Attempting to make pickle work (32161482) · Commits · 钟慕尧 / deepchem

deepchem/models/tensorgraph/layers.py

+30 −31

Original line number	Diff line number	Diff line
		@@ -1388,30 +1388,34 @@ class LSTMStep(Layer):
		input_dim,
		init='glorot_uniform',
		inner_init='orthogonal',
		forget_bias_init='one',
		activation='tanh',
		inner_activation='hard_sigmoid',
		**kwargs):

		super(LSTMStep, self).__init__(**kwargs)

		self.init = init
		self.inner_init = inner_init
		self.output_dim = output_dim

		self.init = initializations.get(init)
		self.inner_init = initializations.get(inner_init)
		# No other forget biases supported right now.
		assert forget_bias_init == "one"
		self.forget_bias_init = initializations.get(forget_bias_init)
		self.activation = activations.get(activation)
		self.inner_activation = activations.get(inner_activation)
		self.activation = activation
		self.inner_activation = inner_activation
		self.input_dim = input_dim

		def get_initial_states(self, input_shape):
		return [model_ops.zeros(input_shape), model_ops.zeros(input_shape)]

		def build(self):
		self.W = self.init((self.input_dim, 4 * self.output_dim))
		self.U = self.inner_init((self.output_dim, 4 * self.output_dim))
		init = initializations.get(self.init)
		inner_init = initializations.get(self.inner_init)
		########################################################## DEBUG
		print("build()")
		print("self.input_dim")
		print(self.input_dim)
		########################################################## DEBUG
		self.W = init((self.input_dim, 4 * self.output_dim))
		self.U = inner_init((self.output_dim, 4 * self.output_dim))

		self.b = tf.Variable(
		np.hstack((np.zeros(self.output_dim), np.ones(self.output_dim),
		@@ -1440,6 +1444,9 @@ class LSTMStep(Layer):
		list
		Returns h, [h + c]
		"""
		activation = activations.get(self.activation)
		inner_activation = activations.get(self.inner_activation)

		self.build()
		if in_layers is None:
		in_layers = self.in_layers
		@@ -1447,6 +1454,12 @@ class LSTMStep(Layer):
		x, h_tm1, c_tm1 = inputs

		# Taken from Keras code [citation needed]
		########################################################## DEBUG
		print("x")
		print(x)
		print("self.W")
		print(self.W)
		########################################################## DEBUG
		z = model_ops.dot(x, self.W) + model_ops.dot(h_tm1, self.U) + self.b

		z0 = z[:, :self.output_dim]
		@@ -1454,15 +1467,16 @@ class LSTMStep(Layer):
		z2 = z[:, 2 * self.output_dim:3 * self.output_dim]
		z3 = z[:, 3 * self.output_dim:]

		i = self.inner_activation(z0)
		f = self.inner_activation(z1)
		c = f * c_tm1 + i * self.activation(z2)
		o = self.inner_activation(z3)
		i = inner_activation(z0)
		f = inner_activation(z1)
		c = f * c_tm1 + i * activation(z2)
		o = inner_activation(z3)

		h = o * self.activation(c)
		h = o * activation(c)

		self.out_tensor = h, [h, c]
		return self.out_tensor
		# TODO(rbharath): Is this correct?
		self.out_tensor = h
		return h, [h, c]

		def cos(x, y):
		"""Computes the inner preduct (cosine distance) between two tensors.
		@@ -1637,21 +1651,6 @@ class IterRefLSTMEmbedding(Layer):

		self.trainable_weights = []

		#def get_output_shape_for(self, input_shape):
		# """Returns the output shape. Same as input_shape.

		# Parameters
		# ----------
		# input_shape: list
		# Will be of form [(n_test, n_feat), (n_support, n_feat)]

		# Returns
		# -------
		# list
		# Of same shape as input [(n_test, n_feat), (n_support, n_feat)]
		# """
		# return input_shape

		def create_tensor(self, in_layers=None, set_tensors=True):
		"""Execute this layer on input tensors.

deepchem/models/tensorgraph/tensor_graph.py

+8 −0

Original line number	Diff line number	Diff line
		@@ -389,6 +389,10 @@ class TensorGraph(Model):
		for node in order:
		with tf.name_scope(node):
		node_layer = self.layers[node]
		########################################################### DEBUG
		print("node_layer")
		print(node_layer)
		########################################################### DEBUG
		node_layer.create_tensor(training=self._training_placeholder)
		self.rnn_initial_states += node_layer.rnn_initial_states
		self.rnn_final_states += node_layer.rnn_final_states
		@@ -506,6 +510,10 @@ class TensorGraph(Model):

		# Pickle itself
		pickle_name = os.path.join(self.model_dir, "model.pickle")
		######################################################### DEBUG
		print("self")
		print(self)
		######################################################### DEBUG
		with open(pickle_name, 'wb') as fout:
		try:
		pickle.dump(self, fout)

deepchem/models/tensorgraph/tests/test_layers_pickle.py

+6 −6

Original line number	Diff line number	Diff line
		@@ -444,14 +444,14 @@ def test_MP_pickle():

		def test_LSTMStep_pickle():
		"""Tests that LSTMStep can be pickled."""
		n_test = 100
		n_feat = 20
		tg = TensorGraph()
		y = Feature(shape=(n_test, n_feat))
		state_zero = Feature(shape=(n_test, n_feat))
		state_one = Feature(shape=(n_test, n_feat))
		n_feat = 10
		tg = TensorGraph(use_queue=False)
		y = Feature(shape=(None, 2*n_feat))
		state_zero = Feature(shape=(None, n_feat))
		state_one = Feature(shape=(None, n_feat))
		lstm = LSTMStep(n_feat, 2*n_feat, in_layers=[y, state_zero, state_one])
		tg.add_output(lstm)
		tg.set_loss(lstm)
		tg.build()
		tg.save()

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