with tf.variable_scope('eval_net'): e1 =
with tf.Session(graph = g1) as sess: tf.global_variables_initializer().run() with tf.variable_scope("", reuse=True): print(sess.run(tf.get_variable("v"))) with tf.Session(graph = g2) as sess: tf.global_variables_initializer().run() with tf.variable_scope("", reuse=True): pr...
with tf.variable_scope('generator', reuse=notis_train):#100 -> 4*4*512 - > 4, 4, 512#进行全连接操作,将100的数据通过全连接,转换为[None ,4*4*512]layer1 = tf.layers.dense(noise_img, 4*4*512)#进行维度变化,将其转换为[-1, 4, 4, 512]layer1 = tf.reshape(layer1, [-1, 4, ...
所以我们使用这个技巧: x = tf.Variable(0.0) x_plus_1 = tf.assign_add(x, 1) with tf.control_dependencies([x_plus_1]): y = tf.identity(x) init = tf.initialize_all_variables() with tf.Session() as session: init.run() for i in xrange(5): print(y.eval()) 这有效:它打印 1...
[3, 4, 5]#构造池化结果的列表pool1_output =[]#循环不同卷积核大小forconv_sizeinconv_sizes:#构造with.name_scope参数的作用范围with tf.name_scope('conv-padding%d'%(conv_size, )):#构造卷积参数的W,维度为[conv_size, embedding_size, 1, filter_num]W = tf.Variable(tf.random_uniform([conv...
x = tf.Variable(0.0) x_plus_1 = tf.assign_add(x, 1) with tf.control_dependencies([x_plus_1]): y = x init = tf.initialize_all_variables() with tf.Session() as session: init.run() for i in xrange(5): print(y.eval()) 它不会:它会打印 0、0、0、0、0。相反,我们似乎需要...
(noise_img, output_dim, is_train, alpha=0.01): with tf.variable_scope('generator', reuse=notis_train):#100 -> 4*4*512 - > 4, 4, 512#进行全连接操作,将100的数据通过全连接,转换为[None ,4*4*512]layer1 = tf.layers.dense(noise_img, 4*4*512)#进行维度变化,将其转换为[-1, 4,...