5e797436c3defd2d863ac1ffab11c48dbd42588e,tests/keras/backend/backend_test.py,TestBackend,test_conv2d,#TestBackend#,804

Before Change

                                   BACKENDS, cntk_dynamicity=True,
                                   data_format="channels_last")

        xval = np.random.random(input_shape)
        kernel_val = np.random.random(kernel_shape) - 0.5
        // Test invalid use cases
        for k in BACKENDS:
            with pytest.raises(ValueError):
                k.conv2d(k.variable(xval), k.variable(kernel_val), data_format="channels_middle")

    def test_depthwise_conv_2d(self):
        // TF kernel shape: (rows, cols, input_depth, depth_multiplier)
        // channels_first input shape: (n, input_depth, rows, cols)
        for (input_shape, kernel_shape, data_format) in [

After Change

    def test_conv2d(self):
        // TF kernel shape: (rows, cols, input_depth, depth)
        // channels_first input shape: (n, input_depth, rows, cols)
        for (input_shape, kernel_shape, data_format) in [
                ((2, 3, 4, 5), (2, 2, 3, 4), "channels_first"),
                ((2, 3, 5, 6), (4, 3, 3, 4), "channels_first"),
                ((1, 6, 5, 3), (3, 3, 3, 2), "channels_last")]:
            check_two_tensor_operation("conv2d", input_shape, kernel_shape,
                                       BACKENDS, cntk_dynamicity=True,
                                       data_format=data_format)

    def test_depthwise_conv_2d(self):
        // TF kernel shape: (rows, cols, input_depth, depth_multiplier)
        // channels_first input shape: (n, input_depth, rows, cols)
        for (input_shape, kernel_shape, data_format) in [

Instances