def quantize(W, nb = 16, clip_through=False):
non_sign_bits = nb-1
m = pow(2,non_sign_bits)
# W = tf.Print(W,[W, non_sign_bits],message = "---- W Quantize Before---- \n ",summarize=10, first_n = 5)
if clip_through:
Wq = clip_through(round_through(W*m),-m,m-1)/m
else:
Wq = K.clip(round_through(W*m),-m,m-1)/m
# Wq = tf.Print(Wq,[Wq],message = "---- Wq Quantize After---- \n ",summarize=10, first_n = 5)
#Wq = tf.Print(Wq,[Wq],summarize=20)
return Wq
def quantize(W, nb=16, clip_through=False):
'''The weights' binarization function,
# Reference:
- [QuantizedNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. 2016](http://arxiv.org/abs/1602.02830}
'''
non_sign_bits = nb - 1
m = pow(2, non_sign_bits)
# W = tf.Print(W,[W, non_sign_bits],message = "---- W Quantize Before---- \n ",summarize=10, first_n = 5)
if clip_through:
Wq = clip_through(round_through(W * m), -m, m - 1) / m
else:
Wq = K.clip(round_through(W * m), -m, m - 1) / m
# Wq = tf.Print(Wq,[Wq],message = "---- Wq Quantize After---- \n ",summarize=10, first_n = 5)
#Wq = tf.Print(Wq,[Wq],summarize=20)
return Wq
def quantized_tanh(W, nb=16):
non_sign_bits = nb-1
m = pow(2,non_sign_bits)
#W = tf.Print(W,[W],summarize=20)
Wq = K.clip(round_through(W*m),-m,m-1)/m
#Wq = tf.Print(Wq,[Wq],summarize=20)
return Wq
def binary_sigmoid(x):
'''Binary hard sigmoid for training binarized neural network.
# Reference:
- [BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. 2016](http://arxiv.org/abs/1602.02830}
'''
return round_through(_hard_sigmoid(x))
def quantized_tanh(W, nb=16):
'''The weights' binarization function,
# Reference:
- [QuantizedNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. 2016](http://arxiv.org/abs/1602.02830}
'''
non_sign_bits = nb - 1
m = pow(2, non_sign_bits)
#W = tf.Print(W,[W],summarize=20)
Wq = K.clip(round_through(W * m), -m, m - 1) / m
#Wq = tf.Print(Wq,[Wq],summarize=20)
return Wq
def binary_tanh(x):
'''Binary hard sigmoid for training binarized neural network.
The neurons' activations binarization function
It behaves like the sign function during forward propagation
And like:
hard_tanh(x) = 2 * _hard_sigmoid(x) - 1
clear gradient when |x| > 1 during back propagation
# Reference:
- [BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. 2016](http://arxiv.org/abs/1602.02830}
'''
return 2 * round_through(_hard_sigmoid(x)) - 1
def log_quantize(W, nb=16):
'''The weights' binarization function,
# Reference:
- [QuantizedNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. 2016](http://arxiv.org/abs/1602.02830}
'''
non_sign_bits = nb
W = tf.Print(W, [W, nb],
message="---- W Log Quantize normal---- \n ",
summarize=50,
first_n=5)
W_where = tf.where(W > 0, W, tf.zeros_like(W) + 1e-15)
W_where = tf.Print(W_where, [W_where, nb],
message="---- W Log Quantize after where---- \n ",
summarize=50,
first_n=5)
W_log = log2_through(W_where)
W_log = tf.Print(W_log, [W_log, nb],
message="---- W Log Quantize after log--- \n ",
summarize=50,
first_n=5)
W_round = round_through(W_log)
W_round = tf.Print(W_round, [W_round, nb],
message="---- W Log Quantize after round---- \n ",
summarize=50,
first_n=5)
W_clip = clip_through(W_round, 0, nb - 1)
W_clip = tf.Print(W_clip, [W_clip, nb],
message="---- W Log Quantize after clip---- \n ",
summarize=50,
first_n=5)
W_pow = pow_through(W_clip, 2)
W_pow = tf.Print(W_pow, [W_pow, nb],
message="---- W Log Quantize after pow---- \n ",
summarize=50,
first_n=5)
W_fix = tf.where(W_round < 0, tf.zeros_like(W_pow), W_pow)
W_fix = tf.Print(W_fix, [W_fix, nb],
message="---- W Log Quantize after fix---- \n ",
summarize=50,
first_n=5)
return W_fix
def log_quantize(W, nb = 16):
non_sign_bits = nb
W = tf.Print(W,[W, nb],message = "---- W Log Quantize normal---- \n ",summarize=50, first_n = 5)
W_where = tf.where(W > 0, W, tf.zeros_like(W) +1e-15)
W_where = tf.Print(W_where,[W_where, nb],message = "---- W Log Quantize after where---- \n ",summarize=50, first_n = 5)
W_log = log2_through(W_where)
W_log = tf.Print(W_log,[W_log, nb],message = "---- W Log Quantize after log--- \n ",summarize=50, first_n = 5)
W_round = round_through(W_log)
W_round = tf.Print(W_round,[W_round, nb],message = "---- W Log Quantize after round---- \n ",summarize=50, first_n = 5)
W_clip = clip_through(W_round,0, nb-1)
W_clip = tf.Print(W_clip,[W_clip, nb],message = "---- W Log Quantize after clip---- \n ",summarize=50, first_n = 5)
W_pow = pow_through(W_clip, 2)
W_pow = tf.Print(W_pow,[W_pow, nb],message = "---- W Log Quantize after pow---- \n ",summarize=50, first_n = 5)
W_fix = tf.where(W_round<0, tf.zeros_like(W_pow), W_pow)
W_fix = tf.Print(W_fix,[W_fix, nb],message = "---- W Log Quantize after fix---- \n ",summarize=50, first_n = 5)
return W_fix
def quantized_relu(W, nb=16):
'''The weights' binarization function,
# Reference:
- [QuantizedNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1, Courbariaux et al. 2016](http://arxiv.org/abs/1602.02830}
'''
#non_sign_bits = nb-1
#m = pow(2,non_sign_bits)
#Wq = K.clip(round_through(W*m),0,m-1)/m
# W = tf.Print(W,[W, nb],message = "---- W Quantize Relu Before---- \n ",summarize=5, first_n = 5)
nb_bits = nb
Wq = K.clip(
2. *
(round_through(_hard_sigmoid(W) * pow(2, nb_bits)) / pow(2, nb_bits)) -
1., 0, 1 - 1.0 / pow(2, nb_bits - 1))
# Wq = tf.Print(Wq,[Wq],message = "---- W Quantize Relu After---- \n ",summarize=5, first_n = 5)
return Wq
def binary_sigmoid(x):
return round_through(_hard_sigmoid(x))
def binary_tanh(x):
return 2 * round_through(_hard_sigmoid(x)) - 1
def quantized_relu(W, nb=16):
nb_bits = nb
Wq = K.clip(2. * (round_through(_hard_sigmoid(W) * pow(2, nb_bits)) / pow(2, nb_bits)) - 1., 0,
1 - 1.0 / pow(2, nb_bits - 1))
# Wq = tf.Print(Wq,[Wq],message = "---- W Quantize Relu After---- \n ",summarize=5, first_n = 5)
return Wq
