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utils.py
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utils.py
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import tensorflow as tf
def weight_variable(shape, stddev=0.02, name=None):
# print(shape)
initial = tf.truncated_normal(shape, stddev=stddev)
if name is None:
return tf.Variable(initial)
else:
# return tf.get_variable(name, initializer=initial)
return tf.Variable(initial, name=name)
def bias_variable(shape, name=None):
initial = tf.constant(0.0, shape=shape)
if name is None:
return tf.Variable(initial)
else:
# return tf.get_variable(name, initializer=initial)
return tf.Variable(initial, name=name)
def conv2d_basic(x, W, bias):
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
return tf.nn.bias_add(conv, bias)
def conv2d_strided(x, W, b):
conv = tf.nn.conv2d(x, W, strides=[1, 2, 2, 1], padding="SAME")
return tf.nn.bias_add(conv, b)
def avg_pool_2x2(x):
return tf.nn.avg_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")