2D avg pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable ten

3D avg pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable t

NOTE: this is older version of the util func. it is deprecated. Batch normalization on convolutional maps and beyond... Ref.: http://stackoverflo

1D convolution with non-linear operation. Args: inputs: 3-D tensor variable BxLxC num_output_channels: int kernel_size: int scope:

2D convolution with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a list of 2

2D convolution transpose with non-linear operation. Args: inputs: 4-D tensor variable BxHxWxC num_output_channels: int kernel_size: a

3D convolution with non-linear operation. Args: inputs: 5-D tensor variable BxDxHxWxC num_output_channels: int kernel_size: a list of

Dropout layer. Args: inputs: tensor is_training: boolean tf.Variable scope: string keep_prob: float in [0,1] noise_shape: list

Fully connected layer with non-linear operation. Args: inputs: 2-D tensor BxN num_outputs: int Returns: Variable tensor of size

2D max pooling. Args: inputs: 4-D tensor BxHxWxC kernel_size: a list of 2 ints stride: a list of 2 ints Returns: Variable ten

3D max pooling. Args: inputs: 5-D tensor BxDxHxWxC kernel_size: a list of 3 ints stride: a list of 3 ints Returns: Variable t