tensorflow-generative-model-collections

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Hey @hwalsuklee,

Thanks for sharing your code which helps me a lot. I have a question about the linear function in ops.py

I would be appreciated if you can explain what you are doing in the this function.

def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
    shape = input_.get_shape().as_list()

    with tf.variable_scope(scope or "Linear"):
        matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
                 tf.random_normal_initializer(stddev=stddev))
        bias = tf.get_variable("bias", [output_size],
        initializer=tf.constant_initializer(bias_start))
        if with_w:
            return tf.matmul(input_, matrix) + bias, matrix, bias
        else:
            return tf.matmul(input_, matrix) + bias

Also, It would be great if you add some comments when you are implementing; to be much easier for others to understand.

Thanks.

Hi.

As it is mentioned on the top in ops.py, this is borrowed from https://github.com/carpedm20/DCGAN-tensorflow

It is just an implementation of fully connected layer.

Input×weight+bias

Hwalsuk Lee

Thanks, you can close this issue.