Introducing test script (#1)

test/runtests.jl

@@ -1,3 +1,4 @@
 
-include("testrbm.jl")
-include("testnets.jl")
+# include("testrbm.jl")
+# include("testnets.jl")
+include("testdropout.jl")

test/testdropout.jl

@@ -0,0 +1,54 @@
+
+using Boltzmann
+using MNIST
+using ImageView
+using Gadfly
+using DataFrames
+
+function plot_weights(W, imsize, padding=10)
+    h, w = imsize
+    n = size(W, 1)
+    rows = int(floor(sqrt(n)))
+    cols = int(ceil(n / rows))
+    halfpad = div(padding, 2)
+    dat = zeros(rows * (h + padding), cols * (w + padding))
+    for i=1:n
+        wt = W[i, :]
+        wim = reshape(wt, imsize)
+        wim = wim ./ (maximum(wim) - minimum(wim))
+        r = div(i - 1, cols) + 1
+        c = rem(i - 1, cols) + 1
+        dat[(r-1)*(h+padding)+halfpad+1 : r*(h+padding)-halfpad,
+            (c-1)*(w+padding)+halfpad+1 : c*(w+padding)-halfpad] = wim
+    end
+    view(dat)
+    return dat
+end
+
+
+function run_mnist()
+    # Configure Test
+    X, y = testdata()  
+    HiddenUnits = 100
+    Epochs = 5    
+    X = X ./ (maximum(X) - minimum(X))
+    m_do = BernoulliRBM(28*28, HiddenUnits)     
+    m = BernoulliRBM(28*28, HiddenUnits)     
+
+    # Fit Models
+    m_do, historical_pl_do = fit(m_do, X; persistent=true, lr=0.1, n_iter=Epochs, batch_size=100, n_gibbs=1, dorate=0.5)
+    m, historical_pl = fit(m, X; persistent=true, lr=0.1, n_iter=Epochs, batch_size=100, n_gibbs=1, dorate=0.0)
+
+    # Put results in dataframe
+    Results = DataFrame(Epochs=[1:Epochs;1:Epochs],PL=[vec(historical_pl_do);vec(historical_pl)],UsingDropout=[trues(Epochs);falses(Epochs)])
+
+    PLPlot = plot(Results,x="Epochs",y="PL",color="UsingDropout",Geom.line,Guide.ylabel("Pseudo-Liklihood"),Guide.xlabel("Training Epoch"))
+    draw(PDF("Dropout_TrainingPL.pdf", 4inch, 3inch), PLPlot)
+
+
+
+    return m
+end
+
+run_mnist()
+