1b97292ef55358a0207ea3fb04f9d26b8266122d,examples/low_data/tox_graph_conv_one_fold.py,,,#,15

Before Change

for trial_num, (task, support) in enumerate(support_generator):
  print("Starting trial %d" % trial_num)
  // Train model on support
  g = tf.Graph()
  sess = tf.Session(graph=g)

  // Do setup required for tf/keras models
  // Number of features on conv-mols
  n_feat = 75
  // Batch size of models
  batch_size = 50

  with g.as_default():
    graph_model = dc.nn.SequentialGraph(n_feat)
    graph_model.add(dc.nn.GraphConv(64, activation="relu"))
    graph_model.add(dc.nn.GraphPool())
    graph_model.add(dc.nn.GraphConv(128, activation="relu"))
    graph_model.add(dc.nn.GraphPool())
    graph_model.add(dc.nn.GraphConv(64, activation="relu"))
    graph_model.add(dc.nn.GraphPool())
    graph_model.add(dc.nn.Dense(128, activation="tanh"))
    graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))

    with tf.Session() as sess:
      model = dc.models.MultitaskGraphClassifier(
        sess, graph_model, 1, batch_size=batch_size,
        learning_rate=1e-3, learning_rate_decay_time=1000,
        optimizer_type="adam", beta1=.9, beta2=.999)

      // Fit trained model
      model.fit(support, nb_epoch=10)

      // Test model
      task_dataset = dc.data.get_task_dataset_minus_support(
          test_dataset, support, task)
      y_pred = model.predict_proba(task_dataset)
      score = metric.compute_metric(
          task_dataset.y, y_pred, task_dataset.w)
      print("Score on task %s is %s" % (str(task), str(score)))
      task_scores[task].append(score)

// Join information for all tasks.
mean_task_scores = {}
std_task_scores = {}
for task in range(len(test_dataset.get_task_names())):
  mean_task_scores[task] = np.mean(np.array(task_scores[task]))

After Change

import numpy as np
np.random.seed(123)
import tensorflow as tf
tf.set_random_seed(123)
import deepchem as dc
from datasets import load_tox21_convmol

// 4-fold splits

Instances