def conv2d(input_, o_dim, k_size, st, name='conv2d'):
with tf.variable_scope(name):
init = ly.xavier_initializer_conv2d()
output = ly.conv2d(input_, num_outputs=o_dim, kernel_size=k_size, stride=st,\
activation_fn=tf.nn.relu, normalizer_fn=ly.batch_norm, padding='SAME',\
weights_initializer=init)
return output
'''
with tf.variable_scope(name):
init = ly.xavier_initializer_conv2d()
fil = tf.get_variable('co_f', k_size+\
[ten_sh(input_)[-1], o_dim],initializer=init)
co = tf.nn.conv2d(input_, fil, strides=[1]+st+[1], \
padding='SAME')
bia = tf.get_variable('co_b', [o_dim])
co = tf.nn.bias_add(co, bia)
return co
'''
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