hyperas
hyperas copied to clipboard
maxpumperla
TypeError: set_postfix() argument after ** must be a mapping, not str
Hello, I am trying to use the hyperas for tuning the multi-input LSTM model, but i am unable to figure out, where things are going wrong. I have searched online, but could not find anything relevant to this.
This is my code:
def lstm_model(x_claim, x_sents, x_labels, test_claims_data, test_sents_data, test_labels):
vocab_size_of_claims = 15000
vocab_size_of_sents = 30000
max_claims_length = 65
max_sents_length = 350
embedding_dim = 300
embeddings_index = pickle.load(open("embeddings_"+str(embedding_dim)+"dim_dict.pkl", "rb"))
(embed_matrix_c, embed_dim_c) = preprocess.create_embedding_matrix(vocab_size_of_claims, embeddings_index, claim_word_train_index, embedding_dim)
(embed_matrix_s, embed_dim_s) = preprocess.create_embedding_matrix(vocab_size_of_sents, embeddings_index, sents_word_train_index, embedding_dim)
claims_input = Input(shape=(None,), dtype='int32', name='claims')
embed_claims = Embedding(vocab_size_of_claims, embed_dim_c)(claims_input)
encoded_claims = LSTM({{choice([16, 64, 256, 512, 1024])}}, return_sequences=True, recurrent_dropout={{uniform(0, 1)}}, dropout={{uniform(0, 1)}})(embed_claims)
encoded_claims = LSTM({{choice([16, 64, 256, 512, 1024])}}, return_sequences=True, recurrent_dropout={{uniform(0, 1)}}, dropout={{uniform(0, 1)}})(encoded_claims)
encoded_claims = LSTM({{choice([16, 64, 256, 512, 1024])}})(encoded_claims)
sentences_input = Input(shape=(None,), dtype='int32', name='sentences')
embed_sents = Embedding(vocab_size_of_sents, embed_dim_s)(sentences_input)
encoded_sentences = LSTM({{choice([8, 16, 64, 256, 512, 1024])}}, return_sequences=True, recurrent_dropout={{uniform(0, 1)}}, dropout={{uniform(0, 1)}})(embed_sents)
encoded_sentences = LSTM({{choice([8, 16, 64, 256, 512, 1024])}}, return_sequences=True, recurrent_dropout={{uniform(0, 1)}}, dropout={{uniform(0, 1)}})(encoded_sentences)
encoded_sentences = LSTM({{choice([8, 16, 64, 256, 512, 1024])}})(encoded_sentences)
concatenate_layers = concatenate([encoded_claims, encoded_sentences],
axis=-1)
concatenate_layers = Dense({{choice([16, 64, 256, 512, 1024])}}, kernel_regularizer=regularizers.l2(0.001), activation='relu')(concatenate_layers)
concatenate_layers = Dropout({{uniform(0, 1)}})(concatenate_layers)
# concatenate_layers = Dense({{choice([16, 64, 256, 512, 1024])}}, kernel_regularizer=regularizers.l2(0.001), activation='relu')(concatenate_layers)
# concatenate_layers = Dropout({{uniform(0, 1)}})(concatenate_layers)
concatenate_layers = Dense({{choice([16, 64, 256, 512, 1024])}}, kernel_regularizer=regularizers.l2(0.001), activation='relu')(concatenate_layers)
concatenate_layers = Dropout({{uniform(0, 1)}})(concatenate_layers)
pred_label = Dense(1, activation='sigmoid')(concatenate_layers)
model = Model([claims_input, sentences_input], pred_label)
model.layers[2].set_weights([embed_matrix_c])
model.layers[2].trainable = False
model.layers[3].set_weights([embed_matrix_s])
model.layers[3].trainable = False
print (model.summary())
early_stopping = EarlyStopping(monitor='val_loss', patience=2)
checkpointer = ModelCheckpoint(filepath='keras_weights_hyperas_fever_full.h5',
verbose=1,
save_best_only=True)
model.compile(loss='binary_crossentropy', optimizer=Adam(), metrics=['accuracy'])
model.fit({'claims': x_claim, 'sentences': x_sents}, x_labels,
batch_size={{choice([64, 128])}},
epochs=20,
verbose=2,
validation_split=0.1, callbacks=[early_stopping, checkpointer])
score, acc = model.evaluate({'claims': test_claims_data, 'sentences': test_sents_data}, test_labels, verbose=0)
print('Test accuracy:', acc)
return {'loss': -acc, 'status': STATUS_OK, 'model': model}
def data():
vocab_size_of_claims = 15000
vocab_size_of_sents = 30000
max_claims_length = 65
max_sents_length = 350
embedding_dim = 300
global claim_word_train_index
global sents_word_train_index
train_dataset_name = "fever_full_binary_train"
test_dataset_name = "fever_full_binary_dev"
train_data = "/home/kkuma12s/thesis/Proof_Extraction/data/fever-full/"+train_dataset_name+".jsonl"
test_data = "/home/kkuma12s/thesis/Proof_Extraction/data/fever-full/"+test_dataset_name+".jsonl"
claims = []
sents = []
labels = []
with jsonlines.open(train_data, mode='r') as f:
for example in f:
claims.append(example["claim"])
sents.append(example["sentence"])
labels.append(example["label"])
preprocess = preProcessing()
x_claim, x_sents, x_labels, claim_word_train_index, sents_word_train_index = preprocess.to_padding(claims,
sents, labels, vocab_size_of_claims, vocab_size_of_sents,
max_claims_length, max_sents_length)
claims = []
sents = []
labels = []
with jsonlines.open(test_data, mode='r') as f:
for example in f:
claims.append(example["claim"])
sents.append(example["sentence"])
labels.append(example["label"])
test_claims_data, test_sents_data, test_labels, claim_word_test_index, sents_word_test_index= preprocess.to_padding(claims,
sents, labels, vocab_size_of_claims, vocab_size_of_sents,
max_claims_length, max_sents_length)
return (x_claim, x_sents, x_labels, test_claims_data, test_sents_data, test_labels)
if __name__ == '__main__':
x_claim, x_sents, x_labels, test_claims_data, test_sents_data, test_labels= data()
best_run, best_model = optim.minimize(model=lstm_model,
data=data,
algo=tpe.suggest,
max_evals=10,
trials=Trials())
print ("best model ", best_model.evaluate({'claims': test_claims_data, 'sentences': test_sents_data}, test_labels, verbose=0))
print ("best run ", best_run)
Error:
Traceback (most recent call last):
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/home/kkuma12s/thesis/Proof_Extraction/models/DeepLearningModels/lstm/tune_hyperas_fever_full.py", line 191, in <module>
trials=Trials())
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperas/optim.py", line 69, in minimize
keep_temp=keep_temp)
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperas/optim.py", line 139, in base_minimizer
return_argmin=True),
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperopt/fmin.py", line 388, in fmin
show_progressbar=show_progressbar,
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperopt/base.py", line 639, in fmin
show_progressbar=show_progressbar)
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperopt/fmin.py", line 407, in fmin
rval.exhaust()
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperopt/fmin.py", line 262, in exhaust
self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/hyperopt/fmin.py", line 198, in run
disable=not self.show_progressbar, dynamic_ncols=True,
File "/home/kkuma12s/anaconda3/envs/bert_env/lib/python3.6/site-packages/tqdm/_tqdm.py", line 850, in __init__
self.set_postfix(refresh=False, **postfix)
TypeError: set_postfix() argument after ** must be a mapping, not str
