Question

输入形状

X_train.shape
Out[29]: (90000, 9)

这是我的模特：

def cnn_1d(window_size,nb_input_series):
    model = Sequential()
    model.add(Conv1D(32, 9, activation='relu', input_shape=(window_size, nb_input_series)))
    model.add(Conv1D(32, 9, activation='relu'))
    model.add(MaxPooling1D(2))
    model.add(Dropout(0.25))
    model.add(Conv1D(64, 9, activation='relu'))
    model.add(Conv1D(64, 9, activation='relu'))
    model.add(MaxPooling1D(pool_size=2))
    model.add(Dropout(0.25))

    model.add(Flatten())
    model.add(Dense(50, activation='relu'))
    model.add(Dropout(0.5))
    model.add(Dense(1, activation='sigmoid'))



model=cnn_1d(1,X_train.shape[1])

但错误提升

ValueError: Negative dimension size caused by subtracting 9 from 1 for 'conv1d_11/convolution/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1,9], [1,9,9,32].

需要：

我应该使用嵌入吗？
需要重塑吗？

提前致谢...

Answer 1

Conv1D图层接受3D输入。您的X_train应该重塑为

(no_samples, steps, input_dim)

Answer 2

您必须重新塑造数据

(no_of_samples/timesteps,timesteps,input_dim)

Keras-conv1d用于不平衡时间序列分类的时间序列

2 个答案: