y=hidden_layer(xs, weight1, bias1, weight2, bias2, weight3, bias3) # 定义损失函数 error_loss= tf.reduce_sum(tf.pow(ys-y,2)) tf.add_to_collection("losses", error_loss) # 参数L2正则化 regularizer= tf.contrib.layers.l2_regularizer(0.01) retularization= regularizer(weight1) + regula...
tf.contrib.layers.l2_regularizer importtensorflowastfimporttensorflow.contribascontrib weight = tf.constant([[1.0, -2.0], [-3.0,4.0]])withtf.Session()assess:# 输出为(|1|+|-2|+|-3|+|4|)*0.5=5print(sess.run(contrib.layers.l1_regularizer(0.5)(weight)))# 输出为(1²+(-2)²+(-3...
第一层是L3(一个或者2个),第二层是L2块,第三层是L3块。具体关系是L3包含一个或者多个L2,L2包含多个或者一个L1,L1里面存放许多的数据块。 1.查找dba_segment,确定段头块 2.在段头块位置找到L... 和尚也爱看AV 0 342 《开发板 — 格式化TF卡》 2019-12-06 15:15 − 1.格式化TF卡 mkfs.ext4...
weights_regularizer = tf.contrib.layers.l2_regularizer(weight_decay) with tf.variable_scope("InceptionV3", "InceptionV3", [images]) as scope: with tf.contrib.slim.arg_scope( [tf.contrib.slim.conv2d, tf.contrib.slim.fully_connected], weights_regularizer=weights_regularizer, trainable=True): wi...
对于axis = 0的1-D张量,计算如下: output = x / sqrt(max(sum(x**2), epsilon)) 1 对于具有更多维度的x,沿着维度axis独立地规范化每个1-D切片. 使用 tf.math.l2_normalize( x, axis=None, epsilon=1e-12, name=None, dim=None ) 1 2 3 4 5 6 7 参数 x: 输入的向量 axis: 要标准化的...
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 可以看到在eager执行下,每个操作后的返回值是tf.Tensor,其包含具体值,不再像Graph模式下那样只是一个计算图节点的符号句柄。由于可以立即看到结果,这非常有助于程序debug。更进一步地,调用tf.Tensor.numpy()方法可以获得Tensor所对应的numpy数组。
weights_regularizer=tf.contrib.layers.l2_regularizer(scale=1.0), biases_initializer=Noneifwith_bnelsetf.zeros_initializer(), biases_regularizer=Noneifwith_bnelsetf.contrib.layers.l2_regularizer( scale=1.0), reuse=reuse, scope=name)returnbatch_normalization(conv2d, is_training, name +'_bn', reuse...
l2_regularizer(0.05)): net = layers.conv2d(inputs, 64, [11, 11], 4, padding='VALID', scope='conv1') net = layers.conv2d(net, 256, [5, 5], scope='conv2') # 其中,第一个conv2d相当于: layers.conv2d(inputs, 64, [11, 11], 4, padding='VALID', initializer=layers.variance_...
out = layers.fully_connected(out, num_outputs=200, weights_initializer = layers.xavier_initializer(uniform=True), weights_regularizer = layers.l2_regularizer(scale=reg), activation_fn = tf.nn.tanh) out = layers.fully_connected(out, num_outputs=200, ...
xavier_initializer(uniform=True), weights_regularizer = layers.l2_regularizer(scale=reg), activation_fn = tf.nn.tanh) out = layers.fully_connected(out, num_outputs=200, weights_initializer = layers.xavier_initializer(uniform=True), weights_regularizer = layers.l2_regularizer(scale=reg), ...