我已经为数字分类实施了CNN
模型。我的模型过度拟合,为了克服过度拟合,我试图在我的成本函数中使用L2 Regularization
。我有一点困惑
如何选择<weights>
以放入成本等式(代码的最后一行)。
...
x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, num_channels], name='x') # Input
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true') # Labels
<Convolution Layer 1>
<Convolution Layer 2>
<Convolution Layer 3>
<Fully Coonected 1>
<Fully Coonected 2> O/P = layer_fc2
# Loss Function
lambda = 0.01
cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2, labels=y_true)
# cost = tf.reduce_mean(cross_entropy) # Nornmal Loss
cost = tf.reduce_mean(cross_entropy + lambda * tf.nn.l2_loss(<weights>)) # Regularized Loss
...
答案 0 :(得分:3)
您应该根据权重定义L2损失 - 使用trainable_variables
:
C = tf.nn.softmax_cross_entropy_with_logits(logits=layer_fc2, labels=y_true)
l2_loss = tf.add_n([tf.nn.l2_loss(v) for v in tf.trainable_variables()])
C = C + lambda * l2_loss