有种更简洁 的方法来一次性对variable_scope进行reuse,现将代码模板总结如下:使用 tf.Variable_scope(…, reuse=tf.AUTO_REUSE)代码# -*- coding...tensorflow as tfdef func(in_put, in_channel, out_channel): with tf.variable_scope(name_or_scope='', reuse=tf.AUTO_REUSE
MLP d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att', reuse=tf.AUTO_REUSE...d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att', reuse=tf.AUTO_REUSE...) d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att', reuse=tf.AUTO_REUSE...dice(_x, axis=-1, epsilon=0.000000001, name=''): with tf.variable_scope(name_or_scope='', reuse=tf.AUTO_REUSE...- x_p) * _x + x_p * _x def parametric_relu(_x): with tf.variable_scope(name_or_scope='', reuse=tf.AUTO_REUSE
., reuse=tf.AUTO_REUSE)。....): with tf.variable_scope(name_or_scope='', reuse=tf.AUTO_REUSE): ### 改动部分 ### pass...as tf def func(in_put, in_channel, out_channel): with tf.variable_scope(name_or_scope='', reuse=tf.AUTO_REUSE
__init__() self.units = int(out_channels / 2) with tf.variable_scope(name, reuse=tf.AUTO_REUSE...self.dense = tf.layers.Dense( self.units, tf.nn.relu, name='dense', _reuse=tf.AUTO_REUSE...self.batch_norm = tf.layers.BatchNormalization( name='batch_norm', fused=True, _reuse=tf.AUTO_REUSE...tf.layers.batch_normalization( temp_conv, axis=-1, fused=True, training=training, reuse=tf.AUTO_REUSE...tf.layers.batch_normalization( temp_conv, axis=-1, fused=True, training=training, reuse=tf.AUTO_REUSE
alpha不更新,退化成Leak with tf.variable_scope(name_or_scope='', reuse=tf.AUTO_REUSE): alphas = tf.get_variable...='auxiliary_net'): bn1 = tf.layers.batch_normalization(inputs=in_, name='bn1' + stag, reuse=tf.AUTO_REUSE...) dnn1 = tf.layers.dense(bn1, 100, activation=None, name='f1' + stag, reuse=tf.AUTO_REUSE)...tf.nn.sigmoid(dnn1) dnn2 = tf.layers.dense(dnn1, 50, activation=None, name='f2' + stag, reuse=tf.AUTO_REUSE...tf.nn.sigmoid(dnn2) dnn3 = tf.layers.dense(dnn2, 2, activation=None, name='f3' + stag, reuse=tf.AUTO_REUSE
assert v.name == "foo/bar/v:0"共享一个变量AUTO_REUSE的基本例子:def foo(): with tf.variable_scope("foo", reuse=tf.AUTO_REUSE...reuse: True、None或tf.AUTO_REUSE;如果为真,则进入此范围以及所有子范围的重用模式;如果特遣部队。...当启用了即时执行时,这个参数总是强制为tf.AUTO_REUSE。dtype: 在此范围中创建的变量的类型(默认为传递的范围中的类型,或从父范围继承的类型)。
def layer_norm(x): with tf.variable_scope('layer_normalization', reuse=tf.AUTO_REUSE): d_model...= Relu(h_iw_1 + b_1)w_2 + b_2 \] def ffn(x, params, mode): with tf.variable_scope('ffn', reuse=tf.AUTO_REUSE...def encode(self, features, mode): with tf.variable_scope('encoding', reuse=tf.AUTO_REUSE):...encode_attention_layers']): with tf.variable_scope('self_attention_layer_{}'.format(i), reuse=tf.AUTO_REUSE...): with tf.variable_scope('self_attention', reuse=tf.AUTO_REUSE): decoder_input
as tf def func(in_put, layer_name, is_training=True): with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE...as tf def func(in_put, layer_name, is_training=True): with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE
inputs, filters, k_size, stride, padding, scope_name): with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE...inputs, ksize, stride, padding='VALID', scope_name='pool'): with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE...def fully_connected(inputs, out_dim, scope_name='fc'): with tf.variable_scope(scope_name, reuse=tf.AUTO_REUSE
())[0] r = w * x + b return r, w, b def func(x): with tf.variable_scope('op1', reuse=tf.AUTO_REUSE...): r1 = my_func(x) with tf.variable_scope('op2', reuse=tf.AUTO_REUSE): r2 = my_func
stag='auxiliary_net'): bn1 = tf.layers.batch_normalization(inputs=in_, name='bn1' + stag, reuse=tf.AUTO_REUSE...) dnn1 = tf.layers.dense(bn1, 100, activation=None, name='f1' + stag, reuse=tf.AUTO_REUSE) dnn1...= tf.nn.sigmoid(dnn1) dnn2 = tf.layers.dense(dnn1, 50, activation=None, name='f2' + stag, reuse=tf.AUTO_REUSE...= tf.nn.sigmoid(dnn2) dnn3 = tf.layers.dense(dnn2, 2, activation=None, name='f3' + stag, reuse=tf.AUTO_REUSE
Did you mean to set reuse=True or reuse=tf.AUTO_REUSE in VarScope?
1) batch_size = tf.shape(task_ids)[0] with tf.variable_scope(params['task_list'][0], reuse=tf.AUTO_REUSE...tf.summary.scalar('loss', loss1) with tf.variable_scope(params['task_list'][1], reuse=tf.AUTO_REUSE..., 1) batch_size = tf.shape(task_ids)[0] with tf.variable_scope('task_discriminator', reuse=tf.AUTO_REUSE...tf.summary.scalar('loss', adv_loss) with tf.variable_scope('task1_{}'.format(params['task_list'][0]), reuse=tf.AUTO_REUSE...tf.summary.scalar('loss', loss1) with tf.variable_scope('task2_{}'.format(params['task_list'][1]), reuse=tf.AUTO_REUSE
ReLu )旨在为不同通道的输入学习不同的权值a # input表示待激活的张量 def prelu(inp,name): with tf.variable_scope(name,reuse=tf.AUTO_REUSE
stag='auxiliary_net'): bn1 = tf.layers.batch_normalization(inputs=input, name='bn1' + stag, reuse=tf.AUTO_REUSE...) dnn1 = tf.layers.dense(bn1, 100, activation=None, name='f1' + stag, reuse=tf.AUTO_REUSE) dnn1...= tf.nn.sigmoid(dnn1) dnn2 = tf.layers.dense(dnn1, 50, activation=None, name='f2' + stag, reuse=tf.AUTO_REUSE...= tf.nn.sigmoid(dnn2) dnn3 = tf.layers.dense(dnn2, 2, activation=None, name='f3' + stag, reuse=tf.AUTO_REUSE
layer_name, is_training=True, decay=0.9): # 新添了一个接口的参数项:decay with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE...) as scope: # tf.variable_scope() 中添加了 reuse=tf.AUTO_REUSE 参数项 weights = tf.get_variable(name...in_channel, out_channel, layer_name, is_training=True): with tf.variable_scope(layer_name, reuse=tf.AUTO_REUSE
axis=-1) d_layer_1_all = tf.layers.dense(din_all, 80, activation=tf.nn.sigmoid, name='f1_att', reuse=tf.AUTO_REUSE...d_layer_2_all = tf.layers.dense(d_layer_1_all, 40, activation=tf.nn.sigmoid, name='f2_att', reuse=tf.AUTO_REUSE...) d_layer_3_all = tf.layers.dense(d_layer_2_all, 1, activation=None, name='f3_att', reuse=tf.AUTO_REUSE
(10)(_input) # tmp = tf.keras.layers.Dense(24)(tmp) # with tf.variable_scope('vad_gru', reuse=tf.AUTO_REUSE
aggregation=tf.VariableAggregation.NONE)下面是一个基本的例子:def foo(): with tf.variable_scope("foo", reuse=tf.AUTO_REUSE
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