I'm trying to implement the binary classification example using the IMDb dataset in Google Colab. I have implemented this model before. But when I tried to do it again after a few days, it returned a value error: 'Object arrays cannot be loaded when allow_pickle=False' for the load_data() function.
I have already tried solving this, referring to an existing answer for a similar problem: How to fix 'Object arrays cannot be loaded when allow_pickle=False' in the sketch_rnn algorithm. But it turns out that just adding an allow_pickle argument isn't sufficient.
My code:
from keras.datasets import imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
The error:
ValueError Traceback (most recent call last)
<ipython-input-1-2ab3902db485> in <module>()
1 from keras.datasets import imdb
----> 2 (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
2 frames
/usr/local/lib/python3.6/dist-packages/keras/datasets/imdb.py in load_data(path, num_words, skip_top, maxlen, seed, start_char, oov_char, index_from, **kwargs)
57 file_hash='599dadb1135973df5b59232a0e9a887c')
58 with np.load(path) as f:
---> 59 x_train, labels_train = f['x_train'], f['y_train']
60 x_test, labels_test = f['x_test'], f['y_test']
61
/usr/local/lib/python3.6/dist-packages/numpy/lib/npyio.py in __getitem__(self, key)
260 return format.read_array(bytes,
261 allow_pickle=self.allow_pickle,
--> 262 pickle_kwargs=self.pickle_kwargs)
263 else:
264 return self.zip.read(key)
/usr/local/lib/python3.6/dist-packages/numpy/lib/format.py in read_array(fp, allow_pickle, pickle_kwargs)
690 # The array contained Python objects. We need to unpickle the data.
691 if not allow_pickle:
--> 692 raise ValueError("Object arrays cannot be loaded when "
693 "allow_pickle=False")
694 if pickle_kwargs is None:
ValueError: Object arrays cannot be loaded when allow_pickle=False
Here's a trick to force imdb.load_data to allow pickle by, in your notebook, replacing this line:
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
by this:
import numpy as np
# save np.load
np_load_old = np.load
# modify the default parameters of np.load
np.load = lambda *a,**k: np_load_old(*a, allow_pickle=True, **k)
# call load_data with allow_pickle implicitly set to true
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
# restore np.load for future normal usage
np.load = np_load_old
This issue is still up on keras git. I hope it gets solved as soon as possible. Until then, try downgrading your numpy version to 1.16.2. It seems to solve the problem.
!pip install numpy==1.16.1
import numpy as np
This version of numpy has the default value of allow_pickle as True.
I just used allow_pickle = True as an argument to np.load() and it worked for me.
np.load(path, allow_pickle=True)