ValueError:检查时出错:预期dense_1_input具有形状(3,)但得到形状为(1,)的数组 [英] ValueError: Error when checking : expected dense_1_input to have shape (3,) but got array with shape (1,)

问题描述

我正在尝试使用学习的 .h5 文件进行预测.学习模型如下.

I am trying to predict using the learned .h5 file. The learning model is as follows.

model =Sequential()
model.add(Dense(12, input_dim=3, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(4, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])

而我把输入的形式写成如下.

And I wrote the form of the input as follows.

x = np.array([[band1_input[input_cols_loop][input_rows_loop]],[band2_input[input_cols_loop][input_rows_loop]],[band3_input[input_cols_loop][input_rows_loop]]])

prediction_prob = model.predict(x)

我认为形状是正确的，但出现了以下错误.

I thought the shape was correct, but the following error occurred.

ValueError:检查时出错:预期dense_1_input 具有形状(3,) 但得到形状为(1,) 的数组

ValueError: Error when checking : expected dense_1_input to have shape (3,) but got array with shape (1,)

x的形状显然是(3,1)，但是上面的错误并没有消失(数据来自一个csv文件，格式为<代码>(值 1，值 2，值 3，类)).

The shape of x is obviously (3,1), but the above error doesn't disappear (the data is from a csv file in the form of (value 1, value 2, value 3, class)).

我该如何解决这个问题?

How can I solve this problem?

推荐答案

x的形状明明是(3,1)，但是上面的错误还在继续.

The shape of x is obviously (3,1), but the above error continues.

你是对的，但这不是 keras 所期望的.它期望 (1, 3) 形状:按照惯例，轴 0 表示批量大小，轴 1 表示特征.第一个 Dense 层接受 3 个特征，这就是为什么当它只看到一个时它会抱怨.

You are right, but that's not what keras expects. It expects (1, 3) shape: by convention, axis 0 denotes the batch size and axis 1 denotes the features. The first Dense layer accepts 3 features, that's why it complains when it sees just one.

解决方案很简单，只需转置x.

The solution is simply to transpose x.

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