使用keras时出现内存错误 [英] Memory error while using keras

问题描述

我正在为CNN使用keras，但问题是内存泄漏.错误是

I am using keras for CNN but the problem is that there is memory leak. The error is

        anushreej@cpusrv-gpu-109:~/12EC35005/MTP_Workspace/MTP$ python cnn_implement.py
        Using Theano backend.
        [INFO] compiling model...
        Traceback (most recent call last):
          File "cnn_implement.py", line 23, in <module>
            model = CNNModel.build(width=150, height=150, depth=3)
          File "/home/ms/anushreej/12EC35005/MTP_Workspace/MTP/cnn/networks/model_define.py", line 27, in build
            model.add(Dense(depth*height*width))
          File "/home/ms/anushreej/anaconda3/lib/python3.5/site-packages/keras/models.py", line 146, in add
            output_tensor = layer(self.outputs[0])
          File "/home/ms/anushreej/anaconda3/lib/python3.5/site-packages/keras/engine/topology.py", line 458, in __call__
            self.build(input_shapes[0])
          File "/home/ms/anushreej/anaconda3/lib/python3.5/site-packages/keras/layers/core.py", line 604, in build
            name='{}_W'.format(self.name))
          File "/home/ms/anushreej/anaconda3/lib/python3.5/site-packages/keras/initializations.py", line 61, in glorot_uniform
            return uniform(shape, s, name=name)
          File "/home/ms/anushreej/anaconda3/lib/python3.5/site-packages/keras/initializations.py", line 32, in uniform
            return K.variable(np.random.uniform(low=-scale, high=scale, size=shape),
          File "mtrand.pyx", line 1255, in mtrand.RandomState.uniform (numpy/random/mtrand/mtrand.c:13575)
          File "mtrand.pyx", line 220, in mtrand.cont2_array_sc (numpy/random/mtrand/mtrand.c:2902)
        MemoryError

现在我不明白为什么会这样.我的训练图像非常小，尺寸为150 * 150 * 3.

Now I am unable to understand why is this happening. My training images are very small of the size 150*150*3.

代码是-:

        # import the necessary packages
        from keras.models import Sequential
        from keras.layers.convolutional import Convolution2D
        from keras.layers.core import Activation
        from keras.layers.core import Flatten
        from keras.layers.core import Dense

        class CNNModel:
          @staticmethod
          def build(width, height, depth):
            # initialize the model
            model = Sequential()
            # first set of CONV => RELU
            model.add(Convolution2D(50, 5, 5, border_mode="same", batch_input_shape=(None, depth, height, width)))
            model.add(Activation("relu"))

            # second set of CONV => RELU
            # model.add(Convolution2D(50, 5, 5, border_mode="same"))
            # model.add(Activation("relu"))

            # third set of CONV => RELU
            # model.add(Convolution2D(50, 5, 5, border_mode="same"))
            # model.add(Activation("relu"))

            model.add(Flatten())

            model.add(Dense(depth*height*width))

            # if weightsPath is not None:
            #   model.load_weights(weightsPath) 

            return model

推荐答案

我遇到了同样的问题，我认为问题在于Flattening层之前的数据点数超出了系统的处理能力(我在其他系统中尝试过因此ram的工作量较高而ram较少的人给出了此错误).只需添加更多的CNN层以减小尺寸，然后添加一个有效的展平层即可.

I faced the same problem, I think the issue is the number data points just before the Flattening layer are more than your system can handle(i tried in difference systems so one with high ram worked and with less ram gave this error). Just add more CNN layers to reduce the size and then add a flattening layer it works.

这给了我一个错误:

model = Sequential()
model.add(Convolution2D(32, 3, 3,border_mode='same',input_shape=(1, 96, 96),activation='relu'))
model.add(Convolution2D(64, 3, 3,border_mode='same',activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(1000,activation='relu'))
model.add(Dense(97,activation='softmax'))

这没有给出错误

model = Sequential()
model.add(Convolution2D(32, 3, 3,border_mode='same',input_shape=(1, 96, 96),activation='relu'))
model.add(Convolution2D(64, 3, 3,border_mode='same',activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Convolution2D(64, 3, 3,border_mode='same',activation='relu'))
model.add(Convolution2D(128, 3, 3,border_mode='same',activation='relu'))
model.add(MaxPooling2D((2,2), strides=(2,2)))
model.add(Flatten())
model.add(Dense(1000,activation='relu'))
model.add(Dense(97,activation='softmax')

希望有帮助.

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