ValueError:未知层:功能性 [英] ValueError: Unknown layer: Functional

问题描述

我在 colab 中制作了一个 CNN 并在每个时期保存模型.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:

I made a CNN in colab and saved the models at every epoch. I exported the h5 file and now am trying to run the model on some test images. Here's the main error:

ValueError: Unknown layer: Functional

这是我用来运行模型并在每个时期保存的代码:

Here's the code I used to run the model and save at each epoch:

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，我刚刚从本地的 colab 侧边栏中下载了 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:

After the model ran I just downloaded the h5 file from the colab sidebar locally. I re-uploaded the file from the local disk, and here's how I'm trying to load the model:

# 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

这是完整的回溯:

---------------------------------------------------------------------------
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

似乎有几个类似的这里有问题，以及此处.更改导入方法还没有帮助，并尝试制作一些 kind自定义 对象也没有工作.

It seems there have been several similar questions here,and here. Changing the import method hasn't helped yet, and trying to make some kind of custom object has not worked either.

推荐答案

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)

加载权重:

model.load_weights('save_at_47.h5')

并对图像进行预测:

# 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)
)

这篇关于ValueError:未知层:功能性的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！