keras 1D卷积输入形状 [英] keras 1D convolution input shape

问题描述

我正在尝试为1D卷积创建模型，但似乎无法正确输入形状.这是我所拥有的:

I am trying to create a model for 1D convolution, but I cant seem to get the input shape correct. Here is what I have:

#this is actually shape (6826, 9000) but I am shortening it
train_dataset_x = np.array([[0, 1, 5, 1, 10], [0, 2, 4, 1, 3]])
#this is actually shape (6826, 1)
train_dataset_y = np.array([[0], [1]])

model.add(Conv1D(32, 11, padding='valid', activation='relu', strides=1, input_shape=( len(train_dataset_x[0]), train_dataset_x.shape[1]) ))
model.add(Conv1D(32, 3, padding='valid', activation='relu', strides=1) )
model.add(MaxPooling1D())

model.add(Conv1D(64, 3, padding='valid', activation='relu', strides=1) )
model.add(Conv1D(64, 3, padding='valid', activation='relu', strides=1) )
model.add(MaxPooling1D())


model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

我收到此错误:

ValueError: Error when checking input: expected conv1d_1_input to have 3 dimensions, but got array with shape (6826, 9000)

有人有建议吗?

推荐答案

对 keras.layers.Conv1D 的输入应为3维，尺寸为(nb_of_examples，时间步长，特征).我假设您有一个长度为 6000 且具有1个功能的序列.在这种情况下:

Input to keras.layers.Conv1D should be 3-d with dimensions (nb_of_examples, timesteps, features). I assume that you have a sequence of length 6000 with 1 feature. In this case:

X = X.reshape((-1, 9000, 1))

应该做这项工作.

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