pickle

I wrote a script that does a bit of data processing, then pickles the results before doing some more processing so I can fiddle with final parameters for plotting. I am pickling lists of two classes that I've created. Pickling is fine for each list, but unpickling one list returns the following error: Traceback (most recent call last): File "script.py", line 980, in <module> main() File "script.py", line 956, in promoter_main open(os.path.join(pickle_dir, 'doublep.pickle'), 'rb')) File "/usr/lib/python2.6/pickle.py", line 1370, in load return Unpickler(file).load() File "/usr/lib/python2.6

I was doing deep run using Keras. However, the following error occurred in the process of storing the model after learning. TypeError: can't pickle NotImplementedType objects I had no problem when I ran the same code in another directory. The code below is the portion of the code that is causing the error. .... model.add(Dense(2, activation='relu')) model.add(Dense(1, activation='sigmoid')) model = multi_gpu_model(model, gpus=4) model.compile(loss='binary_crossentropy', optimizer = 'adam', metrics = ['accuracy']) model.fit(x_train,y_train,epochs = 3, batch_size =500) scores = model.evaluate(x

I tried to save a pygame.Surface but it doesn't let me, error TypeError: can't pickle Surface objects I can make it save surfaces? Or maybe there is another module that can save it ? EXPLANATION: a = pygame.Surface( (5,5) ) file = open("hello", "w") pickle.dump(a, file) I have classes which saves in them Surfaces. As monkey said: You don't want to pickle a surface. But if you really need to save that surfaces' content than use the pygame.image.save() function. If you prefer your surface not to be an actual image file (for whatever reason) you could use the pygame.image.tostring() function

I use python 3.4 with win 7 64-bit system. I ran the following code: 6 """ load single batch of cifar """ 7 with open(filename, 'r') as f: ----> 8 datadict = pickle.load(f) 9 X = datadict['data'] The wrong message is UnicodeDecodeError: 'gbk' codec can't decode byte 0x80 in position 0: illegal multibyte sequence I changed the line 7 as: 6 """ load single batch of cifar """ 7 with open(filename, 'r'，encoding='utf-8') as f: ----> 8 datadict = pickle.load(f) 9 X = datadict['data'] The wrong message became UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte .

I'm developing a class which can be meaningfully pickled only using protocol 2 (and upwards on Python 3.) When an older protocol is used, hard-to-trace bugs happen. I want to save the users of the class some debugging pain, so I want the class to immediately raise an exception if it's being pickled with a protocol of less than 2. How can I do that? You can implement the __reduce_ex__() method on your class. It receives one parameter, which is the protocol version. Simply raise an exception if the version is not 2. 来源： https://stackoverflow.com/questions/5570680/python-ensuring-my-class-gets

I would like to create a class that describes a file resource and then pickle it. This part is straightforward. To be concrete, let's say that I have a class "A" that has methods to operate on a file. I can pickle this object if it does not contain a file handle. I want to be able to create a file handle in order to access the resource described by "A". If I have an "open()" method in class "A" that opens and stores the file handle for later use, then "A" is no longer pickleable. (I add here that opening the file includes some non-trivial indexing which cannot be cached--third party code--so

I wanted to use this classifier in other computer without had to train it again. I used to save some classifiers from scikit with cPickle. Doing the same with LIBSVM it gives me a " ValueError: ctypes objects containing pointers cannot be pickled ". I'm using LibSVM 3.1 and Python 2.7.3. Thanks from libsvm.svm import * from libsvm.svmutil import * import cPickle x = [[1, 0, 1], [-1, 0, -1]] y = [1, -1] prob = svm_problem(y, x) param = svm_parameter() param.kernel_type = LINEAR param.C = 10 m = svm_train(prob, param) labels_pred, acc, probs = svm_predict([-1, 1], [[1, 1, 1], [0, 0, 1]], m)