Source code for deephyper.keras.layers._padding

import tensorflow as tf

[docs]class Padding(tf.keras.layers.Layer): """Multi-dimensions padding layer. This operation pads a tensor according to the paddings you specify. paddings is an integer tensor with shape [n-1, 2], where n is the rank of tensor. For each dimension D of input, paddings[D, 0] indicates how many values to add before the contents of tensor in that dimension, and paddings[D, 1] indicates how many values to add after the contents of tensor in that dimension. The first dimension corresponding to the batch size cannot be padded. Args: padding (list(list(int))): e.g. [[1, 1]] mode (str): 'CONSTANT', 'REFLECT' or 'SYMMETRIC' """ def __init__(self, padding, mode="CONSTANT", constant_values=0, **kwargs): super(Padding, self).__init__(**kwargs) self.padding = [[0, 0]] + padding self.mode = mode self.constant_values = constant_values def call(self, x, mask=None): padding = tf.constant(self.padding) return tf.pad( tensor=x, paddings=padding, mode=self.mode, constant_values=self.constant_values, ) def compute_output_shape(self, input_shape): return tf.TensorShape( [ input_shape[i] + sum(self.padding[i]) if not input_shape[i] is None else None for i in range(len(input_shape)) ] ) def get_config(self): config = { "padding": self.padding[1:], "mode": self.mode, "constant_values": self.constant_values, } base_config = super(Padding, self).get_config() return dict(list(base_config.items()) + list(config.items()))