Commit f964c4f2 authored by Oleg Dzhimiev's avatar Oleg Dzhimiev
Browse files

subbed 325 with corr2d_Nx325.shape[-1]

parent 9a1886bd

nn_ds_neibs11_tmp.py

+6 −4
Original line number Original line Diff line number Diff line
@@ -777,9 +777,9 @@ def network_summary_w_b(scope, in_shape, out_shape, layout, index, network_scope 
    # the scope is known

    # the scope is known

    with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):

    with tf.variable_scope(scope,reuse=tf.AUTO_REUSE):

        # histograms

        # histograms

        print("Specified shape: "+str(in_shape)+","+str(out_shape))

        #print("Specified shape: "+str(in_shape)+","+str(out_shape))

        print("Index: "+str(index))

        #print("Index: "+str(index))

        print("Layout: "+str(layout))

        #print("Layout: "+str(layout))

        

        

        w = tf.get_variable('weights',shape=[in_shape,out_shape])

        w = tf.get_variable('weights',shape=[in_shape,out_shape])

        b = tf.get_variable('biases',shape=[out_shape])

        b = tf.get_variable('biases',shape=[out_shape])
@@ -1583,9 +1583,11 @@ with tf.Session() as sess: 
            #l1_sym8    = NN_LAYOUT1[l1] // 8

            #l1_sym8    = NN_LAYOUT1[l1] // 8

            #l1_non_sum = NN_LAYOUT1[l1] % 8

            #l1_non_sum = NN_LAYOUT1[l1] % 8

                        

                        

            #print("corr2d_Nx325 shape = "+str(corr2d_Nx325.shape))

                        

            if l1_non_sum==0:

            if l1_non_sum==0:

                with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):

                with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):

                    w = tf.get_variable('weights',shape=[325,l1_sym8])

                    w = tf.get_variable('weights',shape=[corr2d_Nx325.shape[-1],l1_sym8])

                    w = tf.transpose(w,(1,0))            

                    w = tf.transpose(w,(1,0))            

                    img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=ZERO_SPAN1),(1,TILE_LAYERS,tile_side1,tile_side1),tiles_per_line=TILES_PER_LINE1,borders=True)

                    img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=ZERO_SPAN1),(1,TILE_LAYERS,tile_side1,tile_side1),tiles_per_line=TILES_PER_LINE1,borders=True)

                    img1 = img1[np.newaxis,...]

                    img1 = img1[np.newaxis,...]