Cleaning up (a01e688d) · Commits · 钟慕尧 / deepchem

deepchem/models/layers.py

+0 −6

Original line number	Diff line number	Diff line
		@@ -2694,7 +2694,6 @@ class DTNNEmbedding(tf.keras.layers.Layer):
		initializer=self.init,
		trainable=True)

		#init = initializers.get(self.init)
		self.embedding_list = init([self.periodic_table_length, self.n_embedding])
		self.built = True

		@@ -2755,7 +2754,6 @@ class DTNNStep(tf.keras.layers.Layer):
		initializer=self.init,
		trainable=True)

		#init = initializers.get(self.init)
		self.W_cf = init([self.n_embedding, self.n_hidden])
		self.W_df = init([self.n_distance, self.n_hidden])
		self.W_fc = init([self.n_hidden, self.n_embedding])
		@@ -2848,7 +2846,6 @@ class DTNNGather(tf.keras.layers.Layer):
		initializer=self.init,
		trainable=True)

		#init = initializers.get(self.init)
		prev_layer_size = self.n_embedding
		for i, layer_size in enumerate(self.layer_sizes):
		self.W_list.append(init([prev_layer_size, layer_size]))
		@@ -3264,7 +3261,6 @@ class EdgeNetwork(tf.keras.layers.Layer):

		n_pair_features = self.n_pair_features
		n_hidden = self.n_hidden
		#init = initializers.get(self.init)
		self.W = init([n_pair_features, n_hidden * n_hidden])
		self.b = backend.zeros(shape=(n_hidden * n_hidden,))
		self.built = True
		@@ -3302,7 +3298,6 @@ class GatedRecurrentUnit(tf.keras.layers.Layer):
		initializer=self.init,
		trainable=True)

		#init = initializers.get(self.init)
		self.Wz = init([n_hidden, n_hidden])
		self.Wr = init([n_hidden, n_hidden])
		self.Wh = init([n_hidden, n_hidden])
		@@ -3365,7 +3360,6 @@ class SetGather(tf.keras.layers.Layer):
		initializer=self.init,
		trainable=True)

		#init = initializers.get(self.init)
		self.U = init((2 * self.n_hidden, 4 * self.n_hidden))
		self.b = tf.Variable(
		np.concatenate((np.zeros(self.n_hidden), np.ones(self.n_hidden),

deepchem/models/tests/test_reload.py

+5 −10

Original line number	Diff line number	Diff line
		@@ -773,6 +773,10 @@ def test_textCNN_classification_reload():
		model_dir=model_dir)
		reloaded_model.restore()

		# Eval model on train
		scores = reloaded_model.evaluate(dataset, [classification_metric])
		assert scores[classification_metric.name] > .8

		assert len(reloaded_model.model.get_weights()) == len(
		model.model.get_weights())
		for (reloaded, orig) in zip(reloaded_model.model.get_weights(),
		@@ -785,18 +789,9 @@ def test_textCNN_classification_reload():
		predset = dc.data.NumpyDataset(Xpred, ids=predmols)
		origpred = model.predict(predset)
		reloadpred = reloaded_model.predict(predset)

		Xproc = reloaded_model.smiles_to_seq_batch(np.array(predmols))
		reloadout = reloaded_model.model(Xproc)
		origout = model.model(Xproc)

		assert len(model.model.layers) == len(reloaded_model.model.layers)

		assert np.all(origpred == reloadpred)

		# Eval model on train
		scores = reloaded_model.evaluate(dataset, [classification_metric])
		assert scores[classification_metric.name] > .8
		assert len(model.model.layers) == len(reloaded_model.model.layers)


		def test_1d_cnn_regression_reload():

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