Question

通过Keras，我基于VGG16和一些自定义层创建了功能性API模型：

input_layer = layers.Input(shape=(150, 150, 3), name="model_input")
conv_base = VGG16(weights="imagenet", include_top=False, input_tensor=input_layer)

cust_model = conv_base(input_layer)
cust_model = layers.Flatten()(cust_model)
cust_model = layers.Dense(256, activation="relu")(cust_model)
cust_model = layers.Dense(1, activation="sigmoid")(cust_model)

final_model = models.Model(input=input_layer, output=cust_model)
...  # model training etc. (works fine)
final_model.save("models/custom_vgg16.h5")

在另一个脚本中，我想加载该模型并创建另一个自定义模型：

model_vgg16 = load_model("models/custom_vgg16.h5")

layer_input = model_vgg16.get_layer("model_input").input
layer_outputs = [layer.output for layer in model_vgg16.get_layer("vgg16").layers[1:]]
activation_model = models.Model(inputs=layer_input, outputs=layer_outputs)

但是最后一行导致以下错误：

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("model_input_1:0", shape=(?, 150, 150, 3), dtype=float32) at layer "model_input". The following previous layers were accessed without issue: []

我在SO和其他站点上发现了一些相关问题，但是这些似乎都不是我在这里面临的问题。你有什么想法吗？

PS：layer_outputs的内容是这样的：

Tensor("block1_conv1/Relu:0", shape=(?, 150, 150, 64), dtype=float32)
Tensor("block1_conv2/Relu:0", shape=(?, 150, 150, 64), dtype=float32)
Tensor("block1_pool/MaxPool:0", shape=(?, 75, 75, 64), dtype=float32)
Tensor("block2_conv1/Relu:0", shape=(?, 75, 75, 128), dtype=float32)
Tensor("block2_conv2/Relu:0", shape=(?, 75, 75, 128), dtype=float32)
Tensor("block2_pool/MaxPool:0", shape=(?, 37, 37, 128), dtype=float32)
Tensor("block3_conv1/Relu:0", shape=(?, 37, 37, 256), dtype=float32)
Tensor("block3_conv2/Relu:0", shape=(?, 37, 37, 256), dtype=float32)
Tensor("block3_conv3/Relu:0", shape=(?, 37, 37, 256), dtype=float32)
Tensor("block3_pool/MaxPool:0", shape=(?, 18, 18, 256), dtype=float32)
Tensor("block4_conv1/Relu:0", shape=(?, 18, 18, 512), dtype=float32)
Tensor("block4_conv2/Relu:0", shape=(?, 18, 18, 512), dtype=float32)
Tensor("block4_conv3/Relu:0", shape=(?, 18, 18, 512), dtype=float32)
Tensor("block4_pool/MaxPool:0", shape=(?, 9, 9, 512), dtype=float32)
Tensor("block5_conv1/Relu:0", shape=(?, 9, 9, 512), dtype=float32)
Tensor("block5_conv2/Relu:0", shape=(?, 9, 9, 512), dtype=float32)
Tensor("block5_conv3/Relu:0", shape=(?, 9, 9, 512), dtype=float32)
Tensor("block5_pool/MaxPool:0", shape=(?, 4, 4, 512), dtype=float32)

Answer 1

我找到了解决方案。必须直接使用vgg16层/模型的输入层。
因此，为了记录：

layer_input = model_vgg16.get_layer("vgg16").get_layer("model_input").input

Keras图已断开连接-但是为什么呢？

1 个答案: