Merge branch 'master' into WEAVE_reload (669a311f) · Commits · 钟慕尧 / deepchem

deepchem/models/layers.py

+24 −8

Original line number	Diff line number	Diff line
		@@ -2951,10 +2951,18 @@ class DAGLayer(tf.keras.layers.Layer):
		init = initializers.get(self.init)
		prev_layer_size = self.n_inputs
		for layer_size in self.layer_sizes:
		self.W_list.append(init([prev_layer_size, layer_size]))
		self.b_list.append(backend.zeros(shape=[
		layer_size,
		]))
		self.W_list.append(
		self.add_weight(
		name='kernel',
		shape=(prev_layer_size, layer_size),
		initializer=self.init,
		trainable=True))
		self.b_list.append(
		self.add_weight(
		name='bias',
		shape=(layer_size,),
		initializer='zeros',
		trainable=True))
		if self.dropout is not None and self.dropout > 0.0:
		self.dropouts.append(Dropout(rate=self.dropout))
		else:
		@@ -3084,10 +3092,18 @@ class DAGGather(tf.keras.layers.Layer):
		init = initializers.get(self.init)
		prev_layer_size = self.n_graph_feat
		for layer_size in self.layer_sizes:
		self.W_list.append(init([prev_layer_size, layer_size]))
		self.b_list.append(backend.zeros(shape=[
		layer_size,
		]))
		self.W_list.append(
		self.add_weight(
		name='kernel',
		shape=(prev_layer_size, layer_size),
		initializer=self.init,
		trainable=True))
		self.b_list.append(
		self.add_weight(
		name='bias',
		shape=(layer_size,),
		initializer='zeros',
		trainable=True))
		if self.dropout is not None and self.dropout > 0.0:
		self.dropouts.append(Dropout(rate=self.dropout))
		else:

docs/examples.rst

+75 −77

Original line number	Diff line number	Diff line
		@@ -103,7 +103,7 @@ For a :class:`GraphConvModel <deepchem.models.GraphConvModel>`, we'll reload our
		>>> assert valid_scores['mean-pearson_r2_score'] > 0.3, valid_scores


		..

		ChEMBL
		-------

		@@ -127,32 +127,31 @@ For a :class:`GraphConvModel <deepchem.models.GraphConvModel>`, we'll reload our
		>>> f'Compound train/valid/test split: {len(train_dataset)}/{len(valid_dataset)}/{len(test_dataset)}'
		'Compound train/valid/test split: 19096/2387/2388'
		>>>
		>>> # We want to know the pearson R squared score, averaged across tasks
		>>> avg_pearson_r2 = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)
		>>> # We want to know the RMS, averaged across tasks
		>>> avg_rms = dc.metrics.Metric(dc.metrics.rms_score, np.mean)
		>>>
		>>> # Create our model
		>>> n_layers = 3
		>>> model = dc.models.MultitaskRegressor(
		... len(chembl_tasks),
		... train_dataset.get_data_shape()[0],
		... n_features=1024,
		... layer_sizes=[1000] * n_layers,
		... dropouts=[.25] * n_layers,
		... weight_init_stddevs=[.02] * n_layers,
		... bias_init_consts=[1.] * n_layers,
		... learning_rate=.0003,
		... weight_decay_penalty=.0001,
		... batch_size=100,
		... verbosity="high")
		... batch_size=100)
		>>>
		>>> model.fit(train_dataset, nb_epoch=20)
		>>> model.fit(train_dataset, nb_epoch=5)
		0...
		>>>
		>>> # We now evaluate our fitted model on our training and validation sets
		>>> train_scores = model.evaluate(train_dataset, [avg_pearson_r2], transformers)
		>>> assert train_scores['mean-pearson_r2_score'] > 0.00 # is currently nan
		>>> train_scores = model.evaluate(train_dataset, [avg_rms], transformers)
		>>> assert train_scores['mean-rms_score'] < 10.00
		>>>
		>>> valid_scores = model.evaluate(valid_dataset, [avg_pearson_r2], transformers)
		>>> assert valid_scores['mean-pearson_r2_score'] > 0.00 # is currently nan
		>>> valid_scores = model.evaluate(valid_dataset, [avg_rms], transformers)
		>>> assert valid_scores['mean-rms_score'] < 10.00

		GraphConvModel
		^^^^^^^^^^^^^^
		@@ -164,20 +163,19 @@ For a :class:`GraphConvModel <deepchem.models.GraphConvModel>`, we'll reload our
		... shard_size=2000, featurizer="GraphConv", set="5thresh", split="random")
		>>> train_dataset, valid_dataset, test_dataset = datasets
		>>>
		>>> # pearson R squared score, averaged across tasks
		>>> avg_pearson_r2 = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)
		>>> # RMS, averaged across tasks
		>>> avg_rms = dc.metrics.Metric(dc.metrics.rms_score, np.mean)
		>>>
		>>> model = dc.models.GraphConvModel(
		... len(chembl_tasks), batch_size=128, mode='regression')
		>>>
		>>> # Fit trained model
		>>> model.fit(train_dataset, nb_epoch=20)
		>>> model.fit(train_dataset, nb_epoch=5)
		0...
		>>>
		>>> # We now evaluate our fitted model on our training and validation sets
		>>> train_scores = model.evaluate(train_dataset, [avg_pearson_r2], transformers)
		>>> assert train_scores['mean-pearson_r2_score'] > 0.00 # is currently nan
		>>> train_scores = model.evaluate(train_dataset, [avg_rms], transformers)
		>>> assert train_scores['mean-rms_score'] < 10.00
		>>>
		>>> valid_scores = model.evaluate(valid_dataset, [avg_pearson_r2], transformers)
		>>> assert valid_scores['mean-pearson_r2_score'] > 0.00 # is currently nan

		>>> valid_scores = model.evaluate(valid_dataset, [avg_rms], transformers)
		>>> assert valid_scores['mean-rms_score'] < 10.00

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