Hi.
Can you provide a self-contained example to reproduce on the issue-tracker?
Maybe you used warm_start=True but changed something about the dataset,
like going from 125 classes to 128?
This works:
from sklearn.neural_network import MLPClassifier
gen_class =
MLPClassifier(hidden_layer_sizes=(200,),max_iter=3000,learning_rate='adaptive',alpha=0.025,warm_start=True)
X_train = np.random.uniform(size=(398, 50))
y_train = np.random.uniform(size=398) > .5
gen_class.fit(X_train, y_train)
best,
Andy
On 10/07/2016 09:51 AM, Aakash Agarwal wrote:
Hi Guys,
I am playing around MLP classifier lately. So i have about 450 inputs
to classify. Each input is a vector of array size 50. I am trying to
fit the model with 90% as train data.
Size of training data: (398, 50)
Size of testing data: (45, 50)
MLP instantiation:
gen_class =
MLPClassifier(hidden_layer_sizes=(200,),max_iter=3000,learning_rate='adaptive',alpha=0.025,warm_start=True)
Batch size is auto so it is taking 200 as batch_size. But when i am
fitting the classifier model, i am getting following error:
Traceback (most recent call last):
File "intent_detection_classifier_selection.py", line 452, in <module>
sk_class.gen_class_fitting(gen_class,corp_lsi_train,train_label)
File "intent_detection_classifier_selection.py", line 77, in
gen_class_fitting
gen_class.fit(data,label)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py",
line 612, in fit
return self._fit(X, y, incremental=False)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py",
line 372, in _fit
intercept_grads, layer_units, incremental)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py",
line 509, in _fit_stochastic
coef_grads, intercept_grads)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/multilayer_perceptron.py",
line 225, in _backprop
loss = LOSS_FUNCTIONS[self.loss](y, activations[-1])
File
"/usr/local/lib/python2.7/dist-packages/sklearn/neural_network/_base.py",
line 222, in log_loss
return -np.sum(y_true * np.log(y_prob)) / y_prob.shape[0]
ValueError: operands could not be broadcast together with shapes
(200,128) (200,125)
Thanks,
Aakash
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