How to apply LSTM with CNN

0
votes

My input is of shape (1, 12000, 250, 150, 3) with labels as (1, 12000, 2) for the CNN. In other words, I am training a CNN on 250x150x3 images with 2 classes; [1,0] or [0,1].

This is ultimately to create a bot to play flappy bird. I have been told that adding LSTMs to classify a few frame concurrently is the way to go. So far I got to 0.984 val_acc with the following purely CNN architecture.

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()

The accuracy:

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

I have tried add a LSTM layer but I'm not sure what is going wrong:

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 says the arguments for LSTM are (units, input shape) and so on. I also read somewhere that TimeDistributed() is no longer needed so I didnt include it. Did I make a mistake in calculating the input shape for LSTM or am I missing something else completely?

Edit 1: I have removed flatten() layer and moved LSTM layer to right after conv layers, before fc layers. I have also added a reshape() so as to reshape the 4 dim output of the 4th conv layer to 3 dim which can then be input to the LSTM layer. 

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))

These are the shapes of every output:

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

The following occurs when I tried to reshape the conv_4 so as to get a 3 dim input into LSTM:

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

Any help is greatly appreciated.

2
pythonkerasconv-neural-networklstm

2 Answers

0
votes

first, i do not see lstm in your model, its just 4 convo to 3 full connected right? Why do you have 2 Conv2D right after one another?

LSTM over frames i would do instead of first full connected right after flatten.

I dont know in Keras, but input in any RNN cell is 3D array, like : (batch size, max sequence, items) or (max_sequence, bach_size, items) , the second format is sort of weird.

The error you got is : expected ndim=3, found ndim=2

so i guess you enter 2D array instead of 3D

You sould modify your flatten to create valide 3D input. This can you do for example by having 5d input but 2d convo like: bach size = 100, frames = 3, channels = 3, items = 28,28 (height, width), flatten to (100, 3, -1) where -1 stands for rest.

I need to try similar stuff myself but i m doing in pytorch...

0
votes

If I had scrolled down a little more, I would have found ConvLSTM2D in the docs and this should solve my problem. Will try it now