PR feedback

    self.capacity = capacity

    super(InputFifoQueue, self).__init__(**kwargs)

  def _create_tensor(self, in_layers=None):

  def create_tensor(self, in_layers=None):

    if in_layers is None:

      in_layers = self.in_layers

    in_layers = convert_to_layers(in_layers)

from __future__ import print_function

from __future__ import division

from __future__ import print_function

from __future__ import unicode_literals

__author__ = "Joseph Gomes"

__copyright__ = "Copyright 2017, Stanford University"

__license__ = "MIT"

import os

import sys

import deepchem as dc

import numpy as np

import tensorflow as tf

sys.path.append("../../models")

from deepchem.models.tensorgraph.layers import Layer, Feature, Label, L2LossLayer, AtomicConvolution, Transpose, Dense

from deepchem.models.tensorgraph.layers import Layer, Feature, Label, AtomicConvolution

from deepchem.models import TensorGraph

import numpy as np

import tensorflow as tf

import itertools

import time

def InitializeWeightsBiases(prev_layer_size,

    tg.fit_generator(

        databag.iterbatches(

            epochs=5000, batch_size=tg.batch_size, pad_batches=True))

            epochs=10, batch_size=tg.batch_size, pad_batches=True))

    prediction = tg.predict_proba_on_generator(databag.iterbatches())

    for i in range(2):

      y_real = ys[i].X

      y_pred = prediction[:, i, :]

      assert_true(np.all(np.isclose(y_pred, y_real, atol=0.2)))

      assert_true(np.all(np.isclose(y_pred, y_real, atol=0.6)))

  def test_single_task_regressor(self):

    n_data_points = 20