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)
