tweaks

vision/convmixer_cifar10/convmixer.jl

@@ -1,8 +1,12 @@
 using Flux, MLDatasets
 using Flux: onehotbatch, onecold, DataLoader, flatten, OptimiserChain
 using BSON:@save,@load
-Flux._old_to_new(rule::ClipNorm) = Flux.Optimisers.ClipNorm(rule.thresh)  # wrong in Flux 0.13.9
+ENV["DATADEPS_ALWAYS_ACCEPT"] = "true" 
 
+# wrong in Flux 0.13.9
+Flux._old_to_new(rule::ClipNorm) = Flux.Optimisers.ClipNorm(rule.thresh) 
+
+# Also, quick test of train(epochs=10, images=128) shows increasing loss, not sure why.
 
 function ConvMixer(in_channels, kernel_size, patch_size, dim, depth, N_classes)
     f = Chain(
@@ -22,19 +26,19 @@ function ConvMixer(in_channels, kernel_size, patch_size, dim, depth, N_classes)
     return f
 end
 
-function get_data(batchsize; dataset = MLDatasets.CIFAR10, idxs = nothing)
-    """
-    idxs=nothing gives the full dataset, otherwise (for testing purposes) only the 1:idxs elements of the train set are given.
-    """
-    ENV["DATADEPS_ALWAYS_ACCEPT"] = "true" 
+"""
+By default gives the full dataset, keyword images gives (for testing purposes) 
+only the 1:images elements of the train set.
+"""
+function get_data(batchsize; dataset = MLDatasets.CIFAR10, images = :)
 
     # Loading Dataset
-    if idxs===nothing
+    if images === (:)
         xtrain, ytrain = dataset(:train)[:]
         xtest, ytest = dataset(:test)[:]
 	else
-        xtrain, ytrain = dataset(:train)[1:idxs]
-        xtest, ytest = dataset(:test)[1:Int(idxs/10)]
+        xtrain, ytrain = dataset(:train)[1:images]
+        xtest, ytest = dataset(:test)[1:(images÷10)]
     end
 
     # Reshape Data to comply to Julia's (width, height, channels, batch_size) convention in case there are only 1 channel (eg MNIST)
@@ -74,10 +78,10 @@ function create_loss_function(dataloader, device)
 end
 
 
-function train(n_epochs=100)
+function train(; epochs=100, images=:)
 
     #params: warning, the training can be long with these params
-    train_loader, test_loader = get_data(128)
+    train_loader, test_loader = get_data(128; images)
     η = 3f-4
     in_channel = 3
     patch_size = 2
@@ -88,8 +92,8 @@ function train(n_epochs=100)
     use_cuda = true
 
     #logging the losses
-    train_save = zeros(n_epochs, 2)
-    test_save = zeros(n_epochs, 2)
+    train_save = zeros(epochs, 2)
+    test_save = zeros(epochs, 2)
 
     if use_cuda
         device = gpu
@@ -111,11 +115,11 @@ function train(n_epochs=100)
             )
     state = Flux.setup(opt, model)
 
-    for epoch in 1:n_epochs
+    for epoch in 1:epochs
         for (x,y) in train_loader
             x,y = x|>device, y|>device
             grads = gradient(m->Flux.logitcrossentropy(m(x), y, agg=sum), model)
-            Flux.Optimise.update!(state, model, grads[1])
+            Flux.update!(state, model, grads[1])
         end
 
         #logging