Python - 重塑不起作用
Python - Reshape not working
我正在使用 keras 训练 CNN，基本错误是维度不匹配。
原因，经过调试是：
print("Before")
print(TX.shape)
print(TeX.shape)
X_train = TX.reshape(1000, 1, img_rows, img_cols)
X_test = TeX.reshape(430, 1, img_rows, img_cols)
print("After")
print(TX.shape)
print(TeX.shape)
生成输出：
Using Theano backend.
Using gpu device 0: GeForce GTX 750 Ti (CNMeM is disabled, CuDNN not available)
Before
(1000, 27, 36)
(430, 27, 36)
After
(1000, 27, 36)
(430, 27, 36)
如果需要，我的模型摘要是：
____________________________________________________________________________________________________
层（类型）输出形状参数#连接到
convolution2d_1 (Convolution2D) (None, 32, 25, 34) 320 convolution2d_input_1[0][0]
activation_1（激活）(None, 32, 25, 34) 0 convolution2d_1[0][0]
convolution2d_2 (Convolution2D) (None, 32, 23, 32) 9248 activation_1[0][0]
activation_2（激活）(None, 32, 23, 32) 0 convolution2d_2[0][0]
convolution2d_3 (Convolution2D) (None, 32, 21, 30) 9248 activation_2[0][0]
activation_3（激活）(None, 32, 21, 30) 0 convolution2d_3[0][0]
maxpooling2d_1 (MaxPooling2D) (None, 32, 10, 15) 0 activation_3[0][0]
dropout_1（辍学）(None, 32, 10, 15) 0 maxpooling2d_1[0][0]
flatten_1（展平）(None, 4800) 0 dropout_1[0][0]
dense_1（密集）(None, 128) 614528 flatten_1[0][0]
activation_4（激活）(None, 128) 0 dense_1[0][0]
dropout_2 (辍学) (None, 128) 0 activation_4[0][0]
dense_2 (密集) (None, 26) 3354 dropout_2[0][0]
activation_5（激活）(None, 26) 0 dense_2[0][0]
总参数：636698
您正在将重塑后的数组分配给一个新变量，但您仍在打印旧变量的形状：
X_train = TX.reshape(..)
您必须使用：
print(X_train.shape)
我正在使用 keras 训练 CNN，基本错误是维度不匹配。
原因，经过调试是：
print("Before")
print(TX.shape)
print(TeX.shape)
X_train = TX.reshape(1000, 1, img_rows, img_cols)
X_test = TeX.reshape(430, 1, img_rows, img_cols)
print("After")
print(TX.shape)
print(TeX.shape)
生成输出：
Using Theano backend.
Using gpu device 0: GeForce GTX 750 Ti (CNMeM is disabled, CuDNN not available)
Before
(1000, 27, 36)
(430, 27, 36)
After
(1000, 27, 36)
(430, 27, 36)
如果需要，我的模型摘要是：
____________________________________________________________________________________________________
层（类型）输出形状参数#连接到
convolution2d_1 (Convolution2D) (None, 32, 25, 34) 320 convolution2d_input_1[0][0]
activation_1（激活）(None, 32, 25, 34) 0 convolution2d_1[0][0]
convolution2d_2 (Convolution2D) (None, 32, 23, 32) 9248 activation_1[0][0]
activation_2（激活）(None, 32, 23, 32) 0 convolution2d_2[0][0]
convolution2d_3 (Convolution2D) (None, 32, 21, 30) 9248 activation_2[0][0]
activation_3（激活）(None, 32, 21, 30) 0 convolution2d_3[0][0]
maxpooling2d_1 (MaxPooling2D) (None, 32, 10, 15) 0 activation_3[0][0]
dropout_1（辍学）(None, 32, 10, 15) 0 maxpooling2d_1[0][0]
flatten_1（展平）(None, 4800) 0 dropout_1[0][0]
dense_1（密集）(None, 128) 614528 flatten_1[0][0]
activation_4（激活）(None, 128) 0 dense_1[0][0]
dropout_2 (辍学) (None, 128) 0 activation_4[0][0]
dense_2 (密集) (None, 26) 3354 dropout_2[0][0]
activation_5（激活）(None, 26) 0 dense_2[0][0]
总参数：636698
您正在将重塑后的数组分配给一个新变量，但您仍在打印旧变量的形状：
X_train = TX.reshape(..)
您必须使用：
print(X_train.shape)