Merge pull request #603 from vipulraheja/add-selu-activation (d257e334) · Commits · 钟慕尧 / deepchem

deepchem/nn/activations.py

+21 −4

Original line number	Diff line number	Diff line
		@@ -12,8 +12,12 @@ import tensorflow as tf
		from deepchem.nn import model_ops
		from deepchem.nn.model_ops import get_ndim

		def get_from_module(identifier, module_params, module_name,
		instantiate=False, kwargs=None):

		def get_from_module(identifier,
		module_params,
		module_name,
		instantiate=False,
		kwargs=None):
		"""Retrieves a class of function member of a module.

		Parameters
		@@ -41,8 +45,7 @@ def get_from_module(identifier, module_params, module_name,
		if isinstance(identifier, six.string_types):
		res = module_params.get(identifier)
		if not res:
		raise ValueError('Invalid ' + str(module_name) + ': ' +
		str(identifier))
		raise ValueError('Invalid ' + str(module_name) + ': ' + str(identifier))
		if instantiate and not kwargs:
		return res()
		elif instantiate and kwargs:
		@@ -52,6 +55,7 @@ def get_from_module(identifier, module_params, module_name,

		return identifier


		def softmax(x):
		ndim = get_ndim(x)
		if ndim == 2:
		@@ -65,30 +69,43 @@ def softmax(x):
		'that is not 2D or 3D. '
		'Here, ndim=' + str(ndim))


		def elu(x, alpha=1.0):
		return model_ops.elu(x, alpha)


		def selu(x):
		return model_ops.selu(x)


		def softplus(x):
		return tf.nn.softplus(x)


		def softsign(x):
		return tf.nn.softsign(x)


		def relu(x, alpha=0., max_value=None):
		return model_ops.relu(x, alpha=alpha, max_value=max_value)


		def tanh(x):
		return tf.nn.tanh(x)


		def sigmoid(x):
		return tf.nn.sigmoid(x)


		def hard_sigmoid(x):
		return model_ops.hard_sigmoid(x)


		def linear(x):
		return x


		def get(identifier):
		if identifier is None:
		return linear

+25 −5

Original line number	Diff line number	Diff line
		@@ -645,6 +645,26 @@ def relu(x, alpha=0., max_value=None):
		return x


		def selu(x):
		"""Scaled Exponential Linear unit.

		Parameters
		----------
		x: A tensor or variable.

		Returns
		-------
		A tensor.

		References
		----------
		- [Self-Normalizing Neural Networks](https://arxiv.org/abs/1706.02515)
		"""
		alpha = 1.6732632423543772848170429916717
		scale = 1.0507009873554804934193349852946
		return scale * elu(x, alpha)


		def hard_sigmoid(x):
		"""Segment-wise linear approximation of sigmoid.
		Faster than sigmoid.