Added NCI progressive MTNN example

  # Do setup required for tf/keras models

  # Number of features on conv-mols

  n_feat = 75

  n_feat = 71

  # Batch size of models

  batch_size = 128

  graph_model = dc.nn.SequentialGraph(n_feat)

"""

Script that trains progressive multitask models on NCI dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

import deepchem as dc

from nci_datasets import load_nci

# Only for debug!

np.random.seed(123)

# Load Delaney dataset

nci_tasks, nci_datasets, transformers = load_nci()

train_dataset, valid_dataset, test_dataset = nci_datasets

# Fit models

metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

n_features = 1024

n_layers = 1

nb_epoch = 10

model = dc.models.ProgressiveMultitaskRegressor(

  len(nci_tasks), n_features,

  layer_sizes=[1000]*n_layers, dropouts=[.25]*n_layers,

  alpha_init_stddevs=[.02]*n_layers, weight_init_stddevs=[.02]*n_layers,

  bias_init_consts=[1.]*n_layers, learning_rate=.001,

  penalty=.0001, penalty_type="l2", optimizer="adam", batch_size=100,

  seed=123, verbosity="high")

# Fit trained model

model.fit(train_dataset)

print("Evaluating model")

train_scores = model.evaluate(train_dataset, [metric], transformers)

valid_scores = model.evaluate(valid_dataset, [metric], transformers)

print("Train scores")

print(train_scores)

print("Validation scores")

print(valid_scores)