實際上有沒有辦法查看神經網路中的各個組件？假設下面的代碼在 tensorflow 中。如何查看每一層的內容、神經元和權重？
# 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
uj5u.com熱心網友回復：
您可以為圖層命名。您的代碼如下所示：
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
如果這樣做，您可以通過執行訪問層權重
model.get_layer("layer_1").weights
因此，通過這種方式，您可以列印圖層的權重。
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上一篇：Keras錯誤：尺寸必須相等，但對于輸入形狀為[8,10]、[8,2]的“loss/output_1_loss/SquaredDifference”，尺寸為10和2