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52 lines
1.6 KiB
52 lines
1.6 KiB
import tensorflow as tf
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import yaml
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def init_weights(shape, name):
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return tf.get_variable(name, shape=shape, initializer=tf.contrib.layers.xavier_initializer())
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def init_biases(shape):
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return tf.Variable(tf.zeros(shape))
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def threshold(x, val=0.5):
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x = tf.clip_by_value(x, 0.5, 0.5001) - 0.5
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x = tf.minimum(x * 10000, 1)
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return x
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def lrelu(x, leak=0.2, name=None):
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return tf.maximum(x, leak * x, name=name)
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def make_var(name, shape=None, initializer=None, trainable=True):
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if shape is not None:
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return tf.get_variable(name, shape, initializer=initializer, trainable=trainable)
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else:
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return tf.get_variable(name, initializer=initializer, trainable=trainable)
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def exp_average_summary(ops, dep_ops, decay=0.9, name='avg', scope_pfix='', raw_pfix=' (raw)', avg_pfix=' (avg)'):
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averages = tf.train.ExponentialMovingAverage(decay, name=name)
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averages_op = averages.apply(ops)
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for op in ops:
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tf.summary.scalar(scope_pfix + op.name + raw_pfix, op)
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tf.summary.scalar(scope_pfix + op.name + avg_pfix, averages.average(op))
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with tf.control_dependencies([averages_op]):
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for i, dep_op in enumerate(dep_ops):
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dep_ops[i] = tf.identity(dep_op, name=dep_op.name.split(':')[0])
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return dep_ops
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def load_from_yml(file_path):
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with open(file_path, 'r') as loadfile:
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y = yaml.load(loadfile)
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return y
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def write_to_yml(data_dict, file_path):
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with open(file_path, 'w') as outfile:
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data_dict = dict(data_dict)
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yaml.dump(data_dict, outfile, default_flow_style=False)
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