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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