h2 # with tf.variable_scope('two_layers') as scope: # logits1 = two_hidden_layers_1(x1) # # scope.reuse_variables...- # with tf.variable_scope('two_layers') as scope: # logits1 = two_hidden_layers_2(x1) # # scope.reuse_variables...------- with tf.variable_scope('two_layers') as scope: logits1 = two_hidden_layers_3(x1) # scope.reuse_variables
with tf.variable_scope('two_layers') as scope: 43# logits1 = two_hidden_layers_1(x1) 44# # scope.reuse_variables...with tf.variable_scope('two_layers') as scope: 50# logits1 = two_hidden_layers_2(x1) 51# # scope.reuse_variables...------- 55 56with tf.variable_scope('two_layers') as scope: 57logits1 = two_hidden_layers_3(x1) 58# scope.reuse_variables
reduction_indices=None, keep_dims=None ) Others a = tf.Variable() tf.name_scope() 对 tf.get_variable() 无效 scope.reuse_variables
True的地方是 tf.get_variable_scope().reuse_variables() 或者 With tf.variable_scope(name) as scope : Scope.reuse_variables
# 要训练的model # This instructs gen_model to reuse the same variables as the model above scope.reuse_variables...enumerate(inputs): # tstep 多少个时刻,多少个单词 if tstep > 0: scope.reuse_variables
input_vocab_size, num_decoder_symbols=output_vocab_size, embedding_size=embedding_dim) scope.reuse_variables
variable_scope_y') as scope: var1 = tf.get_variable(name='var1', shape=[1], dtype=tf.float32) scope.reuse_variables
v1 == v通过捕获范围和设置重用共享一个变量:with tf.variable_scope("foo") as scope: v = tf.get_variable("v", [1]) scope.reuse_variables
GRU_FORWARD') as scope: for step in range(num_steps): if step > 0: scope.reuse_variables...GRU_BACKWARD') as scope: for step in range(num_steps): if step > 0: scope.reuse_variables
),代码如下: with tf.variable_scope("image_filters") as scope: result1 = my_image_filter(image1) scope.reuse_variables
self.batch_size, 1)) self.D1 = discriminator(self.x, self.mlp_hidden_size)#真实的数据 scope.reuse_variables
False): with tf.variable_scope('generator') as scope: if reuse: scope.reuse_variables...False): with tf.variable_scope('discriminator') as scope: if reuse: scope.reuse_variables
而在重复使用的时候, 一定要在代码中强调 scope.reuse_variables(), 否则系统将会报错, 以为你只是单纯的不小心重复使用到了一个变量 with tf.variable_scope(...) var3 = tf.get_variable(name='var3', shape=[1], dtype=tf.float32, initializer=initializer) scope.reuse_variables
with tf.variable_scope("Lenet") as scope: self.train_digits = self.build(True) scope.reuse_variables
infer_gru.zero_state(batch_size, tf.float32) for iter_step in range(8): if iter_step > 0: scope.reuse_variables
, 10, 'h2') with tf.variable_scope('two_layers') as scope: logits1 = two_hidden_layers(x1) scope.reuse_variables
action_input, reuse=False): with tf.variable_scope(name) as scope: if reuse: scope.reuse_variables
batch_size) # 这非常重要,我们必须设置scope重用变量 # 否则,当我们设置测试网络模型,它会设置新的随机变量,这会使在测试批次上进行随机评估,影响评估结果 scope.reuse_variables
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