_metric_result <- metric_D$get(metric_D_value) | ||
cat(paste0("[", iteration, "] ", D_metric_result$name, ": ", D_metric_result$value, "\n")) | ||
} | ||
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if (iteration==1 | iteration %% 100==0){ | ||
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metric_D_value<- metric_D$init() | ||
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par(mfrow=c(3,3), mar=c(0.1,0.1,0.1,0.1)) | ||
for (i in 1:9) { | ||
img <- as.array(exec_G$ref.outputs$G_sym_output)[,,,i] | ||
plot(as.cimg(img), axes=F) | ||
} | ||
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print(as.numeric(as.array(G_values$digit))) | ||
print(as.numeric(as.array(D_values$label))) | ||
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} | ||
} | ||
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mx.symbol.save(D_sym, filename = "models/D_sym_model_v1.json") | ||
mx.nd.save(exec_D$arg.arrays, filename = "models/D_aux_params_v1.params") | ||
mx.nd.save(exec_D$aux.arrays, filename = "models/D_aux_params_v1.params") | ||
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mx.symbol.save(G_sym, filename = "models/G_sym_model_v1.json") | ||
mx.nd.save(exec_G$arg.arrays, filename = "models/G_arg_params_v1.params") | ||
mx.nd.save(exec_G$aux.arrays, filename = "models/G_aux_params_v1.params") | ||
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### Inference | ||
G_sym<- mx.symbol.load("models/G_sym_model_v1.json") | ||
G_arg_params<- mx.nd.load("models/G_arg_params_v1.params") | ||
G_aux_params<- mx.nd.load("models/G_aux_params_v1.params") | ||
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digit<- mx.nd.array(rep(9, times=batch_size)) | ||
data<- mx.nd.one.hot(indices = digit, depth = 10) | ||
data<- mx.nd.reshape(data = data, shape = c(1,1,-1, batch_size)) | ||
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exec_G<- mx.simple.bind(symbol = G_sym, data=data_shape_G, ctx = devices, grad.req = "null") | ||
mx.exec.update.arg.arrays(exec_G, G_arg_params, match.name=TRUE) | ||
mx.exec.update.arg.arrays(exec_G, list(data=data), match.name=TRUE) | ||
mx.exec.update.aux.arrays(exec_G, G_aux_params, match.name=TRUE) | ||
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mx.exec.forward(exec_G, is.train=F) | ||
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par(mfrow=c(3,3), mar=c(0.1,0.1,0.1,0.1)) | ||
for (i in 1:9) { | ||
img <- as.array(exec_G$ref.outputs$G_sym_output)[,,,i] | ||
plot(as.cimg(img), axes=F) | ||
} |
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G_iterator<- function(batch_size){ | ||
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batch<- 0 | ||
batch_per_epoch<-5 | ||
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reset<- function(){ | ||
batch<<- 0 | ||
} | ||
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iter.next<- function(){ | ||
batch<<- batch+1 | ||
if (batch>batch_per_epoch) { | ||
return(FALSE) | ||
} else { | ||
return(TRUE) | ||
} | ||
} | ||
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value<- function(){ | ||
set.seed(123+batch) | ||
digit<- mx.nd.array(sample(0:9, size = batch_size, replace = T)) | ||
data<- mx.nd.one.hot(indices = digit, depth = 10) | ||
data<- mx.nd.reshape(data = data, shape = c(1,1,-1, batch_size)) | ||
return(list(data=data, digit=digit)) | ||
} | ||
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return(list(reset=reset, iter.next=iter.next, value=value, batch_size=batch_size, batch=batch)) | ||
} | ||
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D_iterator<- function(batch_size){ | ||
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batch<- 0 | ||
batch_per_epoch<-5 | ||
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reset<- function(){ | ||
batch<<- 0 | ||
} | ||
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iter.next<- function(){ | ||
batch<<- batch+1 | ||
if (batch>batch_per_epoch) { | ||
return(FALSE) | ||
} else { | ||
return(TRUE) | ||
} | ||
} | ||
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value<- function(){ | ||
set.seed(123+batch) | ||
idx<- sample(length(train_label), size = batch_size, replace = T) | ||
data<- train_data[,,,idx, drop=F] | ||
label<- mx.nd.array(train_label[idx]) | ||
digit<- mx.nd.one.hot(indices = label, depth = 10) | ||
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return(list(data=mx.nd.array(data), digit=digit, label=label)) | ||
} | ||
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return(list(reset=reset, iter.next=iter.next, value=value, batch_size=batch_size, batch=batch)) | ||
} | ||
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