model.summary() 和 plot_model() 未显示 tensorflow.keras 中的构建模型
model.summary() and plot_model() showing nothing from the built model in tensorflow.keras
我正在测试一些东西,其中包括构建一个 FCNN
网络 动态地 。想法是构建层数及其基于给定列表的神经元,虚拟代码是:
neurons = [10,20,30] # First Dense has 10 neuron, 2nd has 20 and third has 30
inputs = keras.Input(shape=(1024,))
x = Dense(10,activation='relu')(inputs)
for n in neurons:
x = Dense(n,activation='relu')(x)
out = Dense(1,activation='sigmoid')(x)
model = Model(inputs,out)
model.summary()
keras.utils.plot_model(model,'model.png')
for layer in model.layers:
print(layer.name)
令我惊讶的是,它显示 nothing.I 甚至编译并再次 运行 函数,但没有任何结果。
model.summary
始终显示可训练和不可训练参数的数量,但不显示模型结构和层名称。为什么会这样?或者这是正常的?
关于 model.summary()
,不要同时混合使用 tf 2.x
和独立 keras。如果我 运行 你在 tf 2.x
中建模,我会得到预期的结果。
from tensorflow.keras.layers import *
from tensorflow.keras import Model
from tensorflow import keras
# your code ...
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 1024)] 0
_________________________________________________________________
dense (Dense) (None, 10) 10250
_________________________________________________________________
dense_1 (Dense) (None, 10) 110
_________________________________________________________________
dense_2 (Dense) (None, 20) 220
_________________________________________________________________
dense_3 (Dense) (None, 30) 630
_________________________________________________________________
dense_4 (Dense) (None, 1) 31
=================================================================
Total params: 11,241
Trainable params: 11,241
Non-trainable params: 0
_________________________________
关于绘制模型,在绘制 keras 模型时可以使用几个选项。这是一个例子:
keras.utils.plot_model(model, show_dtype=True,
show_layer_names=True, show_shapes=True,
to_file='model.png')
我正在测试一些东西,其中包括构建一个 FCNN
网络 动态地 。想法是构建层数及其基于给定列表的神经元,虚拟代码是:
neurons = [10,20,30] # First Dense has 10 neuron, 2nd has 20 and third has 30
inputs = keras.Input(shape=(1024,))
x = Dense(10,activation='relu')(inputs)
for n in neurons:
x = Dense(n,activation='relu')(x)
out = Dense(1,activation='sigmoid')(x)
model = Model(inputs,out)
model.summary()
keras.utils.plot_model(model,'model.png')
for layer in model.layers:
print(layer.name)
令我惊讶的是,它显示 nothing.I 甚至编译并再次 运行 函数,但没有任何结果。
model.summary
始终显示可训练和不可训练参数的数量,但不显示模型结构和层名称。为什么会这样?或者这是正常的?
关于 model.summary()
,不要同时混合使用 tf 2.x
和独立 keras。如果我 运行 你在 tf 2.x
中建模,我会得到预期的结果。
from tensorflow.keras.layers import *
from tensorflow.keras import Model
from tensorflow import keras
# your code ...
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 1024)] 0
_________________________________________________________________
dense (Dense) (None, 10) 10250
_________________________________________________________________
dense_1 (Dense) (None, 10) 110
_________________________________________________________________
dense_2 (Dense) (None, 20) 220
_________________________________________________________________
dense_3 (Dense) (None, 30) 630
_________________________________________________________________
dense_4 (Dense) (None, 1) 31
=================================================================
Total params: 11,241
Trainable params: 11,241
Non-trainable params: 0
_________________________________
关于绘制模型,在绘制 keras 模型时可以使用几个选项。这是一个例子:
keras.utils.plot_model(model, show_dtype=True,
show_layer_names=True, show_shapes=True,
to_file='model.png')