在 tensorflow 中查看神经网络中的各个组件

Question

实际上有没有办法查看神经网络中的各个组件？ 假设下面的代码在 tensorflow 中。 如何查看每一层的内容、神经元和权重？

# Create a `Sequential` model and add a Dense layer as the first layer.
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(32, activation='relu'))
# Now the model will take as input arrays of shape (None, 16)
# and output arrays of shape (None, 32).
# Note that after the first layer, you don't need to specify
# the size of the input anymore:
model.add(tf.keras.layers.Dense(32))
model.output_shape

Answer 1

您可以为图层命名。 您的代码如下所示：

model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(32, activation='relu'), name="layer_1")
# Now the model will take as input arrays of shape (None, 16)
# and output arrays of shape (None, 32).
# Note that after the first layer, you don't need to specify
# the size of the input anymore:
model.add(tf.keras.layers.Dense(32), name="layer_1")
model.output_shape

如果这样做，您可以通过执行访问层权重

model.get_layer("layer_1").weights

因此，通过这种方式，您可以打印图层的权重。