Best model for K562 data downsampled to 1M

Downsampling was unbiased, loss function was MSE.

K562/Unbiased_downsampling/1M/K562-10.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8ade915db058f2a614fea8576612eae8cdcae5f53fd3b94bbd39dfc62cb6bbe7
+size 63138304

K562/Unbiased_downsampling/1M/K562-10.json

@@ -0,0 +1 @@
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K562/Unbiased_downsampling/1M/K562-MSELoss-downsampled1M.config

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+UseCustomLoss = 0
+Tasks = K562
+TaskWeights = 1
+Verbose = 2
+NumConvLayers = 5
+KernelSizes = 8,16,32,64,128
+NumKernels = 1024,512,256,128,64
+MaxTrain=1000000
+MaxTest=999999999
+ShouldTest=1
+Epochs = 200
+
+RevComp = 0
+BatchSize = 128
+EarlyStop = 10
+DropoutRate = 0.5
+LearningRate = 0.002
+ConvResidualSkip = 0
+ConvDropout = 1
+ConvPad = same
+DilationFactor = 1
+ConvPoolSize = 1
+GlobalMaxPool = 0
+GlobalAvePool = 1
+NumDense = 0
+DenseSizes = 0
+NumAttentionLayers = 0
+AttentionHeads = 0
+AttentionKeyDim = 0
+AttentionResidualSkip = 0
+
+
+
+