Add batch normalization for 5d

[Riku-KANO]

Purpose

For introducing 3D ML models into javascript (#8011).

Implementation detail

Added batchNorm5d module

I implemented following sample code of 3d model in keras. After converting this to JS model and importing it from some files, console outputs need for batch normalizain of rank 5. Thus, I added batchnorm5d.ts following the implementation of other batch normalization code like batchnorm4d.ts.

# keras
class MyNet:
    def __init__(self, height, width, depth, args):
        self.height = height
        self.width = width
        self.depth = depth
        self.args = args
        x_image = create_image()
        h_conv1 = Activation('relu')(BatchNormalization(axis=3)(Conv3D(args.num_channels, 3, padding='same')(x_image)))
        h_conv2 = Activation('relu')(BatchNormalization(axis=3)(Conv3D(args.num_channels, 3, padding='same')(h_conv1)))
        h_conv3 = Activation('relu')(BatchNormalization(axis=3)(Conv3D(args.num_channels, 3, padding='same')(h_conv2)))
        h_conv4 = Activation('relu')(BatchNormalization(axis=3)(Conv3D(args.num_channels, 3, padding='valid')(h_conv3)))
        h_conv4_flat = Flatten()(h_conv4)
        ...
        ...

Updated related codes

In batchnorm.ts, the input tensor reshaped into buffered tensor of rank 4(x4D), exceeding rank 5. I modified the code to be able to handle 5-d batch normalization.

Added test code of batchNorm5d.

Also added test code in batchnorm_test.ts. This code also heavily follows other test code implementation like batchnorm4d.

Note

I created this code with 4.x_dev branch and merged it to local master branch. There must be no conflict.

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