序号 | 出版社一般写法 | 出版地 | 备注 |
---|---|---|---|
1 | AAAI | Menlo Park, CA | Association for the Advancement of Artificial Intelligence |
2 | Academic | 同Elsevier | Academic Press is part of Elsevier |
3 | Academy Press | New York/ London/ Paris/ San Diedo,CA/ San Francisco,CA/ Sao Paulo/ Sydney/ Tokyo/Toronto | AP,Academy Press |
4 | ACL | Stroudsburg,PA | Association for Computational Linguistics |
5 | ACM | New York, NY | ACM Press,Association for Computing and Machinery |
6 | AP Professional | Boston,MA/ San Diedo,CA/New York/ London/ Sydney/ Tokyo/ Toronto | |
7 | Chapman & Hall | London/ Glasgow/ Weinheim/ New York/ Madras | CH |
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def dot_product(x, kernel): | |
""" | |
Wrapper for dot product operation, in order to be compatible with both | |
Theano and Tensorflow | |
Args: | |
x (): input | |
kernel (): weights | |
Returns: | |
""" | |
if K.backend() == 'tensorflow': |
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# from https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data | |
def tf_confusion_metrics(model, actual_classes, session, feed_dict): | |
predictions = tf.argmax(model, 1) | |
actuals = tf.argmax(actual_classes, 1) | |
ones_like_actuals = tf.ones_like(actuals) | |
zeros_like_actuals = tf.zeros_like(actuals) | |
ones_like_predictions = tf.ones_like(predictions) | |
zeros_like_predictions = tf.zeros_like(predictions) |