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
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) )
