(X,Y),(test_x,test_y)=cifar.load_data(one_hot=True)
X=X.reshape([-1,32,32,3])
test_x=test_x.reshape([-1,32,32,3])
convnet=input_data(shape=[None,32,32,3],name='input')
convnet=conv_2d(convnet,32,3,activation='relu')
convnet=max_pool_2d(convnet,2)
convnet=conv_2d(convnet,64,3,activation='relu')
convnet=max_pool_2d(convnet,2)
convnet=conv_2d(convnet,128,3,activation='relu')
convnet=conv_2d(convnet,128,3,activation='relu')
convnet=max_pool_2d(convnet,2)
convnet=fully_connected(convnet,512,activation='relu')
convnet=fully_connected(convnet,512,activation='relu')
convnet=dropout(convnet,0.8)
convnet=fully_connected(convnet,10,activation='softmax')
convnet=regression(convnet,optimizer='adam',learning_rate=0.001,loss='categorical_crossentropy')
model=tflearn.DNN(convnet)
model.fit(X,Y,n_epoch=1,validation_set=(test_x,test_y),batch_size=100,snapshot_step=1000,show_metric=True)
model.save('tflearn.model')
'''
model.load('tflearn.model')
print(model.predict(test_x[1]))
'''
当我尝试预测时，它显示错误: “无法为张量 u'input/X:0' 提供形状为 (32, 32, 3) 的值，其形状为“(?, 32, 32, 3)”。
请有人帮忙。
最佳答案
您需要对要进行预测的输入使用tf.expand_dims:
# 't2' is a tensor of shape [32, 32, 3]
shape(expand_dims(t2, axis=0)) ==> [1, 32, 32, 3]
关于python - 当我使用 tflearn 预测测试用例时，它显示 "Cannot feed value of shape (32, 32, 3) for Tensor u' input/X : 0', which has shape ' (? , 32, 32, 3)"，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/42473034/