Question

我的输入的形状为（1，12000，250，150，3），其CNN标签为（1，12000，2）。换句话说，我正在训练250x150x3图像上2类的CNN； [1,0]或[0,1]。

这最终是创建一个机器人来玩飞扬的鸟。有人告诉我，添加LSTM来同时对几个帧进行分类是可行的方法。到目前为止，我使用以下纯粹的CNN架构达到了0.984 val_acc。

model.add(Conv2D(32, 3, 3, border_mode='same', input_shape=(250,150,3), activation='relu'))
model.add(Conv2D(32, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(Conv2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(Conv2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(256, 3, 3, border_mode='same', activation='relu'))
model.add(Conv2D(256, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))

model.add(Dense(256, activation='relu'))
model.add(Dropout(0.5))

#model.add(LSTM(100, input_shape=(32, 32, 19), return_sequences=True))
model.add(Dense(2))
model.add(Activation('sigmoid'))
model.summary()

准确性：

Epoch 15/100
12800/12800 [==============================] - 89s 7ms/step - loss: 0.0390 - acc: 0.9889 - val_loss: 0.1422 - val_acc: 0.9717
Epoch 16/100
12800/12800 [==============================] - 89s 7ms/step - loss: 0.0395 - acc: 0.9883 - val_loss: 0.0917 - val_acc: 0.9821ss: - ETA: 1s - loss: 0.0399 - acc:
Epoch 17/100
12800/12800 [==============================] - 89s 7ms/step - loss: 0.0357 - acc: 0.9902 - val_loss: 0.1383 - val_acc: 0.9816
Epoch 18/100
12800/12800 [==============================] - 89s 7ms/step - loss: 0.0452 - acc: 0.9871 - val_loss: 0.1153 - val_acc: 0.9750
Epoch 19/100
12800/12800 [==============================] - 90s 7ms/step - loss: 0.0417 - acc: 0.9892 - val_loss: 0.1641 - val_acc: 0.9668
Epoch 20/100
12800/12800 [==============================] - 90s 7ms/step - loss: 0.0339 - acc: 0.9904 - val_loss: 0.0927 - val_acc: 0.9840

我尝试添加一个LSTM层，但是我不确定出了什么问题：

ValueError                                Traceback (most recent call last)
<ipython-input-6-59e402ac3b8a> in <module>
     26 model.add(Dropout(0.5))
     27 
---> 28 model.add(LSTM(100, input_shape=(32, 19), return_sequences=True))
     29 
     30 model.add(Dense(2))

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\engine\sequential.py in add(self, layer)
    179                 self.inputs = network.get_source_inputs(self.outputs[0])
    180         elif self.outputs:
--> 181             output_tensor = layer(self.outputs[0])
    182             if isinstance(output_tensor, list):
    183                 raise TypeError('All layers in a Sequential model '

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\layers\recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs)
    530 
    531         if initial_state is None and constants is None:
--> 532             return super(RNN, self).__call__(inputs, **kwargs)
    533 
    534         # If any of `initial_state` or `constants` are specified and are Keras

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
    412                 # Raise exceptions in case the input is not compatible
    413                 # with the input_spec specified in the layer constructor.
--> 414                 self.assert_input_compatibility(inputs)
    415 
    416                 # Collect input shapes to build layer.

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\engine\base_layer.py in assert_input_compatibility(self, inputs)
    309                                      self.name + ': expected ndim=' +
    310                                      str(spec.ndim) + ', found ndim=' +
--> 311                                      str(K.ndim(x)))
    312             if spec.max_ndim is not None:
    313                 ndim = K.ndim(x)

ValueError: Input 0 is incompatible with layer lstm_2: expected ndim=3, found ndim=2

Keras docs说LSTM的参数是（单位，输入形状）等等。我还读到某个地方不再需要TimeDistributed（），所以没有包含它。在为LSTM计算输入形状时我是否犯了一个错误，还是我完全遗漏了其他东西？

编辑1：我删除了flatten（）层，并将LSTM层移到了conv层之后，fc层之前。我还添加了reshape（），以便将第4个conv层的4个dim输出重塑为3个dim，然后可以将其输入到LSTM层。

model.add(Conv2D(32, 3, 3, border_mode='same', input_shape=(250,150,3), activation='relu'))
model.add(Conv2D(32, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
output_1 = model.output_shape

model.add(Conv2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(Conv2D(64, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
output_2 = model.output_shape

model.add(Conv2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(Conv2D(128, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
output_3 = model.output_shape

model.add(Conv2D(256, 3, 3, border_mode='same', activation='relu'))
model.add(Conv2D(256, 3, 3, border_mode='same', activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
output_4 = model.output_shape

model.add(Reshape((15, 9)))
output_5 = model.output_shape
model.add(LSTM(100, input_shape=(15, 9, 256), return_sequences=True))

这些是每个输出的形状：

Conv_1: (None, 125, 75, 32)
Conv_2: (None, 62, 37, 64)
Conv_3: (None, 31, 18, 128)
Conv_4: (None, 15, 9, 256)

当我尝试重塑conv_4以使LSTM获得3个暗淡输入时，会发生以下情况：

ValueError                                Traceback (most recent call last)
<ipython-input-21-7f5240e41ae4> in <module>
     22 output_4 = model.output_shape
     23 
---> 24 model.add(Reshape((15, 9)))
     25 output_5 = model.output_shape
     26 model.add(LSTM(100, input_shape=(15, 9, 256), return_sequences=True))

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\engine\sequential.py in add(self, layer)
    179                 self.inputs = network.get_source_inputs(self.outputs[0])
    180         elif self.outputs:
--> 181             output_tensor = layer(self.outputs[0])
    182             if isinstance(output_tensor, list):
    183                 raise TypeError('All layers in a Sequential model '

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
    472             if all([s is not None
    473                     for s in to_list(input_shape)]):
--> 474                 output_shape = self.compute_output_shape(input_shape)
    475             else:
    476                 if isinstance(input_shape, list):

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\layers\core.py in compute_output_shape(self, input_shape)
    396             # input shape known? then we can compute the output shape
    397             return (input_shape[0],) + self._fix_unknown_dimension(
--> 398                 input_shape[1:], self.target_shape)
    399 
    400     def call(self, inputs):

E:\Applications\Anaconda3\envs\pygpu\lib\site-packages\keras\layers\core.py in _fix_unknown_dimension(self, input_shape, output_shape)
    384             output_shape[unknown] = original // known
    385         elif original != known:
--> 386             raise ValueError(msg)
    387 
    388         return tuple(output_shape)

ValueError: total size of new array must be unchanged

非常感谢您的帮助。

Answer 1

首先，我在您的模型中没有看到lstm，它只有4个convo到3个完全连接对不对？为什么您一个接一个地拥有2个Conv2D？

我会在框架上使用LSTM，而不是在展平后立即进行第一次完全连接。

我在Keras中不知道，但是任何RNN单元中的输入都是3D数组，例如：（批处理大小，最大序列，项目）或（max_sequence，bach_size，项目），第二种格式有点奇怪。

您得到的错误是： expected ndim=3, found ndim=2

所以我想您输入2D数组而不是3D

您已修改展平图以创建有效的3D输入。例如，您可以通过输入5d但使用2d convo来做到这一点，例如： bach大小= 100，框架= 3，通道= 3，项目= 28,28（高度，宽度），展平为（100，3，-1），其中-1代表休息。

我需要自己尝试类似的东西，但是我正在用火炬做...

Answer 2

如果我向下滚动几下，我会在文档中找到ConvLSTM2D，这应该可以解决我的问题。立即尝试

如何在CNN上应用LSTM

2 个答案: