diff --git a/examples/MNIST.jl b/examples/MNIST.jl
index 70d26ee1..fbf61150 100644
--- a/examples/MNIST.jl
+++ b/examples/MNIST.jl
@@ -1,6 +1,6 @@
 using Flux, MNIST
 
-data = [(Vector{Float32}(trainfeatures(i)), onehot(Float32, trainlabel(i), 0:9)) for i = 1:60_000]
+data = [(trainfeatures(i), onehot(trainlabel(i), 0:9)) for i = 1:60_000]
 train = data[1:50_000]
 test = data[50_001:60_000]
 
@@ -16,7 +16,7 @@ model = tf(m)
 # An example prediction pre-training
 model(data[1][1])
 
-@time Flux.train!(model, train, test, η = 1e-3)
+@time Flux.train!(model, train, test, η = 1e-4)
 
 # An example prediction post-training
 model(data[1][1])
diff --git a/src/backend/tensorflow/model.jl b/src/backend/tensorflow/model.jl
index 970e5c97..f9eaa9cf 100644
--- a/src/backend/tensorflow/model.jl
+++ b/src/backend/tensorflow/model.jl
@@ -80,7 +80,8 @@ function Flux.train!(m::Model, train, test=[]; epoch = 1, η = 0.1,
     info("Epoch $e\n")
     @progress for (x, y) in train
       y, cur_loss, _ = run(m.session, vcat(m.output, Loss, minimize_op),
-                           Dict(m.inputs[1]=>batchone(x), Y=>batchone(y)))
+                           Dict(m.inputs[1] => batchone(convertel(Float32, x)),
+                                Y => batchone(convertel(Float32, y))))
       if i % 5000 == 0
         @show y
         @show accuracy(m, test)
diff --git a/src/utils.jl b/src/utils.jl
index 3a36b861..d1b81347 100644
--- a/src/utils.jl
+++ b/src/utils.jl
@@ -2,7 +2,7 @@ export AArray
 
 const AArray = AbstractArray
 
-initn(dims...) = randn(dims...)/10
+initn(dims...) = randn(dims...)/100
 
 function train!(m, train, test = []; epoch = 1, batch = 10, η = 0.1)
     i = 0