def __init__(self, rng):
Network.__init__(self,
n_hidden_layer=n_hidden_layer,
BN_LSTM=BN_LSTM)
print("LSTM layer:")
self.layer.append(
LSTM(rng=rng,
n_inputs=n_inputs,
n_units=n_units,
initial_gamma=initial_gamma,
initial_beta=initial_beta,
length=length - 1,
batch_size=batch_size,
BN=BN_LSTM,
BN_epsilon=BN_epsilon,
dropout=dropout_input,
binary_training=binary_training,
ternary_training=ternary_training,
stochastic_training=stochastic_training))
print("Softmax layer:")
self.layer.append(
linear_layer(rng=rng,
n_inputs=n_units,
n_units=n_classes,
dropout=dropout_input))
def __init__(self, rng):
Network.__init__(self, n_hidden_layer=n_hidden_layer, BN=BN)
print " Fully connected layer 1:"
self.layer.append(
ReLU_layer(rng=rng,
n_inputs=n_inputs,
n_units=n_units,
BN=BN,
BN_epsilon=BN_epsilon,
dropout=dropout_input,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
for k in range(n_hidden_layer - 1):
print " Fully connected layer " + str(k) + ":"
self.layer.append(
ReLU_layer(rng=rng,
n_inputs=n_units,
n_units=n_units,
BN=BN,
BN_epsilon=BN_epsilon,
dropout=dropout_hidden,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " L2 SVM layer:"
self.layer.append(
linear_layer(rng=rng,
n_inputs=n_units,
n_units=n_classes,
BN=BN,
BN_epsilon=BN_epsilon,
dropout=dropout_hidden,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
def __init__(self, rng):
Network.__init__(self, n_hidden_layer = n_hidden_layer, BN = BN)
print " Fully connected layer 1:"
self.layer.append(ReLU_layer(rng = rng, n_inputs = n_inputs, n_units = n_units,
BN = BN, BN_epsilon=BN_epsilon, dropout=dropout_input,
binary_training=binary_training, stochastic_training=stochastic_training,
binary_test=binary_test, stochastic_test=stochastic_test))
for k in range(n_hidden_layer-1):
print " Fully connected layer "+ str(k) +":"
self.layer.append(ReLU_layer(rng = rng, n_inputs = n_units, n_units = n_units,
BN = BN, BN_epsilon=BN_epsilon, dropout=dropout_hidden,
binary_training=binary_training, stochastic_training=stochastic_training,
binary_test=binary_test, stochastic_test=stochastic_test))
print " L2 SVM layer:"
self.layer.append(linear_layer(rng = rng, n_inputs = n_units, n_units = n_classes,
BN = BN, BN_epsilon=BN_epsilon, dropout=dropout_hidden,
binary_training=binary_training, stochastic_training=stochastic_training,
binary_test=binary_test, stochastic_test=stochastic_test))
def __init__(self, rng):
Network.__init__(self, n_hidden_layer=8, BN=BN)
print " C3 layer:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(128, 3, 3, 3),
pool_shape=(1, 1),
pool_stride=(1, 1),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " C3 P2 layers:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(128, 128, 3, 3),
pool_shape=(2, 2),
pool_stride=(2, 2),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " C2 layer:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(256, 128, 2, 2),
pool_shape=(1, 1),
pool_stride=(1, 1),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " C2 P2 layers:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(256, 256, 2, 2),
pool_shape=(2, 2),
pool_stride=(2, 2),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " C2 layer:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(512, 256, 2, 2),
pool_shape=(1, 1),
pool_stride=(1, 1),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " C2 P2 layers:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(512, 512, 2, 2),
pool_shape=(2, 2),
pool_stride=(2, 2),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " C2 layer:"
self.layer.append(
ReLU_conv_layer(rng,
filter_shape=(1024, 512, 2, 2),
pool_shape=(1, 1),
pool_stride=(1, 1),
BN=BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " FC layer:"
self.layer.append(
ReLU_layer(rng=rng,
n_inputs=1024,
n_units=1024,
BN=BN,
BN_epsilon=BN_epsilon,
dropout=dropout_hidden,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
print " L2 SVM layer:"
self.layer.append(
linear_layer(rng=rng,
n_inputs=1024,
n_units=10,
BN=BN,
BN_epsilon=BN_epsilon,
dropout=dropout_hidden,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test))
def __init__(self, rng):
Network.__init__(self, n_hidden_layer = 8, BN = BN)
print " C3 layer:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(128, 3, 3, 3),
pool_shape=(1,1),
pool_stride=(1,1),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " C3 P2 layers:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(128, 128, 3, 3),
pool_shape=(2,2),
pool_stride=(2,2),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " C2 layer:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(256, 128, 2, 2),
pool_shape=(1,1),
pool_stride=(1,1),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " C2 P2 layers:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(256, 256, 2, 2),
pool_shape=(2,2),
pool_stride=(2,2),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " C2 layer:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(512, 256, 2, 2),
pool_shape=(1,1),
pool_stride=(1,1),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " C2 P2 layers:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(512, 512, 2, 2),
pool_shape=(2,2),
pool_stride=(2,2),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " C2 layer:"
self.layer.append(ReLU_conv_layer(
rng,
filter_shape=(1024, 512, 2, 2),
pool_shape=(1,1),
pool_stride=(1,1),
BN = BN,
BN_epsilon=BN_epsilon,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " FC layer:"
self.layer.append(ReLU_layer(
rng = rng,
n_inputs = 1024,
n_units = 1024,
BN = BN,
BN_epsilon=BN_epsilon,
dropout=dropout_hidden,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))
print " L2 SVM layer:"
self.layer.append(linear_layer(
rng = rng,
n_inputs= 1024,
n_units = 10,
BN = BN,
BN_epsilon=BN_epsilon,
dropout = dropout_hidden,
binary_training=binary_training,
stochastic_training=stochastic_training,
binary_test=binary_test,
stochastic_test=stochastic_test
))