def weight_variable(name, shape, trainable):
"""
:param name: string
:param shape: 4D array
:return: tf variable
"""
w = tf.get_variable(name=name, shape=shape, initializer=tf.contrib.layers.variance_scaling_initializer(),
trainable=trainable)
weights_norm = tf.reduce_sum(tf.nn.l2_loss(w),
name=name + '_norm') # Should user want to optimize weight decay
tf.add_to_collection('weight_losses', weights_norm)
return w
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