我正在努力训练CNN（Sklearn神经网络）。我有4个128 x 128像素的图像。形状 - >（4，128，128） 我读相似图片 -在sklearn神经网络中训练CNN图像
in1 = misc.imread('../data/Train_Data/train-1.jpg', mode='L', flatten=True)/255. in2 = misc.imread('../data/Train_Data/train-2.jpg', mode='L', flatten=True)/255. in3 = misc.imread('../data/Train_Data/train-3.jpg', mode='L', flatten=True)/255. in4 = misc.imread('../data/Train_Data/train-4.jpg', mode='L', flatten=True)/255.
然后numpy的阵列被这样创建 -
X_train = [in1,in2,in3,in4]
X_train = np.array(X_train)
同用于标签和测试集。
然后我训练我的CNN -
nn = Classifier(
layers=[
Convolution('Rectifier', channels=12, kernel_shape=(3, 3), border_mode='full'),
Convolution('Rectifier', channels=8, kernel_shape=(3, 3), border_mode='valid'),
Layer('Rectifier', units=64),
Layer('Softmax')],
learning_rate=0.002,
valid_size=0.2,
n_stable=10,
verbose=True)
nn.fit(X_train, y_train)
它抛出错误的 -
Traceback (most recent call last): File "/home/zaverichintan/PycharmProjects/WBC_identification/neural/trial.py", line 91, in nn.fit(X_train, y_train) File "/home/zaverichintan/miniconda2/lib/python2.7/site-packages/sknn/mlp.py", line 383, in fit ys = [lb.fit_transform(y[:,i]) for i, lb in enumerate(self.label_binarizers)] File "/home/zaverichintan/miniconda2/lib/python2.7/site-packages/sklearn/base.py", line 494, in fit_transform return self.fit(X, **fit_params).transform(X) File "/home/zaverichintan/miniconda2/lib/python2.7/site-packages/sklearn/preprocessing/label.py", line 335, in transform sparse_output=self.sparse_output) File "/home/zaverichintan/miniconda2/lib/python2.7/site-packages/sklearn/preprocessing/label.py", line 497, in label_binarize y = column_or_1d(y) File "/home/zaverichintan/miniconda2/lib/python2.7/site-packages/sklearn/utils/validation.py", line 563, in column_or_1d raise ValueError("bad input shape {0}".format(shape)) ValueError: bad input shape (4, 128)