Merge pull request #1313 from rbharath/tfbinding (da74fdaa) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/models/sequence_dnn.py

+14 −8

Original line number	Diff line number	Diff line
		@@ -5,6 +5,7 @@ repository. The SequenceDNN class is useful for prediction
		tasks working with genomic data.
		"""
		import tensorflow as tf
		import collections
		from deepchem.models import Sequential
		from deepchem.models.tensorgraph import layers
		from deepchem.data import NumpyDataset
		@@ -14,9 +15,6 @@ class SequenceDNN(Sequential):
		"""
		Sequence DNN models.

		# TODO(rbharath): This model only supports one-conv layer. Extend
		# so that conv layers of greater depth can be implemented.

		Parameters
		----------
		seq_length : int
		@@ -27,7 +25,7 @@ class SequenceDNN(Sequential):
		number of tasks. Default: 1.
		num_filters : list[int] \| tuple[int]
		number of convolutional filters in each layer. Default: (15,).
		conv_width : list[int] \| tuple[int]
		kernel_size: list[int] \| tuple[int]
		width of each layer's convolutional filters. Default: (15,).
		pool_width : int
		width of max pooling after the last layer. Default: 35.
		@@ -44,17 +42,25 @@ class SequenceDNN(Sequential):
		loss,
		use_RNN=False,
		num_tasks=1,
		num_filters=15,
		kernel_size=15,
		num_filters=(15,),
		kernel_size=(15,),
		pool_width=35,
		L1=0,
		dropout=0.0,
		verbose=True,
		**kwargs):
		super(SequenceDNN, self).__init__(loss, **kwargs)
		if not isinstance(num_filters, collections.Sequence) or not isinstance(
		kernel_size, collections.Sequence):
		raise ValueError("num_filters and kernel_size must be lists")
		if not len(num_filters) == len(kernel_size):
		raise ValueError("num_filters and kernel_size must be of same length")
		n_layers = len(num_filters)
		self.num_tasks = num_tasks
		self.verbose = verbose
		self.add(layers.Conv2D(num_filters, kernel_size=kernel_size))
		for layer in range(n_layers):
		self.add(
		layers.Conv2D(num_filters[layer], kernel_size=kernel_size[layer]))
		self.add(layers.Dropout(dropout))
		self.add(layers.Flatten())
		self.add(layers.Dense(self.num_tasks, activation_fn=tf.nn.relu))

deepchem/models/tensorgraph/tests/test_sequencednn.py

+11 −2

Original line number	Diff line number	Diff line
		@@ -19,7 +19,7 @@ class TestSequenceDNN(unittest.TestCase):
		# # False: num_sequences / num_negatives
		# #} if not multitask else None,
		dataset = dc.data.NumpyDataset(X, y)
		model = dc.models.SequenceDNN(50, "binary_crossentropy", num_filters=1)
		model = dc.models.SequenceDNN(50, "binary_crossentropy", num_filters=[1])
		model.fit(dataset, nb_epoch=1)

		def test_seq_dnn_multifilter_train(self):
		@@ -32,5 +32,14 @@ class TestSequenceDNN(unittest.TestCase):
		# # False: num_sequences / num_negatives
		# #} if not multitask else None,
		dataset = dc.data.NumpyDataset(X, y)
		model = dc.models.SequenceDNN(50, "binary_crossentropy", num_filters=15)
		model = dc.models.SequenceDNN(50, "binary_crossentropy", num_filters=[15])
		model.fit(dataset, nb_epoch=1)

		def test_seq_dnn_multilayer_train(self):
		"""Test SequenceDNN with multiple layers works."""
		X = np.random.rand(10, 1, 4, 50)
		y = np.random.randint(0, 2, size=(10, 1))
		dataset = dc.data.NumpyDataset(X, y)
		model = dc.models.SequenceDNN(
		50, "binary_crossentropy", num_filters=[1, 1], kernel_size=[15, 15])
		model.fit(dataset, nb_epoch=1)

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