from sklearn.neural_network import MLPClassifier
#Initialise Multi Layer Perceptron Classifier (MLP)
model = MLPClassifier(alpha = 0.01, batch_size = 256, epsilon = 1e-08, hidden_layer_sizes = (400,), learning_rate = 'adaptive', max_iter = 500)
model.fit(X_train, y_train)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-41-d768f88d541e> in <module>
----> 1 model.fit(X_train, y_train)
5 frames
/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py in fit(self, X, y)
750 Returns a trained MLP model.
751 """
--> 752 return self._fit(X, y, incremental=False)
753
754 def _check_solver(self):
/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py in _fit(self, X, y, incremental)
391 )
392
--> 393 X, y = self._validate_input(X, y, incremental, reset=first_pass)
394
395 n_samples, n_features = X.shape
/usr/local/lib/python3.8/dist-packages/sklearn/neural_network/_multilayer_perceptron.py in _validate_input(self, X, y, incremental, reset)
1098
1099 def _validate_input(self, X, y, incremental, reset):
-> 1100 X, y = self._validate_data(
1101 X,
1102 y,
/usr/local/lib/python3.8/dist-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
579 y = check_array(y, **check_y_params)
580 else:
--> 581 X, y = check_X_y(X, y, **check_params)
582 out = X, y
583
/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)
962 raise ValueError("y cannot be None")
963
--> 964 X = check_array(
965 X,
966 accept_sparse=accept_sparse,
/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
744 array = array.astype(dtype, casting="unsafe", copy=False)
745 else:
--> 746 array = np.asarray(array, order=order, dtype=dtype)
747 except ComplexWarning as complex_warning:
748 raise ValueError(
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (318, 20) + inhomogeneous part.
for i in X_train:
print(i.shape)
(20, 100)
(20, 80)
(20, 69)
(20, 60)
(20, 67)
(20, 60)
(20, 60)
(20, 69)
(20, 63)
(20, 60)
(20, 92)
(20, 70)
(20, 63)
(20, 79)
(20, 53)
(20, 81)
(20, 86)
(20, 69)
(20, 87)
(20, 79)
(20, 76)
(20, 58)
(20, 84)
(20, 89)
(20, 90)
(20, 94)
(20, 117)
(20, 57)
(20, 83)
(20, 63)
(20, 81)
(20, 76)
(20, 84)
(20, 80)
(20, 73)
(20, 120)
(20, 79)
(20, 73)
(20, 81)
(20, 84)
(20, 81)
(20, 86)
(20, 74)
(20, 92)
(20, 84)
(20, 104)
(20, 73)
(20, 54)
(20, 81)
(20, 84)
(20, 76)
(20, 81)
(20, 107)
(20, 109)
(20, 107)
(20, 86)
(20, 94)
(20, 80)
(20, 73)
(20, 69)
(20, 107)
(20, 67)
(20, 79)
(20, 76)
(20, 70)
(20, 56)
(20, 70)
(20, 66)
(20, 71)
(20, 83)
(20, 74)
(20, 64)
(20, 76)
(20, 94)
(20, 69)
(20, 71)
(20, 103)
(20, 80)
(20, 97)
(20, 83)
(20, 79)
(20, 70)
(20, 70)
(20, 99)
(20, 90)
(20, 67)
(20, 64)
(20, 73)
(20, 87)
(20, 71)
(20, 84)
(20, 69)
(20, 84)
(20, 66)
(20, 92)
(20, 90)
(20, 67)
(20, 104)
(20, 71)
(20, 63)
(20, 96)
(20, 79)
(20, 84)
(20, 104)
(20, 54)
(20, 97)
(20, 81)
(20, 61)
(20, 79)
(20, 81)
(20, 79)
(20, 93)
(20, 102)
(20, 107)
(20, 76)
(20, 106)
(20, 94)
(20, 79)
(20, 99)
(20, 66)
(20, 100)
(20, 70)
(20, 83)
(20, 87)
(20, 93)
(20, 76)
(20, 77)
(20, 76)
(20, 73)
(20, 66)
(20, 107)
(20, 71)
(20, 79)
(20, 699)
(20, 57)
(20, 57)
(20, 70)
(20, 107)
(20, 84)
(20, 71)
(20, 71)
(20, 99)
(20, 67)
(20, 77)
(20, 92)
(20, 81)
(20, 76)
(20, 54)
(20, 77)
(20, 63)
(20, 64)
(20, 83)
(20, 66)
(20, 64)
(20, 110)
(20, 81)
(20, 74)
(20, 64)
(20, 76)
(20, 71)
(20, 71)
(20, 87)
(20, 146)
(20, 96)
(20, 97)
(20, 103)
(20, 70)
(20, 60)
(20, 61)
(20, 77)
(20, 70)
(20, 104)
(20, 83)
(20, 96)
(20, 53)
(20, 86)
(20, 64)
(20, 90)
(20, 92)
(20, 64)
(20, 84)
(20, 69)
(20, 63)
(20, 69)
(20, 46)
(20, 50)
(20, 56)
(20, 60)
(20, 100)
(20, 50)
(20, 51)
(20, 96)
(20, 92)
(20, 87)
(20, 84)
(20, 63)
(20, 64)
(20, 90)
(20, 71)
(20, 54)
(20, 126)
(20, 80)
(20, 79)
(20, 63)
(20, 89)
(20, 94)
(20, 77)
(20, 87)
(20, 69)
(20, 67)
(20, 90)
(20, 84)
(20, 117)
(20, 77)
(20, 70)
(20, 80)
(20, 90)
(20, 81)
(20, 81)
(20, 64)
(20, 79)
(20, 56)
(20, 60)
(20, 79)
(20, 73)
(20, 58)
(20, 67)
(20, 89)
(20, 80)
(20, 57)
(20, 96)
(20, 83)
(20, 70)
(20, 81)
(20, 69)
(20, 83)
(20, 80)
(20, 58)
(20, 93)
(20, 64)
(20, 63)
(20, 60)
(20, 64)
(20, 92)
(20, 63)
(20, 80)
(20, 106)
(20, 93)
(20, 63)
(20, 80)
(20, 96)
(20, 90)
(20, 112)
(20, 80)
(20, 90)
(20, 94)
(20, 86)
(20, 94)
(20, 79)
(20, 80)
(20, 76)
(20, 47)
(20, 60)
(20, 76)
(20, 90)
(20, 70)
(20, 96)
(20, 142)
(20, 92)
(20, 89)
(20, 84)
(20, 69)
(20, 71)
(20, 81)
(20, 106)
(20, 63)
(20, 80)
(20, 69)
(20, 86)
(20, 92)
(20, 69)
(20, 83)
(20, 80)
(20, 57)
(20, 61)
(20, 67)
(20, 97)
(20, 94)
(20, 94)
(20, 54)
(20, 76)
(20, 89)
(20, 70)
(20, 79)
(20, 69)
(20, 67)
(20, 53)
(20, 90)
(20, 81)
(20, 94)
(20, 100)
(20, 90)
(20, 70)
(20, 70)
(20, 71)
(20, 83)
(20, 70)
(20, 84)
(20, 86)
(20, 66)
(20, 87)
(20, 70)
(20, 63)
(20, 69)
(20, 94)
(20, 58)
(20, 92)
(20, 83)
need help to solve this error
