AttributeError: 'numpy.random.mtrand.RandomState' object has no attribute 'integers'
See original GitHub issueThe error:
AttributeError Traceback (most recent call last)
<ipython-input-6-54024aaf20f0> in <module>()
3 algo= tpe.suggest, max_evals= 5,
4 trials= Trials(),
----> 5 notebook_name='Deep learning GridSearch')
6 xtr, ytr, xte, yte= data()
7
~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in minimize(model, data, algo, max_evals, trials, functions, rseed, notebook_name, verbose, eval_space, return_space, keep_temp)
67 notebook_name=notebook_name,
68 verbose=verbose,
---> 69 keep_temp=keep_temp)
70
71 best_model = None
~/anaconda3/lib/python3.6/site-packages/hyperas/optim.py in base_minimizer(model, data, functions, algo, max_evals, trials, rseed, full_model_string, notebook_name, verbose, stack, keep_temp)
137 trials=trials,
138 rstate=np.random.RandomState(rseed),
--> 139 return_argmin=True),
140 get_space()
141 )
~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
553 show_progressbar=show_progressbar,
554 early_stop_fn=early_stop_fn,
--> 555 trials_save_file=trials_save_file,
556 )
557
~/anaconda3/lib/python3.6/site-packages/hyperopt/base.py in fmin(self, fn, space, algo, max_evals, timeout, loss_threshold, max_queue_len, rstate, verbose, pass_expr_memo_ctrl, catch_eval_exceptions, return_argmin, show_progressbar, early_stop_fn, trials_save_file)
686 show_progressbar=show_progressbar,
687 early_stop_fn=early_stop_fn,
--> 688 trials_save_file=trials_save_file,
689 )
690
~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in fmin(fn, space, algo, max_evals, timeout, loss_threshold, trials, rstate, allow_trials_fmin, pass_expr_memo_ctrl, catch_eval_exceptions, verbose, return_argmin, points_to_evaluate, max_queue_len, show_progressbar, early_stop_fn, trials_save_file)
584
585 # next line is where the fmin is actually executed
--> 586 rval.exhaust()
587
588 if return_argmin:
~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in exhaust(self)
362 def exhaust(self):
363 n_done = len(self.trials)
--> 364 self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
365 self.trials.refresh()
366 return self
~/anaconda3/lib/python3.6/site-packages/hyperopt/fmin.py in run(self, N, block_until_done)
277 # processes orchestration
278 new_trials = algo(
--> 279 new_ids, self.domain, trials, self.rstate.integers(2 ** 31 - 1)
280 )
281 assert len(new_ids) >= len(new_trials)
AttributeError: 'numpy.random.mtrand.RandomState' object has no attribute 'integers'
My code:
def data():
(xtr, ytr), (xte, yte)= mnist.load_data()
xtr= xtr.reshape(60000, 784); xtr= xtr.astype('float32')
xte= xte.reshape(10000, 784); xte= xte.astype('float32')
xtr/= 255; xte/= 255
nb_classes= 10
ytr= np_utils.to_categorical(ytr, nb_classes)
yte= np_utils.to_categorical(yte, nb_classes)
return xtr, ytr, xte, yte
def create_model(xtr, ytr, xte, yte):
# returns a dictionary of loss, status and model
model= Sequential()
# 1st layer:
model.add(Dense(512, input_shape= (784,), activation= 'relu')) # equivalently input_dim= 784
model.add(Dropout({{uniform(0,1)}}))
# 2nd layer:
# hyperparameter = {{choice([...])}}
model.add(Dense(units= {{choice([256,5125,1024])}},
activation= {{choice(['relu', 'sigmoid'])}}))
model.add(Dropout({{uniform(0,1)}}))
# 3rd layer:
if {{choice(['three','four'])}} == 'four':
model.add(Dense(100))
# choice between 2 different types of Dense(100) layers:
model.add({{choice([Dropout(0.5), Activation('linear')])}})
model.add(Activation('relu'))
# 4th layer:
model.add(Dense(10, activation= 'softmax'))
model.compile(loss='categorical_crossentropy',
optimizer= {{choice(['rmsprop', 'adam', 'SGD'])}},
metrics=['accuracy'])
# Model fit:
result= model.fit(xtr, ytr, batch_size= {{choice([64, 128])}},
epochs= 2, verbose= 2, validation_split= 0.1)
validation_acc= np.amax(result.history['val_acc'])
print('Best validation accuracy of epoch:', validation_acc)
return {'Loss:', -validation_acc, 'status:', STATUS_OK, 'model:',model}
if __name__ == '__main__':
best_run, best_model= optim.minimize(model= create_model, data= data,
algo= tpe.suggest, max_evals= 5,
trials= Trials(),
notebook_name='Deep learning GridSearch')
xtr, ytr, xte, yte= data()
print('Evaluation of best performing model:')
print(best_model.evaluate(xte, yte))
print('Optimal hyperparameter choice:')
print(best_run)
Issue Analytics
- State:
- Created 2 years ago
- Comments:11
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For me with hyperopt==0.2.7 I have changed the row 139 of optim.py in hyperas from: rstate=np.random.RandomState(rseed) to rstate=np.random.default_rng(rseed)
it maybe the hyperopt version problem,please choose other version of hyperopt pip install hyperopt==0.2.5