reset qm7_ani

from __future__ import unicode_literals

import numpy as np

from models import TensorGraph

np.random.seed(123)

import tensorflow as tf

tf.set_random_seed(123)

import deepchem as dc

# Load Tox21 dataset

# tasks, datasets, transformers = dc.molnet.load_qm7_from_mat(

#   featurizer='BPSymmetryFunction')

# train_dataset, valid_dataset, test_dataset = datasets

tasks, datasets, transformers = dc.molnet.load_qm7_from_mat(

    featurizer='BPSymmetryFunction')

train_dataset, valid_dataset, test_dataset = datasets

# Batch size of models

max_atoms = 23

layer_structures = [128, 128, 64]

atom_number_cases = [1, 6, 7, 8, 16]

# ANItransformer = dc.trans.ANITransformer(

#     max_atoms=max_atoms, atom_cases=atom_number_cases)

# train_dataset = ANItransformer.transform(train_dataset)

# valid_dataset = ANItransformer.transform(valid_dataset)

# test_dataset = ANItransformer.transform(test_dataset)

# print(test_dataset.data_dir)

# n_feat = ANItransformer.get_num_feats() - 1

ANItransformer = dc.trans.ANITransformer(

    max_atoms=max_atoms, atom_cases=atom_number_cases)

train_dataset = ANItransformer.transform(train_dataset)

valid_dataset = ANItransformer.transform(valid_dataset)

test_dataset = ANItransformer.transform(test_dataset)

n_feat = ANItransformer.get_num_feats() - 1

# Fit models

metric = [

]

model = dc.models.ANIRegression(

  1,

    len(tasks),

    max_atoms,

  1000,

    n_feat,

    layer_structures=layer_structures,

    atom_number_cases=atom_number_cases,

    batch_size=batch_size,

    learning_rate=0.001,

    use_queue=False,

    mode="regression")

model.build()

# Fit trained model

model.fit(train_dataset, nb_epoch=300, checkpoint_interval=100)

model.fit(train_dataset, nb_epoch=3000, checkpoint_interval=100)

print("Evaluating model")

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

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

model.save()

model = TensorGraph.load_from_dir(model.model_dir)

train_scores2 = model.evaluate(train_dataset, metric, transformers)

valid_scores2 = model.evaluate(valid_dataset, metric, transformers)

print("Train scores")

print(train_scores)

print(train_scores2)

print("Validation scores")

print(valid_scores)

print(valid_scores2)