Question

import os
import numpy as np
import pandas as pd
import cv2
from glob import glob
from tqdm import tqdm
import tensorflow as tf
from sklearn.model_selection import train_test_split

def read_image(path, size):
    image = cv2.imread(path, cv2.IMREAD_COLOR)
    image = cv2.resize(image, (size, size))
    image = image / 255.0
    image = image.astype(np.float32)
    return image

if __name__ == "__main__":
    path = "Dog Breed Identification/"
    train_path = os.path.join(path, "train/*")
    test_path = os.path.join(path, "test/*")
    labels_path = os.path.join(path, "labels.csv")

    labels_df = pd.read_csv(labels_path)
    breed = labels_df["breed"].unique()
    print("Number of Breed: ", len(breed))

    breed2id = {name: i for i, name in enumerate(breed)}
    id2breed = {i: name for i, name in enumerate(breed)}

    ids = glob(train_path)
    labels = []

    for image_id in ids:
        image_id = image_id.split("\\")[-1].split(".")[0]
        breed_name = list(labels_df[labels_df.id == image_id]["breed"])[0]
        breed_idx = breed2id[breed_name]
        labels.append(breed_idx)

    ids = ids[:1000]
    labels = labels[:1000]

    ## Spliting the dataset
    train_x, valid_x = train_test_split(ids, test_size=0.2, random_state=42)
    train_y, valid_y = train_test_split(labels, test_size=0.2, random_state=42)

    ## Model
    model = tf.keras.models.load_model('model.h5')

    for i, path in tqdm(enumerate(valid_x[:10])):
        image = read_image(path, 224)
        image = np.expand_dims(image, axis=0)
        pred = model.predict(image)[0]
        label_idx = np.argmax(pred)
        breed_name = id2breed[label_idx]
        ori_breed = id2breed[valid_y[i]]
        ori_image = cv2.imread(path, cv2.IMREAD_COLOR)
        ori_image = cv2.putText(ori_image, breed_name, (0, 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1)
        ori_image = cv2.putText(ori_image, ori_breed, (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1)
        cv2.imwrite(f"save/valid_{i}.png", ori_image)`enter code here`

2020-09-26 10:05:36.196524: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_101.dll


Number of Breed:  120

Traceback (most recent call last):

  File "D:\WoRkZ\CSE465\Project\New folder\Dog Breed Classification\test.py", line 48, in <module>
    model = tf.keras.models.load_model('model.h5')

  File "C:\Users\Catalan JM\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\tensorflow\python\keras\saving\save.py", line 184, in load_model
    return hdf5_format.load_model_from_hdf5(filepath, custom_objects, compile)

  File "C:\Users\Catalan JM\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\tensorflow\python\keras\saving\hdf5_format.py", line 177, in load_model_from_hdf5
    model = model_config_lib.model_from_config(model_config,

  File "C:\Users\Catalan JM\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\tensorflow\python\keras\saving\model_config.py", line 55, in model_from_config
    return deserialize(config, custom_objects=custom_objects)

  File "C:\Users\Catalan JM\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\tensorflow\python\keras\layers\serialization.py", line 105, in deserialize
    return deserialize_keras_object(

  File "C:\Users\Catalan JM\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\tensorflow\python\keras\utils\generic_utils.py", line 361, in deserialize_keras_object
    (cls, cls_config) = class_and_config_for_serialized_keras_object(

  File "C:\Users\Catalan JM\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.8_qbz5n2kfra8p0\LocalCache\local-packages\Python38\site-packages\tensorflow\python\keras\utils\generic_utils.py", line 321, in class_and_config_for_serialized_keras_object
    raise ValueError('Unknown ' + printable_module_name + ': ' + class_name)


ValueError: Unknown layer: Functional

[Finished in 12.301s]

您知道导致错误的原因是什么吗？我必须重写整个内容吗？

Answer 1

没关系，我需要做的就是pip安装Tensorflow 2.3.1并修复它并完美测试我的图像。

尽管它是CPU渲染而不是GpU渲染。因为有 2020-09-26 11：12：19.333012：W tensorflow / stream_executor / platform / default / dso_loader.cc：59]无法加载动态库'cublas64_10.dll'； dlerror：找不到cublas64_10.dll

没有影响我的模型的测试。我必须离题。

CNN ValueError：未知层：在测试模型时起作用

1 个答案: