Commit 7a9d4e6e authored by Oleg Dzhimiev's avatar Oleg Dzhimiev

colored weights

parent 90a92606
...@@ -1326,20 +1326,28 @@ with tf.Session() as sess: ...@@ -1326,20 +1326,28 @@ with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer()) sess.run(tf.local_variables_initializer())
merged = tf.summary.merge_all()
# display weights, part 1 begin # display weights, part 1 begin
import numpy_visualize_weights as npw import numpy_visualize_weights as npw
#l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1))) # only for SYM8_SUB
#l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2))) if SYM8_SUB:
#wimg1_placeholder = tf.placeholder(tf.float32, [1,160,80,3]) l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
#wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder) l1_sym8 = NN_LAYOUT1[l1] // 8
l1_non_sum = NN_LAYOUT1[l1] % 8
#wimg2_placeholder = tf.placeholder(tf.float32, [1,120,60,3]) if l1_non_sum==0:
#wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder) wimg1_placeholder = tf.placeholder(tf.float32, [1,40,80,3])
wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder)
l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
wimg2_placeholder = tf.placeholder(tf.float32, [1,250,100,3])
wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder)
# display weights, part 1 end # display weights, part 1 end
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph) train_writer = tf.summary.FileWriter(TRAIN_PATH, sess.graph)
test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph) test_writer = tf.summary.FileWriter(TEST_PATH, sess.graph)
test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph) test_writer1 = tf.summary.FileWriter(TEST_PATH1, sess.graph)
...@@ -1552,24 +1560,29 @@ with tf.Session() as sess: ...@@ -1552,24 +1560,29 @@ with tf.Session() as sess:
# display weights, part 2 begin # display weights, part 2 begin
#l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
#l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
#with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE): if SYM8_SUB:
#w = tf.get_variable('weights',shape=[325,NN_LAYOUT1[l1]])
#w = tf.transpose(w,(1,0)) #l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))
#img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True) #l1_sym8 = NN_LAYOUT1[l1] // 8
#img1 = img1[np.newaxis,...] #l1_non_sum = NN_LAYOUT1[l1] % 8
#train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)
#with tf.variable_scope('g_fc_inter'+str(l2),reuse=tf.AUTO_REUSE):
#w = tf.get_variable('weights',shape=[144,NN_LAYOUT1[l2]])
#w = tf.transpose(w,(1,0))
#img2 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(3,3,4,4),tiles_per_line=4,borders=True)
#img2 = img2[np.newaxis,...]
#train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch) if l1_non_sum==0:
with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[325,l1_sym8])
w = tf.transpose(w,(1,0))
img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True)
img1 = img1[np.newaxis,...]
train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)
#l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))
with tf.variable_scope('g_fc_inter'+str(l2),reuse=tf.AUTO_REUSE):
w = tf.get_variable('weights',shape=[400,NN_LAYOUT2[l2]])
w = tf.transpose(w,(1,0))
img2 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(5,5,4,4),tiles_per_line=4,borders=True)
img2 = img2[np.newaxis,...]
train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)
# display weights, part 2 end # display weights, part 2 end
train_writer.add_summary(train_summary, epoch) train_writer.add_summary(train_summary, epoch)
......
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