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

colored weights

parent 90a92606

nn_ds_neibs11_tmp.py

+37 −24
Original line number Diff line number Diff line
@@ -1326,20 +1326,28 @@ with tf.Session() as sess: 
    sess.run(tf.global_variables_initializer())

    sess.run(tf.local_variables_initializer())

    

    merged = tf.summary.merge_all()

    

    # display weights, part 1 begin

    import numpy_visualize_weights as npw

    

    #l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))

    #l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))

    # only for SYM8_SUB

    if SYM8_SUB:

        

        l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))

        l1_sym8    = NN_LAYOUT1[l1] // 8

        l1_non_sum = NN_LAYOUT1[l1] % 8

    

        if l1_non_sum==0:

            wimg1_placeholder = tf.placeholder(tf.float32, [1,40,80,3])

            wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder)



    #wimg1_placeholder = tf.placeholder(tf.float32, [1,160,80,3])

    #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)

        

    #wimg2_placeholder = tf.placeholder(tf.float32, [1,120,60,3])

    #wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder)

    # display weights, part 1 end

    

    merged = tf.summary.merge_all()

    train_writer =    tf.summary.FileWriter(TRAIN_PATH, sess.graph)

    test_writer  =    tf.summary.FileWriter(TEST_PATH, sess.graph)

    test_writer1  =   tf.summary.FileWriter(TEST_PATH1, sess.graph)
@@ -1552,24 +1560,29 @@ with tf.Session() as sess: 
             

             

        # 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):

            #w = tf.get_variable('weights',shape=[325,NN_LAYOUT1[l1]])

            #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,...]

        if SYM8_SUB:

        

        #train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)

            #l1 = NN_LAYOUT1.index(next(filter(lambda x: x!=0, NN_LAYOUT1)))

            #l1_sym8    = NN_LAYOUT1[l1] // 8

            #l1_non_sum = NN_LAYOUT1[l1] % 8

            

            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)

        

        #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,...]

            #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)

                    

        #train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)

        # display weights, part 2 end

             

        train_writer.add_summary(train_summary, epoch)