y_dim=6, dataset_name='AmazonFashion6ImgPartitioned.npy', input_fname_pattern='*,jpg', crop='False', checkpoint_dir='../GAN/checkpoint', sample_dir='.') dcgan.load('../GAN/checkpoint') with tf.device('/gpu:0'): x=np.random.normal(0,0.5,size=[16,256]) z=tf.Variable(x,name='input_code',dtype=tf.float32) y=tf.placeholder(dtype=tf.float32,shape=[16,6]) gan_image=dcgan.get_gen(z, y) gan_rf=dcgan.get_dis(gan_image, y) image=tf.image.resize_nearest_neighbor(images=gan_image, size=[224,224], align_corners=None, name=None) with tf.variable_scope("DVBPR") as scope: scope.reuse_variables() result = CNN(image,1.0) lamda=1.0 user=tf.placeholder(dtype=tf.int32,shape=[1]) with tf.variable_scope('opt'): obj=tf.reduce_mean(tf.matmul(result,tf.transpose(tf.gather(thetau,user))))-tf.reduce_mean(tf.square(gan_rf-1))*lamda optimizer = tf.train.AdamOptimizer(learning_rate=0.05).minimize(-obj,var_list=[z]) idx=tf.reduce_sum(tf.matmul(result,tf.transpose(tf.gather(thetau,user))),1)