【发布时间】：2022-01-18 15:21:43
【问题描述】：
我正在尝试/摆弄/学习一些小的 ML 问题。
我有一个基于预训练卷积基础的加载模型，其中包含一些自训练的密集层（有关模型详细信息，请参见下文）。
我想尝试在模型上应用一些可视化，例如激活和 Grad CAM 可视化 (https://www.statworx.com/de/blog/erklaerbbarkeit-von-deep-learning-modellen-mit-grad-cam/)。但我做不到。
我试图创建一个基于我的新模型（如文章中所示）
grad_model = tf.keras.models.Model(model.inputs,
[model.get_layer('vgg16').output,
model.output])
但这已经失败并出现错误：
ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_5_12:0", shape=(None, None, None, 3), dtype=float32) at layer "block1_conv1". The following previous layers were accessed without issue: []
我不明白这是什么意思。该模型确实有效（我可以对其进行评估并进行预测）。
如果我从输出列表中省略 model.get_layer('vgg16').output，调用不会失败，但当然，这是可视化所必需的。
我做错了什么？
在我从头开始构建和训练的模型中，我能够创建一个以激活作为输出的类似模型，但在这里我得到了这些错误。
我的模特的详细信息
使用以下代码创建模型，然后训练并保存。
from tensorflow import keras
from tensorflow.keras import models
from tensorflow.keras import layers
from tensorflow.keras import optimizers
conv_base = keras.applications.vgg16.VGG16(
weights="vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5",
include_top=False)
conv_base.trainable = False
data_augmentation = keras.Sequential(
[
layers.experimental.preprocessing.RandomFlip("horizontal"),
layers.experimental.preprocessing.RandomRotation(0.1),
layers.experimental.preprocessing.RandomZoom(0.2),
]
)
inputs = keras.Input(shape=(180, 180, 3))
x = data_augmentation(inputs)
x = conv_base(x)
x = layers.Flatten()(x)
x = layers.Dense(256)(x)
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(inputs, outputs)
model.compile(loss="binary_crossentropy",
optimizer="rmsprop",
metrics=["accuracy"])
稍后加载：
model = keras.models.load_model("myModel.keras")
print(model.summary())
print(model.get_layer('sequential').summary())
print(model.get_layer('vgg16').summary())
输出：
Model: "functional_3"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_6 (InputLayer) [(None, 180, 180, 3)] 0
_________________________________________________________________
sequential (Sequential) (None, 180, 180, 3) 0
_________________________________________________________________
vgg16 (Functional) (None, None, None, 512) 14714688
_________________________________________________________________
flatten_1 (Flatten) (None, 12800) 0
_________________________________________________________________
dense_2 (Dense) (None, 256) 3277056
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_3 (Dense) (None, 1) 257
=================================================================
Total params: 17,992,001
Trainable params: 10,356,737
Non-trainable params: 7,635,264
_________________________________________________________________
None
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
random_flip (RandomFlip) (None, 180, 180, 3) 0
_________________________________________________________________
random_rotation (RandomRotat (None, 180, 180, 3) 0
_________________________________________________________________
random_zoom (RandomZoom) (None, 180, 180, 3) 0
=================================================================
Total params: 0
Trainable params: 0
Non-trainable params: 0
_________________________________________________________________
None
Model: "vgg16"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, None, None, 3)] 0
_________________________________________________________________
block1_conv1 (Conv2D) multiple 1792
_________________________________________________________________
block1_conv2 (Conv2D) multiple 36928
_________________________________________________________________
block1_pool (MaxPooling2D) multiple 0
_________________________________________________________________
block2_conv1 (Conv2D) multiple 73856
_________________________________________________________________
block2_conv2 (Conv2D) multiple 147584
_________________________________________________________________
block2_pool (MaxPooling2D) multiple 0
_________________________________________________________________
block3_conv1 (Conv2D) multiple 295168
_________________________________________________________________
block3_conv2 (Conv2D) multiple 590080
_________________________________________________________________
block3_conv3 (Conv2D) multiple 590080
_________________________________________________________________
block3_pool (MaxPooling2D) multiple 0
_________________________________________________________________
block4_conv1 (Conv2D) multiple 1180160
_________________________________________________________________
block4_conv2 (Conv2D) multiple 2359808
_________________________________________________________________
block4_conv3 (Conv2D) multiple 2359808
_________________________________________________________________
block4_pool (MaxPooling2D) multiple 0
_________________________________________________________________
block5_conv1 (Conv2D) multiple 2359808
_________________________________________________________________
block5_conv2 (Conv2D) multiple 2359808
_________________________________________________________________
block5_conv3 (Conv2D) multiple 2359808
_________________________________________________________________
block5_pool (MaxPooling2D) multiple 0
=================================================================
Total params: 14,714,688
Trainable params: 7,079,424
Non-trainable params: 7,635,264
【问题讨论】：
标签： python-3.x tensorflow keras