keras.models.load_model("") 在 h5f.open() 上给出错误

Question

使用

keras.models.load

时，它会在

h5f.open(name, flags, fapl=fapl)

上抛出错误并显示

OSError: Unable to open file (file signature not found)

。

DNN模型文件代码

import random
import numpy as np
import tensorflow as tf
from keras.layers import Dense, Dropout
from keras.models import Sequential
from keras.regularizers import l1, l2
from keras.optimizers import Adam

def set_seeds(seed = 100):
    random.seed(seed)
    np.random.seed(seed)
    tf.random.set_seed(seed)
    
def cw(df):
    c0, c1 = np.bincount(df["dir"])
    w0 = (1/c0) * (len(df)) / 2
    w1 = (1/c1) * (len(df)) / 2
    return {0:w0, 1:w1}

optimizer = Adam(lr = 0.0001)

def create_model(hl = 2, hu = 100, dropout = False, rate = 0.3, regularize = False,
                 reg = l1(0.0005), optimizer = optimizer, input_dim = None):
    if not regularize:
        reg = None
    model = Sequential()
    model.add(Dense(hu, input_dim = input_dim, activity_regularizer = reg ,activation = "relu"))
    if dropout: 
        model.add(Dropout(rate, seed = 100))
    for layer in range(hl):
        model.add(Dense(hu, activation = "relu", activity_regularizer = reg))
        if dropout:
            model.add(Dropout(rate, seed = 100))
    model.add(Dense(1, activation = "sigmoid"))
    model.compile(loss = "binary_crossentropy", optimizer = optimizer, metrics = ["accuracy"])
    return model

加载模型及参数

# Loading the model
import keras
model = keras.models.load_model("C:/Users/Hussein Ali/Desktop/d/DNNModel.py")

错误：

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
Cell In[1], line 3
      1 # Loading the model
      2 import keras
----> 3 model = keras.models.load_model("C:/Users/Hussein Ali/Desktop/d/DNNModel.py")

File C:\anaconda\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File C:\anaconda\lib\site-packages\h5py\_hl\files.py:533, in File.__init__(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, fs_strategy, fs_persist, fs_threshold, fs_page_size, page_buf_size, min_meta_keep, min_raw_keep, locking, alignment_threshold, alignment_interval, **kwds)
    525     fapl = make_fapl(driver, libver, rdcc_nslots, rdcc_nbytes, rdcc_w0,
    526                      locking, page_buf_size, min_meta_keep, min_raw_keep,
    527                      alignment_threshold=alignment_threshold,
    528                      alignment_interval=alignment_interval,
    529                      **kwds)
    530     fcpl = make_fcpl(track_order=track_order, fs_strategy=fs_strategy,
    531                      fs_persist=fs_persist, fs_threshold=fs_threshold,
    532                      fs_page_size=fs_page_size)
--> 533     fid = make_fid(name, mode, userblock_size, fapl, fcpl, swmr=swmr)
    535 if isinstance(libver, tuple):
    536     self._libver = libver

File C:\anaconda\lib\site-packages\h5py\_hl\files.py:226, in make_fid(name, mode, userblock_size, fapl, fcpl, swmr)
    224     if swmr and swmr_support:
    225         flags |= h5f.ACC_SWMR_READ
--> 226     fid = h5f.open(name, flags, fapl=fapl)
    227 elif mode == 'r+':
    228     fid = h5f.open(name, h5f.ACC_RDWR, fapl=fapl)

File h5py\_objects.pyx:54, in h5py._objects.with_phil.wrapper()

File h5py\_objects.pyx:55, in h5py._objects.with_phil.wrapper()

File h5py\h5f.pyx:106, in h5py.h5f.open()

OSError: Unable to open file (file signature not found)

Answer 1

model

可以表示几个不同的元素。

• 变量中的Python代码

  model

• 模型的权重保存在文件中

  H5

你混淆了这些概念。

---

如果您想从

DNNModel.py

加载代码，请使用标准

import DNNModel

model = DNNModel.create_model()

但是这给出了模型中没有预训练

weights

的新鲜模型，并且需要很长时间来训练它。

因此，我们使用文件

H5

来保留模型中预训练的

weights

，然后我们再次加载它以使用预训练的

weights

创建模型，这样我们就不必浪费时间再次训练它。

keras.models.load_model(model, 'model.h5')   # save pretrained model in file

model = keras.models.load_model('model.h5')  # load pretrained model from file