run with train instead of test to eval

just to make sure it's not some weird bug with that part.

tensorboard log-dir: log_data/May_18/May_18_23:19:37/

pcanet.py

@@ -94,7 +94,6 @@ def __init__(self, image_batch, hyperparams, info):
             self.binary_quantize_viz = tf.reshape(tf.expand_dims(self.binary_quantize, axis=4), [-1, info.IMAGE_W, info.IMAGE_H, 1])
             self.binary_encoded_viz = tf.expand_dims(self.binary_encoded[:, 1, :, :], axis=3)
             tf.summary.image('quantized', self.binary_quantize_viz, max_outputs=10)
-            tf.summary.image('encoded', self.binary_encoded_viz, max_outputs=10)
 
         with tf.name_scope("histograms"):
             self.n_bins = k = pow(2, l2)
@@ -199,14 +198,13 @@ def main():
     train_summary = tf.summary.merge_all('train')
     test_summary = tf.summary.merge_all('test')
 
+    # q = sess.run(m.binary_encoded)
+    # print(q[0])
+
     # extract PCA features from training set
     train_pcanet_features, train_labels, summary = sess.run([m.output_features, train_label_batch, merged_summary])
     writer.add_summary(summary, 0)
 
-    # q = sess.run(m.x_eig1)
-    # np.savetxt('eig.csv', np.squeeze(q))
-    # exit(0)
-
     # train linear SVM
     svm = LinearSVC(C=1, fit_intercept=False)
     svm.fit(train_pcanet_features, train_labels)
@@ -219,11 +217,12 @@ def main():
     # switch to test set, compute PCA filters, and score with learned SVM parameters
     scores = []
     test_labels = sess.run(test_label_batch)
-    m.image_batch = test_image_batch
+    # m.image_batch = test_image_batch
     for i in range(4):
         test_pcanet_features = sess.run(m.output_features)
 
-        score = svm.score(test_pcanet_features, test_labels)
+        # score = svm.score(test_pcanet_features, test_labels)
+        score = svm.score(train_pcanet_features, train_labels)
         scores.append(score)
 
         print("batch test score:", score)