Skopt gp_minimize eroor while looking for optimal point - tensorflow

Question

I have a problem with gp_minimize from skopt. I'm doing hyperparameter tunning for transfer learning (base model vgg19). Everything works fine until gp_minimize starts to evaluate optimal point (evaluate random point works fine). I have this error, i spend few days on this and i still don't know what to do:
Traceback (most recent call last):
File "C:/Users/mea/Train_models/04_VGG19_train_model.py", line 144, in <module>
search_result = gp_minimize(func=fitness,
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\optimizer\gp.py", line 259, in gp_minimize
return base_minimize(
File "C:\Users\Wme\anaconda3\envs\Train_models\lib\site-packages\skopt\optimizer\base.py", line 300, in base_minimize
result = optimizer.tell(next_x, next_y)
File "C:\Users\mea\anaconda3\envs\Train_models\lib\site-packages\skopt\optimizer\optimizer.py", line 493, in tell
return self._tell(x, y, fit=fit)
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\optimizer\optimizer.py", line 552, in _tell
X = self.space.transform(self.space.rvs(
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\space\space.py", line 900, in rvs
columns.append(dim.rvs(n_samples=n_samples, random_state=rng))
File "C:\Users\Weronika Gramacka\anaconda3\envs\Train_models\lib\site-packages\skopt\space\space.py", line 698, in rvs
return self.inverse_transform(list(choices))
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\space\space.py", line 685, in inverse_transform
inv_transform = super(Categorical, self).inverse_transform(Xt)
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\space\space.py", line 168, in inverse_transform
return self.transformer.inverse_transform(Xt)
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\space\transformers.py", line 309, in inverse_transform
X = transformer.inverse_transform(X)
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\space\transformers.py", line 216, in inverse_transform
return [
File "C:\Users\me\anaconda3\envs\Train_models\lib\site-packages\skopt\space\transformers.py", line 217, in <listcomp>
self.inverse_mapping_[int(np.round(i))] for i in Xt
KeyError: 6
Process finished with exit code 1
Code is from tutorial and looks like this (only important parts):
dim_num_dense_layers = Integer(low=1, high=3, name='num_dense_layers')
dim_num_dense_nodes = Integer(low=60, high=1500, name='num_dense_nodes')
dim_activation = Categorical(categories=[ 'sigmoid', 'tanh', 'relu', 'softmax'],
name='activation')
dim_dropout = Real(low = 0.01, high = 0.4, name = 'dropout')
dim_init = Categorical(categories = ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'], name = 'kernel_initializer')
dim_loss = Categorical(categories = ['categorical_crossentropy', 'categorical_hinge', 'mean_squared_error', 'huber_loss'], name = 'loss')
dimensions = [dim_num_dense_layers,
dim_num_dense_nodes,
dim_activation,
dim_dropout,
dim_init,
dim_loss]
default_parameters = [2, 600, 'relu', 0.2, 'uniform', 'huber_loss']
#use_named_args(dimensions=dimensions)
def fitness(num_dense_layers, num_dense_nodes, activation, dropout, kernel_initializer, loss):
# Print the hyper-parameters.
keras.backend.clear_session()
model = create_model(num_dense_layers, num_dense_nodes, activation, dropout, kernel_initializer, loss)
log_dir = "Tensor_board/04/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = TensorBoard(
log_dir='log_dir',
histogram_freq=1,
write_graph=True,
write_grads=False,
write_images=False)
history = model.fit(train_data,
epochs=1,
batch_size=32,
validation_data=val_data,
callbacks=[tensorboard_callback])
accuracy = max(history.history['val_accuracy'])
global best_accuracy
if accuracy > best_accuracy:
model.save("Models/vgg19_flat.h5")
best_accuracy = accuracy
del model
gc.collect()
keras.backend.clear_session()
return -accuracy
checkpoint_saver = CheckpointSaver("./checkpoint.pkl", compress=9)
search_result = gp_minimize(func=fitness,
dimensions=dimensions,
acq_func='EI',
n_calls=30,
n_initial_points=1,
x0=default_parameters, verbose=True, callback=[checkpoint_saver])