# Copyright 2019 Katsuya Shimabukuro. All rights reserved.
# Licensed under the MIT License.
from nose.tools import ok_, eq_
from model.fcnn import FCNNClassifier
from tests.utils.dummy_dataset import DummyDataset


class TestFCNN(object):
    def test_init(self):
        fcnn = FCNNClassifier(
                    dataset=DummyDataset(),
                    hidden_nums=16,
                    dropout_rate=0.8)
        eq_(fcnn.dense1.kernel.shape[1], 16)
        eq_(fcnn.dropout.rate, 0.8)

    def test_train(self):
        fcnn = FCNNClassifier(
                    dataset=DummyDataset(batch_size=2),
                    hidden_nums=16,
                    dropout_rate=0.8)
        history = fcnn.train()

        ok_('loss' in history)

6293cab0c964ee7eae8efe87688e0a64d91429bd
<mask token>


class TestFCNN(object):
    <mask token>
    <mask token>

<mask token>


class TestFCNN(object):
    <mask token>

    def test_train(self):
        fcnn = FCNNClassifier(dataset=DummyDataset(batch_size=2),
            hidden_nums=16, dropout_rate=0.8)
        history = fcnn.train()
        ok_('loss' in history)

<mask token>


class TestFCNN(object):

    def test_init(self):
        fcnn = FCNNClassifier(dataset=DummyDataset(), hidden_nums=16,
            dropout_rate=0.8)
        eq_(fcnn.dense1.kernel.shape[1], 16)
        eq_(fcnn.dropout.rate, 0.8)

    def test_train(self):
        fcnn = FCNNClassifier(dataset=DummyDataset(batch_size=2),
            hidden_nums=16, dropout_rate=0.8)
        history = fcnn.train()
        ok_('loss' in history)

from nose.tools import ok_, eq_
from model.fcnn import FCNNClassifier
from tests.utils.dummy_dataset import DummyDataset


class TestFCNN(object):

    def test_init(self):
        fcnn = FCNNClassifier(dataset=DummyDataset(), hidden_nums=16,
            dropout_rate=0.8)
        eq_(fcnn.dense1.kernel.shape[1], 16)
        eq_(fcnn.dropout.rate, 0.8)

    def test_train(self):
        fcnn = FCNNClassifier(dataset=DummyDataset(batch_size=2),
            hidden_nums=16, dropout_rate=0.8)
        history = fcnn.train()
        ok_('loss' in history)

# Copyright 2019 Katsuya Shimabukuro. All rights reserved.
# Licensed under the MIT License.
from nose.tools import ok_, eq_
from model.fcnn import FCNNClassifier
from tests.utils.dummy_dataset import DummyDataset


class TestFCNN(object):
    def test_init(self):
        fcnn = FCNNClassifier(
                    dataset=DummyDataset(),
                    hidden_nums=16,
                    dropout_rate=0.8)
        eq_(fcnn.dense1.kernel.shape[1], 16)
        eq_(fcnn.dropout.rate, 0.8)

    def test_train(self):
        fcnn = FCNNClassifier(
                    dataset=DummyDataset(batch_size=2),
                    hidden_nums=16,
                    dropout_rate=0.8)
        history = fcnn.train()

        ok_('loss' in history)
