unittest for UNET

import tensorflow as tf

import deepchem as dc

from deepchem.models import Sequential

from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature

from deepchem.models.tensorgraph.layers import Conv2D, MaxPool2D, Conv2DTranspose, Concat, Feature, Label

from deepchem.models.tensorgraph.layers import SoftMaxCrossEntropy, ReduceMean, SoftMax

from deepchem.models import TensorGraph

    self.filters = filters

    input = Feature(shape=(None, self.img_rows, self.img_cols, 3))

    labels = Feature(shape=(None, self.img_rows, self.img_cols))

    labels = Label(shape=(None, self.img_rows * self.img_cols))

    conv1 = Conv2D(

        num_outputs=self.filters[0],

import deepchem as dc

import numpy as np

import tensorflow as tf

import unittest

from deepchem.models.tensorgraph.models import unet

from deepchem.models.tensorgraph import layers

class TestUNet(unittest.TestCase):

  def test_unet(self):

    unet2D = deepchem.models.tensorgraph.models.unet.UNet(

        learning_rate=0.003, img_rows=32, img_cols=32)

    data = np.ones((5, 32, 32, 3))

    labels = np.ones((5, 32 * 32))

    train = deepchem.data.NumpyDataset(data, labels)

    unet2D.fit(train, nb_epochs=0)