def conv2d(input: tf.Tensor,
output_dim: int,
kernel_width: int = 5,
kernel_height: int = 5,
horizontal_stride: int = 2,
vertical_stride: int = 2,
weight_initializer: Optional[Initializer] = None,
bias_initializer: Optional[Initializer] = None,
name: str = "conv2d"):
"""
Apply a 2D-convolution to a tensor.
Parameters
----------
input: tf.Tensor
The tensor to which the convolution should be applied. Must be of shape [batch_size, height, width, channels]
output_dim: int
The number of convolutional filters
kernel_width: int, optional
The width of the convolutional filters (default 5)
kernel_height: int, optional
The height of the convolutional filters (default 5)
horizontal_stride: int, optional
The horizontal stride of the convolutional filters (default 2)
vertical_stride: int, optional
The vertical stride of the convolutional filters (default 2)
weight_initializer: tf.Initializer, optional
A custom initializer for the weight matrices of the filters
bias_initializer: tf.Initializer, optional
A custom initializer for the bias vectors of the filters
name: str, optional
A name for the operation (default "conv2d")
Returns
-------
tf.Tensor
The result of applying a 2D-convolution to the input tensor.
"""
shape = input.get_shape().as_list()
with tf.variable_scope(name):
weights = tf.get_variable(name="weights",
shape=[kernel_height, kernel_width, shape[-1], output_dim],
initializer=weight_initializer)
bias = tf.get_variable(name="bias",
shape=[output_dim],
initializer=bias_initializer)
conv = tf.nn.conv2d(input,
filter=weights,
strides=[1, vertical_stride, horizontal_stride, 1],
padding='SAME')
conv = tf.nn.bias_add(conv, bias)
return conv
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