升级到 Tensorflow 2.5 现在使用预训练的 Keras 应用程序模型时会出现 Lambda 层错误

Question

我按照本教程为我的问题构建了一个连体网络。 我使用的是 Tensorflow 2.4.1 现在升级了

这段代码以前工作得很好

base_cnn = resnet.ResNet50(
    weights="imagenet", input_shape=target_shape + (3,), include_top=False
)

flatten = layers.Flatten()(base_cnn.output)
dense1 = layers.Dense(512, activation="relu")(flatten)
dense1 = layers.BatchNormalization()(dense1)
dense2 = layers.Dense(256, activation="relu")(dense1)
dense2 = layers.BatchNormalization()(dense2)
output = layers.Dense(256)(dense2)

embedding = Model(base_cnn.input, output, name="Embedding")

trainable = False
for layer in base_cnn.layers:
    if layer.name == "conv5_block1_out":
        trainable = True
    layer.trainable = trainable

现在每个 resnet 层或 mobilenet 或高效网络（都尝试过）都会抛出这些错误：

WARNING:tensorflow:
The following Variables were used a Lambda layer's call (tf.nn.convolution_620), but
are not present in its tracked objects:
  <tf.Variable 'stem_conv/kernel:0' shape=(3, 3, 3, 48) dtype=float32>
It is possible that this is intended behavior, but it is more likely
an omission. This is a strong indication that this layer should be
formulated as a subclassed Layer rather than a Lambda layer.

它编译并且似乎适合。

但是我们必须在 2.5 中以不同的方式初始化模型吗？

感谢您的任何指点！

Answer 1

此处无需恢复到TF2.4.1 。 我总是建议尝试使用最新版本，因为它解决了许多性能问题和新功能。

我能够在TF2.5中执行上述代码而没有任何问题。

import tensorflow as tf
print(tf.__version__)
from tensorflow.keras.applications import ResNet50
from tensorflow.keras import layers, Model


img_width, img_height = 224, 224
target_shape = (img_width, img_height, 3)


base_cnn = ResNet50(
    weights="imagenet", input_shape=target_shape, include_top=False
)

flatten = layers.Flatten()(base_cnn.output)
dense1 = layers.Dense(512, activation="relu")(flatten)
dense1 = layers.BatchNormalization()(dense1)
dense2 = layers.Dense(256, activation="relu")(dense1)
dense2 = layers.BatchNormalization()(dense2)
output = layers.Dense(256)(dense2)

embedding = Model(base_cnn.input, output, name="Embedding")

trainable = False
for layer in base_cnn.layers:
    if layer.name == "conv5_block1_out":
        trainable = True
    layer.trainable = trainable

Output：

2.5.0
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
94773248/94765736 [==============================] - 1s 0us/step

根据@Olli，重新启动并清除 session kernel 已解决问题。

Answer 2

我不确定您的问题的主要原因是什么，因为它通常无法重现。 但这里有一些关于该警告信息的说明。 您的问题中显示的回溯不是来自ResNet而是来自EfficientNet 。

现在，我们知道存在Lambda层，因此在构建顺序和功能API 模型时，可以使用任意表达式作为Layer 。 Lambda层最适合简单操作或快速实验。 虽然可以将变量与 Lambda 层一起使用，但不鼓励这种做法，因为它很容易导致错误。 例如：

import tensorflow as tf 

x_input = tf.range(12.).numpy().reshape(-1, 4)
weights = tf.Variable(tf.random.normal((4, 2)), name='w')
bias = tf.ones((1, 2), name='b')

# lambda custom layer
mylayer1 = tf.keras.layers.Lambda(lambda x: tf.add(tf.matmul(x, weights),
                                                           bias), name='lambda1')
mylayer1(x_input)

WARNING:tensorflow:
The following Variables were used a Lambda layer's call (lambda1), but
are not present in its tracked objects:
  <tf.Variable 'w:0' shape=(4, 2) dtype=float32, numpy=
array([[-0.753139  , -1.1668463 ],
       [-1.3709341 ,  0.8887151 ],
       [ 0.3157893 ,  0.01245957],
       [-1.3878908 , -0.38395467]], dtype=float32)>
It is possible that this is intended behavior, but it is more likely
an omission. This is a strong indication that this layer should be
formulated as a subclassed Layer rather than a Lambda layer.
<tf.Tensor: shape=(3, 2), dtype=float32, numpy=
array([[ -3.903028 ,   0.7617702],
       [-16.687727 ,  -1.8367348],
       [-29.472424 ,  -4.43524  ]], dtype=float32)>

这是因为mylayer1层不直接跟踪tf.Variables ，因此这些参数不会出现在mylayer1.trainable_weights中。

mylayer1.trainable_weights
[]

一般来说， Lambda层可以方便简单的无状态计算，但更复杂的应该使用子类层来代替。 从您的回溯来看， step_conv层似乎可能存在这种情况。

for layer in EfficientNetB0(weights=None).layers:
    if layer.name == 'stem_conv':
        print(layer)
<tensorflow.python.keras.layers.convolutional.Conv2D object.. 

快速调查tf.compat.v1.nn.conv2d的源代码，导致可能是原因的lambda 表达式。

Answer 3

pip 安装 tensorflow==2.3.0，为我工作而不是 tf 2.5 我遇到了与使用 Lambda 层相关的问题