Conv2D crash on MNIST in tensforlow lib #193
Comments
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@eaplatanios I can verify that this happens whenever a convolutional layer is used. I have reproduced this in There seems to be some error resulting due to the Graph optimizer Tensorflow is using in the backend. What do you think? |
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This does indeed look related to grappler (the TF graph optimizer). Does it reproduce with version |
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Just tried with |
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Still failing with version import tensorflow.data.image.CIFARLoader
val dataSet = CIFARLoader.load(Paths.get("/home/windymelt/Downloads/cifar-100-python"), CIFARLoader.CIFAR_100)
import tensorflow.api.ops.data.Data
import tensorflow.api.::
val trainImages = () => Data.datasetFromTensorSlices(dataSet.trainImages, "TrainImages").map(_.toFloat)
val trainLabels = () => Data.datasetFromTensorSlices(dataSet.trainLabels(::, 1), "TrainLabels").map(_.toLong)
val trainData = () => trainImages().zip(trainLabels())
.repeat()
.shuffle(10000)
.batch(32)
.prefetch(10)
import tensorflow.api._
import tensorflow.api.learn.layers._
val input = Input(
FLOAT32,
Shape(-1, dataSet.trainImages.shape(1), dataSet.trainImages.shape(2), dataSet.trainImages.shape(3))
)
val trainInput = Input(INT64, Shape(-1))
import tensorflow.api.ops.NN.SameConvPadding
val layer = //Conv2D[Float]("Layer_0/Conv2D", Shape(2, 2, 3, 16), 1, 1, SameConvPadding) >>
AddBias[Float]("Layer_0/Bias") >>
ReLU[Float]("Layer_0/ReLU", 0.1f) >>
MaxPool[Float]("Layer_0/MaxPool", Seq(1, 2, 2, 1), 1, 1, SameConvPadding) >>
// Conv2D[Float]("Layer_1/Conv2D", Shape(2, 2, 16, 32), 1, 1, SameConvPadding) >>
AddBias[Float]("Bias_1") >>
ReLU[Float]("Layer_1/ReLU", 0.1f) >>
MaxPool[Float]("Layer_1/MaxPool", Seq(1, 2, 2, 1), 1, 1, SameConvPadding) >>
Flatten[Float]("Layer_2/Flatten") >>
Linear[Float]("Layer_2/Linear", 256) >>
ReLU[Float]("Layer_2/ReLU", 0.1f) >>
Linear[Float]("OutputLayer/Linear", 100)
val loss = SparseSoftmaxCrossEntropy[Float, Long, Float]("Loss/CrossEntropy") >>
Mean[Float]("Loss/Mean") >>
ScalarSummary[Float]("Loss/Summary", "Loss")
val optimizer = tf.train.AdaGrad(0.1f)
val model = tf.learn.Model.simpleSupervised(
input = input,
trainInput = trainInput,
layer = layer,
loss = loss,
optimizer = optimizer)
val summariesDir = Paths.get("temp/cnn-cifar")
val estimator = tensorflow.api.learn.estimators.InMemoryEstimator(
model,
tensorflow.api.learn.Configuration(Some(summariesDir)),
tensorflow.api.learn.StopCriteria(maxSteps = Some(100000)),
Set(
tensorflow.api.learn.hooks.LossLogger(trigger = tf.learn.StepHookTrigger(100)),
tensorflow.api.learn.hooks.StepRateLogger(log = false, summaryDir = summariesDir, trigger = tensorflow.api.learn.hooks.StepHookTrigger(100)),
tensorflow.api.learn.hooks.CheckpointSaver(summariesDir, tensorflow.api.learn.hooks.StepHookTrigger(1000))),
tensorBoardConfig = tensorflow.api.config.TensorBoardConfig(summariesDir, reloadInterval = 1))
estimator.train(trainData, tensorflow.api.learn.StopCriteria(maxSteps = Some(10000)))I removed Error message follows: |
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Using
tf.learn.Conv2Dlayer for MNIST dataset leads to a fatal error somewhere in C++ code of tensorflow library.How to reproduce
Below code leads to an error. This code is based on existing examples of MNIST and CIFAR:
Version:
Error:
Observations:
Above error contains suspicious message about problematic frame:
Some extract from the error log:
The text was updated successfully, but these errors were encountered: