kmarlen1988 commented on issue #1546: predict returning the same value for all observations URL: https://github.com/apache/incubator-mxnet/issues/1546#issuecomment-356256041 I'm using googlenet in mxnetR but the predicted probabilities are the same for all images. I tried to change the activation function, the batch size and i normalized the input to [-1,1] The code is the following: num_classes = 2 ConvFactory <- function(data, num_filter, kernel, stride = c(1, 1), pad = c(0, 0), name = '', suffix = '') { conv <- mx.symbol.Convolution(data = data, num_filter = num_filter, kernel = kernel, stride = stride, pad = pad, name = paste('conv_', name, suffix, sep = "")) act <- mx.symbol.Activation(data = conv, act_type = 'relu', name = paste('relu_', name, suffix, sep = '')) return(act) } InceptionFactory <- function(data, num_1x1, num_3x3red, num_3x3, num_d5x5red, num_d5x5, pool, proj, name) { # 1x1 c1x1 <- ConvFactory(data = data, num_filter = num_1x1, kernel = c(1, 1), name = paste(name, '_1x1', sep = '')) # 3x3 reduce + 3x3 c3x3r = ConvFactory(data = data, num_filter = num_3x3red, kernel = c(1, 1), name = paste(name, '_3x3', sep = ''), suffix = '_reduce') c3x3 = ConvFactory(data = c3x3r, num_filter = num_3x3, kernel = c(3, 3), pad = c(1, 1), name = paste(name, '_3x3', sep = '')) # double 3x3 reduce + double 3x3 cd5x5r = ConvFactory(data = data, num_filter = num_d5x5red, kernel = c(1, 1), name = paste(name, '_5x5', sep = ''), suffix = '_reduce') cd5x5 = ConvFactory(data = cd5x5r, num_filter = num_d5x5, kernel = c(5, 5), pad = c(2, 2), name = paste(name, '_5x5', sep = '')) # pool + proj pooling = mx.symbol.Pooling(data = data, kernel = c(3, 3), stride = c(1, 1), pad = c(1, 1), pool_type = pool, name = paste(pool, '_pool_', name, '_pool', sep = '')) cproj = ConvFactory(data = pooling, num_filter = proj, kernel = c(1, 1), name = paste(name, '_proj', sep = '')) # concat concat_lst <- list() concat_lst <- c(c1x1, c3x3, cd5x5, cproj) concat_lst$num.args = 4 concat_lst$name = paste('ch_concat_', name, '_chconcat', sep = '') concat = mxnet:::mx.varg.symbol.Concat(concat_lst) return(concat) } data <- mx.symbol.Variable("data") conv1 <- ConvFactory(data, 64, kernel = c(7, 7), stride = c(2, 2), pad = c(3, 3), name = "conv1") pool1 <- mx.symbol.Pooling(conv1, kernel = c(3, 3), stride = c(2, 2), pool_type = "max", pooling_convention='full') conv2 <- ConvFactory(pool1, 64, kernel = c(1, 1), stride = c(1, 1), name = "conv2") conv3 <- ConvFactory(conv2, 192, kernel = c(3, 3), stride = c(1, 1), pad = c(1, 1), name = "conv3") pool3 <- mx.symbol.Pooling(conv3, kernel = c(3, 3), stride = c(2, 2), pool_type = "max", pooling_convention='full') in3a <- InceptionFactory(pool3, 64, 96, 128, 16, 32, "max", 32, name = "in3a") in3b <- InceptionFactory(in3a, 128, 128, 192, 32, 96, "max", 64, name = "in3b") pool4 <- mx.symbol.Pooling(in3b, kernel = c(3, 3), stride = c(2, 2), pool_type = "max", pooling_convention='full') in4a <- InceptionFactory(pool4, 192, 96, 208, 16, 48, "max", 64, name = "in4a") in4b <- InceptionFactory(in4a, 160, 112, 224, 24, 64, "max", 64, name = "in4b") in4c <- InceptionFactory(in4b, 128, 128, 256, 24, 64, "max", 64, name = "in4c") in4d <- InceptionFactory(in4c, 112, 144, 288, 32, 64, "max", 64, name = "in4d") in4e <- InceptionFactory(in4d, 256, 160, 320, 32, 128, "max", 128, name = "in4e") pool5 <- mx.symbol.Pooling(in4e, kernel = c(3, 3), stride = c(2, 2), pool_type = "max", pooling_convention='full') in5a <- InceptionFactory(pool5, 256, 160, 320, 32, 128, "max", 128, name = "in5a") in5b <- InceptionFactory(in5a, 384, 192, 384, 48, 128, "max", 128, name = "in5b") pool6 <- mx.symbol.Pooling(in5b, kernel = c(7, 7), stride = c(1, 1), pool_type = "avg" ) flatten <- mx.symbol.Flatten(data = pool6) fc1 <- mx.symbol.FullyConnected(data = flatten, num_hidden = num_classes) googlenet <- mx.symbol.SoftmaxOutput(data = fc1, name = 'softmax') model <- mx.model.FeedForward.create(googlenet, X = train_array, y = train_y, ctx = device.cpu, num.round = 20, array.batch.size = 32, learning.rate = 0.05, momentum = 0.9, wd = 0.00001, eval.metric = mx.metric.accuracy, initializer=mx.init.uniform(0.07), epoch.end.callback = mx.callback.save.checkpoint("checkpoint")) predict_probs <- predict(model, test_array)
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