我一直在玩內核大小和通道安排一段時間，但沒有運氣。我不完全確定如何計算 Conv2D 層的校正器參數，我不確定這些參數的更改會在多大程度上影響與論文中模型的相似性。任何幫助將不勝感激。我嘗試根據文獻中的設計構建的模型input_shape = (4, 30, 180)model = Sequential()model.add(Convolution2D(32, (8, 8), strides=(4,4), activation='relu', input_shape=(4,30,180), data_format='channels_first'))model.add(Activation('relu'))model.add(Convolution2D(64, (4, 4), strides=(2, 2)))model.add(Activation('relu'))model.add(Convolution2D(64, (3, 3), strides=(1, 1)))model.add(Activation('relu'))model.add(Flatten())model.add(Dense(512))model.add(Activation('relu'))model.add(Dense(2))model.add(Activation('linear'))我收到的錯誤消息Traceback (most recent call last): File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1659, in _create_c_op c_op = c_api.TF_FinishOperation(op_desc)tensorflow.python.framework.errors_impl.InvalidArgumentError: Negative dimension size caused by subtracting 3 from 2 for 'conv2d_3/convolution' (op: 'Conv2D') with input shapes: [?,15,2,64], [3,3,64,64].During handling of the above exception, another exception occurred:Traceback (most recent call last): File "stock_env.py", line 101, in <module> model.add(Convolution2D(64, (3, 3), strides=(1, 1))) File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/keras/engine/sequential.py", line 181, in add output_tensor = layer(self.outputs[0]) File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/keras/engine/base_layer.py", line 457, in __call__ output = self.call(inputs, **kwargs) File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/keras/layers/convolutional.py", line 171, in call dilation_rate=self.dilation_rate) File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 3650, in conv2d data_format=tf_data_format) File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py", line 851, in convolution
2 回答
不負相思意
TA貢獻1777條經驗 獲得超10個贊
你有這個錯誤是因為你的內核和步幅對于你的輸入來說太大了，一個常見的開始是使用形狀(3, 3)和步幅的內核(1, 1)。
嘗試閱讀如何計算卷積，讓您直觀了解如何設置正確的內核/步幅大小：http ://cs231n.github.io/convolutional-networks/
此外，您有一個輸入channel first，因此您將第一個 conv 設置為channel first，這很好，但是您對所有卷積都執行此操作，因為默認情況下 keras 卷積將使用channel last.
例如，這是有效的：
input_shape = (4, 30, 180)
model = Sequential()
model.add(Conv2D(32, (8, 8), strides=(4, 4), activation='relu', input_shape=(4, 30, 180), data_format='channels_first'))
model.add(Activation('relu'))
model.add(Conv2D(64, (4, 4), strides=(1, 1), data_format='channels_first'))
model.add(Activation('relu'))
model.add(Conv2D(64, (3, 3), strides=(1, 1), data_format='channels_first'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(2))
model.add(Activation('linear'))
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