local (87f92ee1) · Commits · 钟慕尧 / deepchem

contrib/dragonn/GTC_workshop_tutorial.ipynb

+15 −6

Original line number	Diff line number	Diff line
		@@ -41,7 +41,7 @@
		},
		{
		"cell_type": "code",
		"execution_count": 1,
		"execution_count": 10,
		"metadata": {
		"collapsed": false
		},
		@@ -50,17 +50,26 @@
		"name": "stderr",
		"output_type": "stream",
		"text": [
		"/home/rbharath/anaconda3/envs/deepchem/lib/python3.6/site-packages/matplotlib-1.5.3-py3.6-linux-x86_64.egg/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
		" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
		"/home/rbharath/anaconda3/envs/deepchem/lib/python3.6/site-packages/matplotlib-1.5.3-py3.6-linux-x86_64.egg/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
		" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
		"/home/rbharath/anaconda3/envs/deepchem/lib/python3.6/site-packages/matplotlib-1.5.3-py3.6-linux-x86_64.egg/matplotlib/__init__.py:1357: UserWarning: This call to matplotlib.use() has no effect\n",
		"because the backend has already been chosen;\n",
		"matplotlib.use() must be called before pylab, matplotlib.pyplot,\n",
		"or matplotlib.backends is imported for the first time.\n",
		"\n",
		" warnings.warn(_use_error_msg)\n",
		"Using Theano backend.\n",
		"/home/rbharath/anaconda3/envs/deepchem/lib/python3.6/site-packages/matplotlib-1.5.3-py3.6-linux-x86_64.egg/matplotlib/__init__.py:1357: UserWarning: This call to matplotlib.use() has no effect\n",
		"because the backend has already been chosen;\n",
		"matplotlib.use() must be called before pylab, matplotlib.pyplot,\n",
		"or matplotlib.backends is imported for the first time.\n",
		"\n",
		" warnings.warn(_use_error_msg)\n"
		]
		}
		],
		"source": [
		"%reload_ext autoreload\n",
		"%autoreload 2\n",
		"from dragonn.tutorial_utils import *\n",
		"from tutorial_utils import *\n",
		"%matplotlib inline"
		]
		},

contrib/dragonn/models.py

+5 −6

Original line number	Diff line number	Diff line
		@@ -12,12 +12,17 @@ from dragonn.metrics import ClassificationResult
		from sklearn.svm import SVC as scikit_SVC
		from sklearn.tree import DecisionTreeClassifier as scikit_DecisionTree
		from sklearn.ensemble import RandomForestClassifier
		from keras.models import model_from_json
		from keras.models import Sequential
		from keras.layers.core import (Activation, Dense, Dropout, Flatten, Permute,
		Reshape, TimeDistributedDense)
		from keras.layers.convolutional import Convolution2D, MaxPooling2D
		from keras.layers.recurrent import GRU
		from keras.regularizers import l1
		from keras.layers.core import (Activation, Dense, Flatten,
		TimeDistributedDense)
		from keras.layers.recurrent import GRU
		from keras.callbacks import EarlyStopping


		class Model(object):
		@@ -327,7 +332,6 @@ class SequenceDNN(Model):

		@staticmethod
		def load(arch_fname, weights_fname=None):
		from keras.models import model_from_json
		model_json_string = open(arch_fname).read()
		sequence_dnn = SequenceDNN(keras_model=model_from_json(model_json_string))
		if weights_fname is not None:
		@@ -338,10 +342,6 @@ class SequenceDNN(Model):
		class MotifScoreRNN(Model):

		def __init__(self, input_shape, gru_size=10, tdd_size=4):
		from keras.models import Sequential
		from keras.layers.core import (Activation, Dense, Flatten,
		TimeDistributedDense)
		from keras.layers.recurrent import GRU
		self.model = Sequential()
		self.model.add(
		GRU(gru_size, return_sequences=True, input_shape=input_shape))
		@@ -354,7 +354,6 @@ class MotifScoreRNN(Model):
		self.model.compile(optimizer='adam', loss='binary_crossentropy')

		def train(self, X, y, validation_data):
		from keras.callbacks import EarlyStopping
		print('Training model...')
		multitask = y.shape[1] > 1
		if not multitask:

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