Merge pull request #448 from ktaneishi/bt_gcv (c860937b) · Commits · 钟慕尧 / deepchem

examples/benchmark.py

+82 −78

Original line number	Diff line number	Diff line
		@@ -495,25 +495,27 @@ def benchmark_classification(train_dataset,
		sess = tf.Session(graph=g)
		K.set_session(sess)
		# Building graph convolution model
		with g.as_default():
		tf.set_random_seed(seed)
		graph_model = dc.nn.SequentialGraph(n_features)
		graph_model.add(dc.nn.GraphConv(int(n_filters), activation='relu'))
		graph_model.add(
		dc.nn.GraphConv(int(n_filters), n_features, activation='relu'))
		graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
		graph_model.add(dc.nn.GraphPool())
		graph_model.add(dc.nn.GraphConv(int(n_filters), activation='relu'))
		graph_model.add(
		dc.nn.GraphConv(int(n_filters), int(n_filters), activation='relu'))
		graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
		graph_model.add(dc.nn.GraphPool())
		# Gather Projection
		graph_model.add(
		dc.nn.Dense(int(n_fully_connected_nodes), activation='relu'))
		dc.nn.Dense(
		int(n_fully_connected_nodes), int(n_filters), activation='relu'))
		graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
		graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))
		with tf.Session() as sess:
		model_graphconv = dc.models.MultitaskGraphClassifier(
		sess,
		graph_model,
		len(tasks),
		n_features,
		batch_size=batch_size,
		learning_rate=learning_rate,
		optimizer_type="adam",
		@@ -693,25 +695,27 @@ def benchmark_regression(train_dataset,
		sess = tf.Session(graph=g)
		K.set_session(sess)
		# Building graph convoluwtion model
		with g.as_default():
		tf.set_random_seed(seed)
		graph_model = dc.nn.SequentialGraph(n_features)
		graph_model.add(dc.nn.GraphConv(int(n_filters), activation='relu'))
		graph_model.add(
		dc.nn.GraphConv(int(n_filters), n_features, activation='relu'))
		graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
		graph_model.add(dc.nn.GraphPool())
		graph_model.add(dc.nn.GraphConv(int(n_filters), activation='relu'))
		graph_model.add(
		dc.nn.GraphConv(int(n_filters), int(n_filters), activation='relu'))
		graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
		graph_model.add(dc.nn.GraphPool())
		# Gather Projection
		graph_model.add(
		dc.nn.Dense(int(n_fully_connected_nodes), activation='relu'))
		dc.nn.Dense(
		int(n_fully_connected_nodes), int(n_filters), activation='relu'))
		graph_model.add(dc.nn.BatchNormalization(epsilon=1e-5, mode=1))
		graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))
		with tf.Session() as sess:
		model_graphconvreg = dc.models.MultitaskGraphRegressor(
		sess,
		graph_model,
		len(tasks),
		n_features,
		batch_size=batch_size,
		learning_rate=learning_rate,
		optimizer_type="adam",

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