Keras - Nan in summary histogram LSTM

问题

I've written an LSTM model using Keras, and using LeakyReLU advance activation:

    # ADAM Optimizer with learning rate decay
    opt = optimizers.Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0001)

    # build the model
    model = Sequential()

    num_features = data.shape[2]
    num_samples = data.shape[1]

    model.add(
        LSTM(16, batch_input_shape=(None, num_samples, num_features), return_sequences=True, activation='linear'))
    model.add(LeakyReLU(alpha=.001))
    model.add(Dropout(0.1))
    model.add(LSTM(8, return_sequences=True, activation='linear'))
    model.add(Dropout(0.1))
    model.add(LeakyReLU(alpha=.001))
    model.add(Flatten())
    model.add(Dense(1, activation='sigmoid'))

    model.compile(loss='binary_crossentropy', optimizer=opt,
                  metrics=['accuracy', keras_metrics.precision(), keras_metrics.recall(), f1])

My data is a balanced binary labeled set. i.e: 50% labeled 1 50% labeled 0. I've used activation='linear' for the LSTM layers preceding the LeakyReLU activation, similar to this example I found on GitHub.

The model throws Nan in summary histogram error in that configuration. Changing the LSTM activations to activation='sigmoid' works well, but seems like the wrong thing to do.

Reading this StackOverflow question suggested "introducing a small value when computing the loss", I'm just not sure how to do it on a built-in loss function.

Any help/explanation would be appreciated.

Update: I can see that the loss is nan on the first epoch

260/260 [==============================] - 6s 23ms/step - 
loss: nan - acc: 0.5000 - precision: 0.5217 - recall: 0.6512 - f1: nan - val_loss: nan - val_acc: 0.0000e+00 - val_precision: -2147483648.0000 - val_recall: -49941480.1860 - val_f1: nan

Update 2 I've upgraded both TensorFlow & Keras to versions 1.12.0 & 2.2.4 . There was no effect.

I also tried adding a loss to the first LSTM layer as suggested by @Oluwafemi Sule, it looks like a step in the right direction, now the loss is not nan on the first epoch, however, I still get the same error ... probably because of other nan values, like the val_loss / val_f1.

[==============================] - 7s 26ms/step - 
loss: 1.9099 - acc: 0.5077 - precision: 0.5235 - recall: 0.6544 - f1: 0.5817 - val_loss: nan - val_acc: 0.5172 - val_precision: 35.0000 - val_recall: 0.9722 - val_f1: nan

Update 3 I tried to compile the network with just the accuracy metric, with no success:

Epoch 1/300
260/260 [==============================] - 8s 29ms/step - loss: nan - acc: 0.5538 - val_loss: nan - val_acc: 0.0000e+00

回答1:

This answers starts from the suggestion to introduce a small value when computing the loss.

keras.layers.LSTM as with all layers that are direct or indirect subclasses of keras.engine.base_layer.Layer has a add_loss method that can be used to set a starting value for the loss.

I suggest to do this for the LSTM layer and see if it makes any difference for your results.

lstm_layer = LSTM(8, return_sequences=True, activation='linear')
lstm_layer.add_loss(1.0)

model.add(lstm_layer)

来源：https://stackoverflow.com/questions/53080289/keras-nan-in-summary-histogram-lstm

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python

tensorflow

keras

lstm

activation-function