ValueError: 未知层：功能

  • Z时代
  • 2024-01-10
  • 分类：问答

我在colab中制作了CNN，并在每个时代保存了模型。我导出了h5

文件，现在尝试在一些测试图像上运行模型。这是主要

错误：

ValueError: Unknown layer: Functional

这是我用于运行模型并在每个时期保存的代码：

epochs = 50
callbacks = [
    tf.keras.callbacks.TensorBoard(log_dir='./logs'),
    keras.callbacks.ModelCheckpoint("save_at_{epoch}.h5"),
]
model.compile(
    optimizer=keras.optimizers.Adam(1e-3),
    loss="binary_crossentropy",
    metrics=["accuracy"],
)
model.fit(
    train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds,
)

模型运行后，我刚刚从colab侧边栏

本地下载了h5文件。我从本地磁盘重新上传了文件，这是我尝试

加载模型的方式：

# load and evaluate a saved model
from tensorflow.keras.models import load_model
# load model#
loaded_model = load_model('save_at_47.h5')
loaded_model.layers[0].input_shape

这是完整的追溯：

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-6af7396280fa> in <module>()
      3 
      4 # load model#
----> 5 loaded_model = load_model('save_at_47.h5')
      6 loaded_model.layers[0].input_shape
5 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/save.py in load_model(filepath, custom_objects, compile)
    182     if (h5py is not None and (
    183         isinstance(filepath, h5py.File) or h5py.is_hdf5(filepath))):
--> 184       return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)
    185 
    186     if sys.version_info >= (3, 4) and isinstance(filepath, pathlib.Path):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/hdf5_format.py in load_model_from_hdf5(filepath, custom_objects, compile)
    176     model_config = json.loads(model_config.decode('utf-8'))
    177     model = model_config_lib.model_from_config(model_config,
--> 178                                                custom_objects=custom_objects)
    179 
    180     # set weights
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/saving/model_config.py in model_from_config(config, custom_objects)
     53                     '`Sequential.from_config(config)`?')
     54   from tensorflow.python.keras.layers import deserialize  # pylint: disable=g-import-not-at-top
---> 55   return deserialize(config, custom_objects=custom_objects)
     56 
     57
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/serialization.py in deserialize(config, custom_objects)
    107       module_objects=globs,
    108       custom_objects=custom_objects,
--> 109       printable_module_name='layer')
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in deserialize_keras_object(identifier, module_objects, custom_objects, printable_module_name)
    360     config = identifier
    361     (cls, cls_config) = class_and_config_for_serialized_keras_object(
--> 362         config, module_objects, custom_objects, printable_module_name)
    363 
    364     if hasattr(cls, 'from_config'):
/usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/utils/generic_utils.py in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
    319   cls = get_registered_object(class_name, custom_objects, module_objects)
    320   if cls is None:
--> 321     raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)
    322 
    323   cls_config = config['config']
ValueError: Unknown layer: Functional

回答：

从头开始重建网络：

image_size = (212, 212)
batch_size = 32
data_augmentation = keras.Sequential(
    [
        layers.experimental.preprocessing.RandomFlip("horizontal_and_vertical"),
        layers.experimental.preprocessing.RandomRotation(0.8),
    ]
)
def make_model(input_shape, num_classes):
    inputs = keras.Input(shape=input_shape)
    # Image augmentation block
    x = data_augmentation(inputs)
    # Entry block
    x = layers.experimental.preprocessing.Rescaling(1.0 / 255)(x)
    x = layers.Conv2D(32, 3, strides=2, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)
    x = layers.Conv2D(64, 3, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)
    previous_block_activation = x  # Set aside residual
    for size in [128, 256, 512, 728]:
        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(size, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)
        x = layers.Activation("relu")(x)
        x = layers.SeparableConv2D(size, 3, padding="same")(x)
        x = layers.BatchNormalization()(x)
        x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
        # Project residual
        residual = layers.Conv2D(size, 1, strides=2, padding="same")(
            previous_block_activation
        )
        x = layers.add([x, residual])  # Add back residual
        previous_block_activation = x  # Set aside next residual
    x = layers.SeparableConv2D(1024, 3, padding="same")(x)
    x = layers.BatchNormalization()(x)
    x = layers.Activation("relu")(x)
    x = layers.GlobalAveragePooling2D()(x)
    if num_classes == 2:
        activation = "sigmoid"
        units = 1
    else:
        activation = "softmax"
        units = num_classes
    x = layers.Dropout(0.5)(x)
    outputs = layers.Dense(units, activation=activation)(x)
    return keras.Model(inputs, outputs)
model = make_model(input_shape=image_size + (3,), num_classes=2)
keras.utils.plot_model(model, show_shapes=False)

Loaded the weights:

model.load_weights('save_at_47.h5')

And ran a prediction on an image:

# Running inference on new data
img = keras.preprocessing.image.load_img(
    "le_image.jpg", target_size=image_size
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)  # Create batch axis
predictions = model.predict(img_array)
score = predictions[0]
print(
    "This image is %.2f percent negative and %.2f percent positive."
    % (100 * (1 - score), 100 * score)
)

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