我想查看我的Keras模型的可训练权重值,以确定是否存在大片的零或一。我的Keras使用tensorflow后端,在docker镜像中运行,并从jupyter笔记本中运行。以下是我所做的工作。print(model.summary())将产生所有可训练参数的列表。
我该如何打印这些变量的值以查看有多少个出现了0、1或无穷大等异常值?
_____________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) (None, 512, 512, 3) 0
_________________________________________________________________
conv2d_1 (Conv2D) (None, 512, 512, 16) 448
_________________________________________________________________
activation_1 (Activation) (None, 512, 512, 16) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 512, 512, 16) 64
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 256, 256, 16) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 256, 256, 32) 4640
model.trainable_weights让我可以查看底层的tensorflow变量。
[<tf.Variable 'conv2d_1/kernel:0' shape=(3, 3, 3, 16) dtype=float32_ref>,
<tf.Variable 'conv2d_1/bias:0' shape=(16,) dtype=float32_ref>,
<tf.Variable 'batch_normalization_1/gamma:0' shape=(16,) dtype=float32_ref>,
<tf.Variable 'batch_normalization_1/beta:0' shape=(16,) dtype=float32_ref>,
<tf.Variable 'conv2d_2/kernel:0' shape=(3, 3, 16, 32) dtype=float32_ref>,
<tf.Variable 'conv2d_2/bias:0' shape=(32,) dtype=float32_ref>,
我该如何打印这些变量的值以查看有多少个出现了0、1或无穷大等异常值?