我正在嘗試/擺弄/學習一些小的 ML 問題。
我有一個基于預訓練卷積基礎的加載模型，其中包含一些自訓練密集層（有關模型詳細資訊，請參見下文）。
我想嘗試在模型上應用一些可視化，如激活和 Grad CAM 可視化（https://www.statworx.com/de/blog/erklaerbbarkeit-von-deep-learning-modellen-mit-grad-cam/） . 但我沒能做到。
我試圖創建一個基于我的新模型（如文章中所示）
grad_model = tf.keras.models.Model(model.inputs,
[model.get_layer('vgg16').output,
model.output])
但這已經失敗并出現錯誤：
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: []
我不明白這是什么意思。該模型肯定有效（我可以對其進行評估并進行預測）。如果我model.get_layer('vgg16').output從輸出串列中省略 ，呼叫不會失敗，但當然，這是可視化所必需的。
我做錯了什么？
在我從頭開始構建和訓練的模型中，我能夠創建一個類似的模型，將激活作為輸出，但在這里我得到了這些錯誤。
我的模型的詳細資訊
該模型是使用以下代碼創建的，然后進行訓練并保存。
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"])
后來它被加載：
model = keras.models.load_model("myModel.keras")
print(model.summary())
print(model.get_layer('sequential').summary())
print(model.get_layer('vgg16').summary())
輸出：
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
uj5u.com熱心網友回復：
您可以通過以下方式實作您想要的。首先，定義您的模型如下：
inputs = tf.keras.Input(shape=(180, 180, 3))
x = data_augmentation(inputs, training=True)
x = keras.applications.VGG16(input_tensor=x,
include_top=False,
weights=None)
x.trainable = False
x = layers.Flatten()(x.output)
x = layers.Dense(256)(x)
x = layers.Dropout(0.5)(x)
x = layers.Dense(1, activation='sigmoid')(x)
model = keras.Model(inputs, x)
for i, layer in enumerate(model.layers):
print(i, layer.name, layer.output_shape, layer.trainable)
...
17 block5_conv2 (None, 11, 11, 512) False
18 block5_conv3 (None, 11, 11, 512) False
19 block5_pool (None, 5, 5, 512) False
20 flatten_2 (None, 12800) True
21 dense_4 (None, 256) True
22 dropout_2 (None, 256) True
23 dense_5 (None, 1) True
現在，構建具有所需輸出層的 grad-cam 模型，如下所示：
grad_model = keras.models.Model(
[model.inputs],
[model.get_layer('block5_pool').output,
model.output]
)
測驗
image = np.random.rand(1, 180, 180, 3).astype(np.float32)
with tf.GradientTape() as tape:
convOutputs, predictions = grad_model(tf.cast(image, tf.float32))
loss = predictions[:, tf.argmax(predictions[0])]
grads = tape.gradient(loss, convOutputs)
print(grads)
tf.Tensor(
[[[[ 9.8454033e-04 3.6991197e-03 ... -1.2012678e-02
-1.7934230e-03 2.2925171e-03]
[ 1.6165405e-03 -1.9513096e-03 ... -2.5789393e-03
1.2443252e-03 -1.3931725e-03]
[-2.0554627e-04 1.2232144e-03 ... 5.2324748e-03
3.1955825e-04 3.4566019e-03]
[ 2.3650150e-03 -2.5699558e-03 ... -2.4103196e-03
5.8940407e-03 5.3285398e-03]
...
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