ymnliu’s gists

ymnliu / run_cnn.sh

Last active October 24, 2019 21:47

	# activate the conda environment to use
	conda activate tutorial

	# go to the work dir, or do "git clone" to download the dir from scratch.
	cd AMIA19_W22_large_scale_nlp/scripts/

	# run script
	python cnn_separate_sense.py

ymnliu / cnn_result.txt

Last active October 23, 2019 22:16

	########################################
	MS
	########################################
	_________________________________________________________________
	Layer (type) Output Shape Param #
	=================================================================
	embedding_1 (Embedding) (None, 100, 50) 268350
	_________________________________________________________________
	dropout_1 (Dropout) (None, 100, 50) 0
	_________________________________________________________________

ymnliu / svm_vec_result.txt

Last active October 22, 2019 22:34

	########################################
	MS
	########################################
	/Users/m142167/anaconda3/lib/python3.7/site-packages/sklearn/metrics/classification.py:1437: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.
	'precision', 'predicted', average, warn_for)
	precision recall f1-score support
	UNSURED SENSE 0.00 0.00 0.00 1
	morphine sulfate 0.97 0.97 0.97 30
	multiple sclerosis 0.85 0.94 0.89 18
	musculoskeletal 0.00 0.00 0.00 1

ymnliu / AttentionWithContext.py

Created October 11, 2017 16:01 — forked from cbaziotis/AttentionWithContext.py

Keras Layer that implements an Attention mechanism, with a context/query vector, for temporal data. Supports Masking. Follows the work of Yang et al. [https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf] "Hierarchical Attention Networks for Document Classification"

	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':

ymnliu / keras_bidirectional_tagger.py

Created August 3, 2017 16:39 — forked from dirko/keras_bidirectional_tagger.py

Keras bidirectional LSTM NER tagger

	# Keras==1.0.6
	from keras.models import Sequential
	import numpy as np
	from keras.layers.recurrent import LSTM
	from keras.layers.core import TimeDistributedDense, Activation
	from keras.preprocessing.sequence import pad_sequences
	from keras.layers.embeddings import Embedding
	from sklearn.cross_validation import train_test_split
	from keras.layers import Merge
	from keras.backend import tf

ymnliu / lambdawithmask.py

Created June 21, 2017 14:56 — forked from braingineer/lambdawithmask.py

keras lambda layer supporting masking

	class Lambda(Layer):
	'''Used for evaluating an arbitrary Theano / TensorFlow expression
	on the output of the previous layer.

	# Examples

	```python
	# add a x -> x^2 layer
	model.add(Lambda(lambda x: x ** 2))
	```

ymnliu / keras_unidirectional_tagger.py

Last active June 21, 2017 14:44 — forked from dirko/keras_unidirectional_tagger.py

Keras LSTM NER tagger

	# Keras==2.0.2
	import numpy as np
	from keras.models import Sequential
	from keras.layers.recurrent import LSTM
	from keras.layers.core import Activation, Dense
	from keras.layers.wrappers import TimeDistributed
	from keras.preprocessing.sequence import pad_sequences
	from keras.layers.embeddings import Embedding
	from sklearn.cross_validation import train_test_split
	from sklearn.metrics import confusion_matrix, accuracy_score, precision_recall_fscore_support

Sijia Liu ymnliu