我试图了解如何在 Keras 上编写 Conv1D 模型,但我不断遇到维度不匹配错误。现在 x_train = [1000,294] (1000 个项目和 294 个特征)和 y_train = [1000,9] (1000 个项目和 9 个标签)。我不断收到错误消息需要 3 个维度,但得到了 2 个维度。但我尝试修复它,它又出现了。 github 上的一些问题建议使用 Flatten()
但它已经存在并且没有运气。知道我缺少什么吗?谢谢
我得到的错误,
- ValueError:输入 0 与层 conv1d_1 不兼容:预期 ndim=3,找到ndim=2
代码包含在下面
tb = [keras.callbacks.TensorBoard(log_dir='./dllogs')]
main_input = Input(shape=(294, ), dtype='float32')
x_train = np.array(x_train)
y_train = np.array(y_train)
x_dev = np.array(x_dev)
y_dev = np.array(y_dev)
x = Conv1D(128, 5, activation='relu')(main_input)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(35)(x)
x = Flatten()(x)
x = Dropout(0.25)(x)
x = Dense(128, activation='relu')(x)
preds = Dense(pred_dim, activation='softmax')(x)
model = Model(inputs=main_input, outputs=preds)
model.compile(optimizer='adam', loss='kullback_leibler_divergence', metrics=['accuracy'])
print(model.summary())
history_NN = model.fit(x_train, y_train, batch_size=BATCHSIZE, epochs=EPOCHS, callbacks=tb, validation_data=(x_dev, y_dev))
回答
- 尺寸是一个引起麻烦的问题,Today 的评论解决了这个问题。
- 接下来是池化尺寸,这是池化后尺寸的减少,所以在整个过程之后,当我更改它时,最终池化时没有 35 张幻灯片。固定的。
结果代码,
tb = [keras.callbacks.TensorBoard(log_dir='./dllogs')]
main_input = Input(shape=(294, 1))#changed
x_train = np.array(x_train)
y_train = np.array(y_train)
x_dev = np.array(x_dev)
y_dev = np.array(y_dev)
x_train = np.expand_dims(x_train, axis=-1) #changed
x_dev = np.expand_dims(x_dev, axis=-1) #changed
x = Conv1D(128, 5, activation='relu')(main_input)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(5)(x)
x = Conv1D(128, 5, activation='relu')(x)
x = MaxPooling1D(11)(x)
x = Flatten()(x)
x = Dropout(0.25)(x)
x = Dense(128, activation='relu')(x)
preds = Dense(pred_dim, activation='softmax')(x)
model = Model(inputs=main_input, outputs=preds)
model.compile(optimizer='adam', loss='kullback_leibler_divergence', metrics=['accuracy'])
print(model.summary())
history_NN = model.fit(x_train, y_train, batch_size=BATCHSIZE, epochs=EPOCHS, callbacks=tb, validation_data=(x_dev, y_dev))
最佳答案
Conv1D
层需要形状为 (sequence_length, num_features)
的序列输入。看起来你有长度为 294 的序列,具有 一个 特征;因此,每个输入样本需要具有 (294,1)
的形状(而不是 (294,)
)。要修复此问题,您可以使用 np.expand_dims
将大小为 1 的第三个维度添加到输入数据中:
x_train = np.expand_dims(x_train, axis=-1)
x_dev = np.expand_dims(x_dev, axis=-1)
main_input = Input(shape=(294, 1)) # fix the input shape here as well
关于python - keras 中输入数据不兼容错误,维度不匹配 ValueError,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/57714476/