added script for viewing denoising autoencoder reconstructions

exploring_estimation_criteria/reconstruct_grbm_smd.py

@@ -0,0 +1,74 @@
+#!/bin/env python
+import numpy as N
+import sys
+from pylearn2.utils import serial
+from pylearn2.gui.patch_viewer import PatchViewer
+from pylearn2.config import yaml_parse
+import theano.tensor as T
+from theano import function
+
+model = serial.load(sys.argv[1])
+sigma = float(sys.argv[2])
+model.redo_theano()
+
+n = model.get_input_dim()
+ns = int(N.sqrt(n))
+
+rows = 10
+examplesPerRow = 5
+
+
+if 'dataset_desc' not in dir(model):
+    dataset = yaml_parse.load(model.dataset_yaml_src)
+
+if dataset.view_shape()[2] == 3:
+    print 'n='+str(n)
+    grey_dim = n/3
+    print 'grey_dim = '+str(grey_dim)
+    ns = int(N.sqrt(grey_dim))
+    assert ns*ns == grey_dim
+
+p = PatchViewer((rows,examplesPerRow*5),(ns,ns), is_color = dataset.view_shape()[2] == 3)
+
+def reshape(x):
+    if dataset.view_shape()[2] == 3:
+        fuckyou = [  ]
+        for i in xrange(3):
+            #print 'x shape :'+str(x.shape)
+            #print (i*grey_dim,(i+1)*grey_dim)
+            channel = x[:,i*grey_dim:(i+1)*grey_dim]
+            #print 'channel shape :'+str(channel.shape)
+            fuckyou.append(channel.reshape(ns,ns,1))
+        return N.concatenate( fuckyou    ,axis=2)
+    else:
+        return x.reshape((ns,ns))
+
+F = T.matrix()
+recons_func = function([F], model.energy_function.reconstruct(F))
+
+def reconstruct(X, use_noise):
+
+    corrupt_X = X.copy()
+    if use_noise:
+        noise = N.random.randn(*corrupt_X.shape)
+        scaled_noise = noise * sigma
+        corrupt_X += scaled_noise
+    R = recons_func(corrupt_X)
+
+    return X, corrupt_X, R
+
+for i in range(0,rows):
+    for j in range(0, examplesPerRow):
+        x = dataset.get_batch_design(1)
+
+        p.add_patch( reshape(x),rescale=True)
+        truth, noise, reconstruction = reconstruct(x, use_noise = True)
+
+        p.add_patch(reshape(truth ),rescale=True)
+        p.add_patch(reshape(noise ), rescale=True)
+        p.add_patch(reshape(reconstruction) , rescale=True)
+        print ( 'mse', N.square(reconstruction-truth).mean(), \
+                'mae', N.abs(reconstruction-truth).mean() )
+        truth, noise, reconstruction = reconstruct(x, use_noise = False)
+        p.add_patch( reshape(reconstruction) , rescale=True)
+p.show()