Keras.applications 源代碼中的更改導致本地主機中缺少變量時出錯

Question

對於圖像聚類，我使用了一段完美運行的代碼。

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)



import os
import keras
from keras.preprocessing import image
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
from keras.models import Model

def get_pca_fingerprint(images):

    # GET VGG-16 FINGERPRINT FOR EACH IMAGE
    # we will use a pretrained network and run each image through
    # the network. We will pull the feature vector for the last layer.
    # This is essentially a fingerprint that describes the image.
    # This fingerprint is used to perform clustering.
    
    model = keras.applications.VGG16(weights='imagenet', include_top=True)
    feat_extractor = Model(inputs=model.input, outputs=model.get_layer("fc2").output)

    tic = time.clock()

    features = []
    for i, image_path in enumerate(images):
        if i % 500 == 0:
            toc = time.clock()
            elap = toc-tic;
            print("analyzing image %d / %d. Time: %4.4f seconds." % (i, len(images),elap))
            tic = time.clock()
        img, x = load_image(image_path);
        feat = feat_extractor.predict(x)[0]
        features.append(feat)

    print('finished extracting features for %d images' % len(images))
    
    max_comp = 300
    if len(list(images)) < max_comp:
        max_comp=5
    
    features = np.array(features)
    pca = PCA(n_components=max_comp)
    pca.fit(features)

    pca_features = pca.transform(features)

    return pca_features

然而，從一天到另一天，現在的可能正在失敗：

AttributeError: module 'keras.applications' has no attribute 'VGG16'

我曾嘗試使用 tf.keras.applications.VGG16 對其進行更改，但是彈出了一大堆我無法解決的新錯誤：

FailedPreconditionError                   Traceback (most recent call last)

<ipython-input-28-e0d4bda5e849> in <module>()
      2 #the variable 'images' should be a list with all the paths to the images now
      3 model = tf.keras.applications.VGG16(weights='imagenet', include_top=True)
----> 4 pca_fingerprints = get_pca_fingerprint(images)

5 frames

<ipython-input-27-151877a5d62b> in get_pca_fingerprint(images)
     46             tic = time.clock()
     47         img, x = load_image(image_path);
---> 48         feat = feat_extractor.predict(x)[0]
     49         features.append(feat)
     50 

/usr/local/lib/python3.7/dist-packages/keras/engine/training_v1.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
    978         max_queue_size=max_queue_size,
    979         workers=workers,
--> 980         use_multiprocessing=use_multiprocessing)
    981 
    982   def reset_metrics(self):

/usr/local/lib/python3.7/dist-packages/keras/engine/training_arrays_v1.py in predict(self, model, x, batch_size, verbose, steps, callbacks, **kwargs)
    703         verbose=verbose,
    704         steps=steps,
--> 705         callbacks=callbacks)

/usr/local/lib/python3.7/dist-packages/keras/engine/training_arrays_v1.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq, mode, validation_in_fit, prepared_feed_values_from_dataset, steps_name, **kwargs)
    374 
    375         # Get outputs.
--> 376         batch_outs = f(ins_batch)
    377         if not isinstance(batch_outs, list):
    378           batch_outs = [batch_outs]

/usr/local/lib/python3.7/dist-packages/keras/backend.py in __call__(self, inputs)
   4018 
   4019     fetched = self._callable_fn(*array_vals,
-> 4020                                 run_metadata=self.run_metadata)
   4021     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   4022     output_structure = tf.nest.pack_sequence_as(

/usr/local/lib/python3.7/dist-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
   1480         ret = tf_session.TF_SessionRunCallable(self._session._session,
   1481                                                self._handle, args,
-> 1482                                                run_metadata_ptr)
   1483         if run_metadata:
   1484           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

FailedPreconditionError: 2 root error(s) found.
  (0) Failed precondition: Could not find variable fc1_15/kernel. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Container localhost does not exist. (Could not find resource: localhost/fc1_15/kernel)
     [[{{node fc1_15/MatMul/ReadVariableOp}}]]
     [[fc2_15/Relu/_27]]
  (1) Failed precondition: Could not find variable fc1_15/kernel. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status=Not found: Container localhost does not exist. (Could not find resource: localhost/fc1_15/kernel)
     [[{{node fc1_15/MatMul/ReadVariableOp}}]]
0 successful operations.
0 derived errors ignored.

我嘗試調試但無濟於事。 誰能幫我解決問題？

Answer 1

我切換到 TF2 而不是禁用 v2 行為，這已經解決了問題

import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)

至

import tensorflow as tf
config = tf.compat.v1.ConfigProto

#and loading VGG16 as
tf.keras.applications.VGG16(weights='imagenet', include_top=True)