Arrays 如何修复层之间阵列形状的不匹配
我正在构建钢筋DNN（DQN），但在将数据发送到模型时出现以下错误：Arrays 如何修复层之间阵列形状的不匹配,arrays,keras,deep-learning,Arrays,Keras,Deep Learning,我正在构建钢筋DNN（DQN），但在将数据发送到模型时出现以下错误： ValueError：检查目标时出错：预期密集_2有2个维度，但得到了形状为（64,4,1）的数组。 我使用（1139）的输入，最小批量为64，使其为：（641139） 我对模型进行了总结： Model: "sequential_1" _________________________________________________________________ Layer (type) O
ValueError：检查目标时出错：预期密集_2有2个维度，但得到了形状为（64,4,1）的数组。 我使用（1139）的输入，最小批量为64，使其为：（641139） 我对模型进行了总结：
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_1 (LSTM) (None, 1, 128) 137216
_________________________________________________________________
dropout_1 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 1, 128) 512
_________________________________________________________________
lstm_2 (LSTM) (None, 1, 128) 131584
_________________________________________________________________
dropout_2 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 1, 128) 512
_________________________________________________________________
dense_1 (Dense) (None, 1, 32) 4128
_________________________________________________________________
dropout_3 (Dropout) (None, 1, 32) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 32) 0
_________________________________________________________________
dense_2 (Dense) (None, 4) 132
=================================================================
Total params: 274,084
Trainable params: 273,572
Non-trainable params: 512
_________________________________________________________________
None
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_3 (LSTM) (None, 1, 128) 137216
_________________________________________________________________
dropout_4 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 1, 128) 512
_________________________________________________________________
lstm_4 (LSTM) (None, 1, 128) 131584
_________________________________________________________________
dropout_5 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 1, 128) 512
_________________________________________________________________
dense_3 (Dense) (None, 1, 32) 4128
_________________________________________________________________
dropout_6 (Dropout) (None, 1, 32) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 32) 0
_________________________________________________________________
dense_4 (Dense) (None, 4) 132
=================================================================
Total params: 274,084
Trainable params: 273,572
Non-trainable params: 512
_________________________________________________________________
None
展平层不应该使其成为二维阵列吗？有什么想法吗-/ 这条线没有任何意义
model.add（展平（））
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_1 (LSTM) (None, 1, 128) 137216
_________________________________________________________________
dropout_1 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 1, 128) 512
_________________________________________________________________
lstm_2 (LSTM) (None, 1, 128) 131584
_________________________________________________________________
dropout_2 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 1, 128) 512
_________________________________________________________________
dense_1 (Dense) (None, 1, 32) 4128
_________________________________________________________________
dropout_3 (Dropout) (None, 1, 32) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 32) 0
_________________________________________________________________
dense_2 (Dense) (None, 4) 132
=================================================================
Total params: 274,084
Trainable params: 273,572
Non-trainable params: 512
_________________________________________________________________
None
Model: "sequential_2"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
lstm_3 (LSTM) (None, 1, 128) 137216
_________________________________________________________________
dropout_4 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 1, 128) 512
_________________________________________________________________
lstm_4 (LSTM) (None, 1, 128) 131584
_________________________________________________________________
dropout_5 (Dropout) (None, 1, 128) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 1, 128) 512
_________________________________________________________________
dense_3 (Dense) (None, 1, 32) 4128
_________________________________________________________________
dropout_6 (Dropout) (None, 1, 32) 0
_________________________________________________________________
flatten_2 (Flatten) (None, 32) 0
_________________________________________________________________
dense_4 (Dense) (None, 4) 132
=================================================================
Total params: 274,084
Trainable params: 273,572
Non-trainable params: 512
_________________________________________________________________
None