如何在Keras模型中使用TensorFlow的采样softmax损失函数?

There is definitely a more elegant way to do this, but I believe this works, so I figured I'd post it here now as-is rather than wait until I have something that's a little bit neater. For example, you'd probably want to make the number of classes an argument of the SampledSoftmax layer, or better yet, condense this all into the loss function as in the original question and avoid passing in the training data twice.from keras.models import Modelfrom keras.layers import Input, Dense, Layerfrom keras import backend as Kclass SampledSoftmax(Layer): def __init__(self, **kwargs): super(SampledSoftmax, self).__init__(**kwargs) def call(self, inputs): """ The first input should be the model as it were, and the second the target (i.e., a repeat of the training data) to compute the labels argument """ # the labels input to this function is batch size by 1, where the # value at position (i, 1) is the index that is true (not zero) # e.g., (0, 0, 1) => (2) or (0, 1, 0, 0) => (1) return K.tf.nn.sampled_softmax_loss(weights=inputs[0]._keras_history[0].weights[0], biases=inputs[0]._keras_history[0].bias, inputs=inputs[0], labels=K.tf.reshape(K.tf.argmax(inputs[1], 1), [-1, 1]), num_sampled=1000, num_classes=200000)def custom_loss(y_true, y_pred): return K.tf.reduce_mean(y_pred)num_classes = 200000input = Input(shape=(300,))target_input = Input(shape=(num_classes,))dense = Dense(num_classes)outputs = dense(input)outputs = SampledSoftmax()([outputs, target_input])model = Model([input, target_input], outputs)model.compile(optimizer=u'adam', loss=custom_loss)# train as desired 这篇关于如何在Keras模型中使用TensorFlow的采样softmax损失函数?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！ 1403页，肝出来的..