Keras 負維度 size Conv2D

Question

我一直在玩 kernel 大小和頻道安排一段時間，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變化有多大會影響與論文中 model 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model

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'))

我收到的錯誤信息

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
    return op(input, filter)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py", line 966, in __call__
    return self.conv_op(inp, filter)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py", line 591, in __call__
    return self.call(inp, filter)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/nn_ops.py", line 208, in __call__
    name=self.name)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/gen_nn_ops.py", line 1026, in conv2d
    data_format=data_format, dilations=dilations, name=name)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py", line 788, in _apply_op_helper
    op_def=op_def)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/util/deprecation.py", line 507, in new_func
    return func(*args, **kwargs)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 3300, in create_op
    op_def=op_def)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1823, in __init__
    control_input_ops)
  File "/Users/zacharyfrederick/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/ops.py", line 1662, in _create_c_op
    raise ValueError(str(e))
ValueError: 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].

Answer 1

你有這個錯誤是因為你的 Kernels 和 strides 對於你的輸入來說太大了，一個常見的開始是使用形狀為(3, 3)和 strides (1, 1)的內核。

嘗試閱讀如何計算卷積，讓您直觀地了解如何設置正確的內核/步幅大小： http://cs231n.github.io/convolutional.networks/

此外，你有一個channel first的輸入，所以你首先設置你的第一個 conv channel first ，這很好，但是你對所有的卷積都這樣做，因為默認情況下 keras 卷積將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'))

Answer 2

另一個答案在診斷中是正確的：卷積后你的圖像變小，在某個時候 kernel 變得比圖像大。 嘗試

1) 降低您的 kernel 尺寸或

2）添加, padding='same'到你的卷積層。

使用計算卷積層中的 Output 大小來計算輸出大小。