def test_conv1d_model(self):
    Conv1D = Conv1d
    MaxPooling1D = Pooling
    model_m = Sequential(self.s)
    model_m.add(InputLayer(width=(80 * 3), height=1, n_channels=1))
    model_m.add(Conv1D(100, 10, act='relu'))
    model_m.add(Conv1D(100, 10, act='relu'))
    model_m.add(MaxPooling1D(3))
    model_m.add(Conv1D(160, 10, act='relu'))
    model_m.add(Conv1D(160, 10, act='relu'))
    model_m.add(GlobalAveragePooling1D(dropout=0.5))
    model_m.add(OutputLayer(n=6, act='softmax'))
    self.assertEqual(model_m.layers[3].output_size, (1, 80, 100))
    model_m.print_summary()
    self.assertEqual(model_m.summary.iloc[(1, (- 1))], 240000)
def test_conv1d_model(self):
    Conv1D = Conv1d
    MaxPooling1D = Pooling
    model_m = Sequential(self.s)
    model_m.add(InputLayer(width=(80 * 3), height=1, n_channels=1))
    model_m.add(Conv1D(100, 10, act='relu'))
    model_m.add(Conv1D(100, 10, act='relu'))
    model_m.add(MaxPooling1D(3))
    model_m.add(Conv1D(160, 10, act='relu'))
    model_m.add(Conv1D(160, 10, act='relu'))
    model_m.add(GlobalAveragePooling1D(dropout=0.5))
    model_m.add(OutputLayer(n=6, act='softmax'))
    self.assertEqual(model_m.layers[3].output_size, (1, 80, 100))
    model_m.print_summary()
    self.assertEqual(model_m.summary.iloc[(1, (- 1))], 240000)