How to fix 'Object arrays cannot be loaded whe

Question

I was running the sketch_rnn.ipynb on my jupyter notebook, upon loading the environment to load the trained dataset, it returned an error 'Object arrays cannot be loaded when allow_pickle=False'

This is the code already used by google developers in developing the sketch_rnn algorithm that was even run in the google colab. In the past i have ran it myself on the google colab it worked but seems not to be working on my own jupyter notebook

from magenta.models.sketch_rnn.sketch_rnn_train import *
from magenta.models.sketch_rnn.model import *
from magenta.models.sketch_rnn.utils import *
from magenta.models.sketch_rnn.rnn import * 

model_params.batch_size = 1
eval_model_params = sketch_rnn_model.copy_hparams(model_params)
eval_model_params.use_input_dropout = 0
eval_model_params.use_recurrent_dropout = 0
eval_model_params.use_output_dropout = 0
eval_model_params.is_training = 0
sample_model_params = sketch_rnn_model.copy_hparams(eval_model_params)
sample_model_params.max_seq_len = 1
return [model_params, eval_model_params, sample_model_params]


[train_set, valid_set, test_set, hps_model, eval_hps_model, 
sample_hps_model] = load_env_compatible(data_dir, model_dir)

i expected the output to be

INFO:tensorflow:Downloading http://github.com/hardmaru/sketch-rnn- 
datasets/raw/master/aaron_sheep/aaron_sheep.npz
INFO:tensorflow:Loaded 7400/300/300 from aaron_sheep.npz
INFO:tensorflow:Dataset combined: 8000 (7400/300/300), avg len 125
INFO:tensorflow:model_params.max_seq_len 250.
total images <= max_seq_len is 7400
total images <= max_seq_len is 300
total images <= max_seq_len is 300
INFO:tensorflow:normalizing_scale_factor 18.5198.

But it gave me

ValueError: Object arrays cannot be loaded when allow_pickle=False

Answer 1

Use allow_pickle=True as one of the arguments to np.load().

Answer 2

So I believe this has just surfaced due to a change in numpy to load(), if you observe the line that the error occurs it references something like

    with np.load(path) as f:
        x_train, labels_train = f['x_train'], f['y_train']
        x_test, labels_test = f['x_test'], f['y_test']

but the Keras source code, for example here at line 58: https://github.com/keras-team/keras/blob/master/keras/datasets/imdb.py

now uses

    with np.load(path, allow_pickle=True) as f:
        x_train, labels_train = f['x_train'], f['y_train']
        x_test, labels_test = f['x_test'], f['y_test']

where np.load(path) becomes np.load(path, boolean)

From brief reading, the addition of pickles has to do with security, since pickles can contain arbitrary Python code that would be run when something is loaded. (Possibly similar to the way SQL injections are performed)

After updating np.load with the new param list, it's working for my project

Answer 3

This code solved the problem at my side.

# Downgrate numpy to fix a problem
!pip install numpy==1.16.2
import numpy as np
print(np.__version__)

I just downgrade numpy as the problem is due to some internal conflict.