nystrom is now embeded in a fct and in a namespace

main/nystrom/nystrom_approx.py

@@ -93,6 +93,21 @@ def tf_rbf_kernel(X, Y, gamma):
     return K
 
 
+def nystrom_layer(input_x, input_subsample, gamma):
+    with tf.name_scope("nystrom"):
+        init_dim = np.prod([s.value for s in input_x.shape[1:] if s.value is not None])
+        h_conv_flat = tf.reshape(input_x, [-1, init_dim])
+        h_conv_nystrom_subsample_flat = tf.reshape(input_subsample, [NYSTROM_SAMPLE_SIZE, init_dim])
+        with tf.name_scope("kernel_vec"):
+            kernel_vector = tf_rbf_kernel(h_conv_flat, h_conv_nystrom_subsample_flat, gamma=gamma)
+
+        D = weight_variable((NYSTROM_SAMPLE_SIZE,))
+        V = weight_variable((NYSTROM_SAMPLE_SIZE, NYSTROM_SAMPLE_SIZE))
+
+        out_fc = tf.matmul(kernel_vector, tf.matmul(tf.multiply(D, V), tf.transpose(V)))
+    return out_fc
+
+
 def main():
     GAMMA = 0.001
     print("Gamma = {}".format(GAMMA))
@@ -116,16 +131,7 @@ def main():
             scope_conv_mnist.reuse_variables()
             h_conv_nystrom_subsample = convolution_mnist(x_nystrom_image, trainable=False)
 
-        init_dim = np.prod([s.value for s in h_conv.shape[1:] if s.value is not None])
-        h_conv_flat = tf.reshape(h_conv, [-1, init_dim])
-        h_conv_nystrom_subsample_flat = tf.reshape(h_conv_nystrom_subsample, [NYSTROM_SAMPLE_SIZE, init_dim])
-        with tf.name_scope("kernel_vec"):
-            kernel_vector = tf_rbf_kernel(h_conv_flat, h_conv_nystrom_subsample_flat, gamma=GAMMA)
-
-        D = weight_variable((NYSTROM_SAMPLE_SIZE,))
-        V = weight_variable((NYSTROM_SAMPLE_SIZE, NYSTROM_SAMPLE_SIZE))
-
-        out_fc = tf.matmul(kernel_vector, tf.matmul(tf.multiply(D, V), tf.transpose(V)))
+        out_fc = nystrom_layer(h_conv, h_conv_nystrom_subsample, GAMMA)
 
         # classification
         with tf.name_scope("fc_2"):
@@ -177,10 +183,8 @@ def main():
             feed_dict = {x: X_batch, y_: Y_batch, keep_prob: 0.5}
             # le _ est pour capturer le retour de "train_optimizer" qu'il faut appeler
             # pour calculer le gradient mais dont l'output ne nous interesse pas
-            _, loss, y_result, x_exp, k_vec, eigenvec = sess.run([train_optimizer, cross_entropy, y_conv, x_image, kernel_vector, V], feed_dict=feed_dict)
+            _, loss, y_result, x_exp = sess.run([train_optimizer, cross_entropy, y_conv, x_image], feed_dict=feed_dict)
             if i % 100 == 0:
-                print(k_vec[0])
-                print("Difference with identity:", np.linalg.norm(eigenvec - np.eye(*eigenvec.shape)))
                 print('step {}, loss {} (with dropout)'.format(i, loss))
                 r_accuracy = sess.run([accuracy], feed_dict=feed_dict_val)
                 print("accuracy: {} on validation set (without dropout).".format(r_accuracy))
@@ -198,4 +202,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
\ No newline at end of file
+    main()