Fixed test failures (2d9a7197) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+17 −33

Original line number	Diff line number	Diff line
		@@ -47,19 +47,6 @@ class Layer(object):
		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		raise NotImplementedError("Subclasses must implement for themselves")

		def __key(self):
		return self.name

		def __eq__(x, y):
		if x is None or y is None:
		return False
		if type(x) != type(y):
		return False
		return x.__key() == y.__key()

		def __hash__(self):
		return hash(self.__key())

		def shared(self, in_layers):
		"""
		Share weights with different in tensors and a new out tensor
		@@ -143,8 +130,6 @@ class Dense(Layer):
		biases_initializer=tf.zeros_initializer,
		weights_initializer=tf.contrib.layers.variance_scaling_initializer,
		time_series=False,
		scope_name=None,
		reuse=False,
		**kwargs):
		"""Create a dense layer.

		@@ -166,10 +151,6 @@ class Dense(Layer):
		the initializer for weight values
		time_series: bool
		if True, the dense layer is applied to each element of a batch in sequence
		scope_name: str
		an optional scope name for the layer's variables
		reuse: bool
		whether or not the layer and its variables should be reused
		"""
		super(Dense, self).__init__(**kwargs)
		self.out_channels = out_channels
		@@ -178,8 +159,8 @@ class Dense(Layer):
		self.biases_initializer = biases_initializer
		self.weights_initializer = weights_initializer
		self.time_series = time_series
		self.reuse = reuse
		self.scope_name = scope_name
		self._reuse = False
		self._shared_with = None

		def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
		if in_layers is None:
		@@ -192,10 +173,6 @@ class Dense(Layer):
		biases_initializer = None
		else:
		biases_initializer = self.biases_initializer()
		if self.scope_name is None:
		scope_name = self.name
		else:
		scope_name = self.scope_name
		if not self.time_series:
		self.out_tensor = tf.contrib.layers.fully_connected(
		parent.out_tensor,
		@@ -203,8 +180,8 @@ class Dense(Layer):
		activation_fn=self.activation_fn,
		biases_initializer=biases_initializer,
		weights_initializer=self.weights_initializer(),
		scope=scope_name,
		reuse=self.reuse,
		scope=self._get_scope_name(),
		reuse=self._reuse,
		trainable=True)
		return self.out_tensor
		dense_fn = lambda x: tf.contrib.layers.fully_connected(x,
		@@ -212,8 +189,8 @@ class Dense(Layer):
		activation_fn=self.activation_fn,
		biases_initializer=biases_initializer,
		weights_initializer=self.weights_initializer(),
		scope=self.scope_name,
		reuse=self.reuse,
		scope=self._get_scope_name(),
		reuse=self._reuse,
		trainable=True)
		out_tensor = tf.map_fn(dense_fn, parent.out_tensor)
		if set_tensors:
		@@ -223,16 +200,23 @@ class Dense(Layer):
		return out_tensor

		def shared(self, in_layers):
		self.reuse = True
		return Dense(
		copy = Dense(
		self.out_channels,
		self.activation_fn,
		self.biases_initializer,
		self.weights_initializer,
		time_series=self.time_series,
		reuse=self.reuse,
		scope_name=self.scope_name,
		in_layers=in_layers)
		self._reuse = True
		copy._reuse = True
		copy._shared_with = self
		return copy

		def _get_scope_name(self):
		if self._shared_with is None:
		return self.name
		else:
		return self._shared_with._get_scope_name()


		class Flatten(Layer):

deepchem/models/tensorgraph/tensor_graph.py

+1 −0

Original line number	Diff line number	Diff line
		@@ -349,6 +349,7 @@ class TensorGraph(Model):
		shapes = []
		pre_q_inputs = []
		q = InputFifoQueue(shapes, names, in_layers=pre_q_inputs)
		q.name = "%s_%s" % (q.__class__.__name__, len(self.layers)+1)

		for layer in self.features + self.labels + self.task_weights:
		pre_q_input = layer.create_pre_q(self.batch_size)

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