def cifar10_shufflenet(x, n_groups=2, n_filters=200, ratio=1.0, seed=42):
layers = []
variables = []
training = tf.placeholder(tf.bool, name="training")
variables.append(("training", training))
conv = conv2d_bn_relu(x,
size=3,
n_filters=24,
is_training=training,
kernel_init=He_normal(seed=seed + 1),
name="initial_conv")
layers.append(("initial_conv", conv))
slayer1 = shufflenet.shufflenet_layer(conv,
n_filters=n_filters,
n_repeat=3,
n_groups=n_groups,
reduction_ratio=ratio,
is_training=training,
kernel_init=He_normal(seed=seed + 2),
name="shufflenet_layer_1")
layers.append(("shufflenet_layer_1", slayer1))
slayer2 = shufflenet.shufflenet_layer(slayer1,
n_filters=n_filters * 2,
n_repeat=7,
n_groups=n_groups,
reduction_ratio=ratio,
is_training=training,
kernel_init=He_normal(seed=seed + 3),
name="shufflenet_layer_2")
layers.append(("shufflenet_layer_2", slayer2))
slayer3 = shufflenet.shufflenet_layer(slayer2,
n_filters=n_filters * 4,
n_repeat=3,
n_groups=n_groups,
reduction_ratio=ratio,
is_training=training,
kernel_init=He_normal(seed=seed + 4),
name="shufflenet_layer_3")
layers.append(("shufflenet_layer_3", slayer3))
pool = global_avg_pool2d(slayer3)
layers.append(("pool", pool))
dense1 = dense(pool,
n_units=10,
kernel_init=Kumar_normal(activation=None,
mode="FAN_IN",
seed=seed + 5),
name="dense_1")
layers.append(("logit", dense1))
prob = tf.nn.softmax(dense1, name="prob")
layers.append(("prob", prob))
return layers, variables
def auxiliary_classifier_bn(inputs,
pool=avg_pool2d,
pool_size=5,
pool_stride=3,
n_filters_1x1=128,
n_units=1024,
is_training=False,
regularizer_conv=None,
regularizer_dense=None,
kernel_init_conv=He_normal(seed=42),
kernel_init_dense=He_normal(seed=42),
name="inception_auxiliary_classifier_batchnorm"):
with tf.variable_scope(name):
# pool
pool = pool(inputs,
size=pool_size,
stride=pool_stride,
padding="VALID",
name="pool")
# 1x1
x_1x1 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_filters_1x1,
stride=1,
is_training=is_training,
regularizer=regularizer_conv,
kernel_init=kernel_init_conv,
name="conv_1x1")
# dense
flat = flatten(x_1x1, name="flatten")
dense = dense_bn_relu(flat,
n_units=n_units,
is_training=is_training,
regularizer=regularizer_dense,
kernel_init=kernel_init_dense,
name="dense")
return dense
def cifar10_mobilenet_v2(x, expand_ratio=6, seed=42):
layers = []
variables = []
training = tf.placeholder(tf.bool, name="training")
variables.append(("training", training))
conv1 = conv2d_bn_relu(x,
size=3,
n_filters=32,
kernel_init=He_normal(seed=seed + 1),
is_training=training,
name="initial_conv")
layers.append(("initial_conv", conv1))
# 32x32x32 -> 32x32x16
invres1 = mobilenet_v2.inverted_residual_block(
conv1,
n_repeat=1,
n_filters=16,
stride=1,
expand_ratio=1,
kernel_init=He_normal(seed=seed + 2),
is_training=training,
name="inverted_residual_block_1")
layers.append(("inverted_residual_block_1", invres1))
# 32x32x16 -> 32x32x24
invres2 = mobilenet_v2.inverted_residual_block(
invres1,
n_repeat=2,
n_filters=24,
stride=1,
expand_ratio=expand_ratio,
kernel_init=He_normal(seed=seed + 3),
is_training=training,
name="inverted_residual_block_2")
layers.append(("inverted_residual_block_2", invres2))
#32x32x24 -> 16x16x32
invres3 = mobilenet_v2.inverted_residual_block(
invres2,
n_repeat=3,
n_filters=32,
stride=2,
expand_ratio=expand_ratio,
kernel_init=He_normal(seed=seed + 4),
is_training=training,
name="inverted_residual_block_3")
layers.append(("inverted_residual_block_3", invres3))
#16x16x32 -> 8x8x64
invres4 = mobilenet_v2.inverted_residual_block(
invres3,
n_repeat=4,
n_filters=64,
stride=2,
expand_ratio=expand_ratio,
kernel_init=He_normal(seed=seed + 5),
is_training=training,
name="inverted_residual_block_4")
layers.append(("inverted_residual_block_4", invres4))
#8x8x64 -> 8x8x96
invres5 = mobilenet_v2.inverted_residual_block(
invres4,
n_repeat=3,
n_filters=96,
stride=1,
expand_ratio=expand_ratio,
kernel_init=He_normal(seed=seed + 6),
is_training=training,
name="inverted_residual_block_5")
layers.append(("inverted_residual_block_5", invres5))
#8x8x96 -> 4x4x160
invres6 = mobilenet_v2.inverted_residual_block(
invres5,
n_repeat=3,
n_filters=160,
stride=2,
expand_ratio=expand_ratio,
kernel_init=He_normal(seed=seed + 7),
is_training=training,
name="inverted_residual_block_6")
layers.append(("inverted_residual_block_6", invres6))
#4x4x160 -> 4x4x320
invres7 = mobilenet_v2.inverted_residual_block(
invres6,
n_repeat=1,
n_filters=320,
stride=1,
expand_ratio=expand_ratio,
kernel_init=He_normal(seed=seed + 8),
is_training=training,
name="inverted_residual_block_7")
layers.append(("inverted_residual_block_7", invres7))
conv2 = conv2d_bn_relu(invres7,
size=1,
n_filters=1280,
kernel_init=He_normal(seed=seed + 9),
is_training=training,
name="final_conv")
layers.append(("final_conv", conv2))
pool = global_avg_pool2d(conv2)
layers.append(("pool", pool))
dense1 = dense(pool,
n_units=10,
kernel_init=Kumar_normal(activation=None,
mode="FAN_IN",
seed=seed + 10),
name="dense_1")
layers.append(("logit", dense1))
prob = tf.nn.softmax(dense1, name="prob")
layers.append(("prob", prob))
return layers, variables
def reduction_bn_v4_2(inputs,
n_reduce_3x3=192,
n_filters_3x3=192,
n_reduce_7x7=256,
n_filters_1x7=256,
n_filters_7x1=320,
n_filters_7x7_3x3=320,
pool=max_pool2d,
pool_size=3,
padding="VALID",
is_training=False,
regularizer=None,
kernel_init=He_normal(seed=42),
kernel_init_reduce=He_normal(seed=42),
name="inception_v4_reduction_2"):
with tf.variable_scope(name):
# 3x3 stride 2
reduce_3x3 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_3x3,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_3x3")
x_3x3 = conv2d_bn_relu(reduce_3x3,
size=3,
n_filters=n_filters_3x3,
stride=2,
padding=padding,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3")
# 1x7 + 7x1 + 3x3 stride 2
reduce_7x7 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_7x7,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_7x7")
x_1x7 = conv2d_bn_relu(reduce_7x7,
size=[1, 7],
n_filters=n_filters_1x7,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_1x7")
x_7x1 = conv2d_bn_relu(x_1x7,
size=[7, 1],
n_filters=n_filters_7x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_7x1")
x_7x7_3x3 = conv2d_bn_relu(x_7x1,
size=3,
n_filters=n_filters_7x7_3x3,
stride=2,
padding=padding,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_7x7_3x3")
# pool
pool = pool(inputs,
size=pool_size,
stride=2,
padding=padding,
name="pool")
inception = tf.concat([x_3x3, x_7x7_3x3, pool], axis=3)
return inception
def reduction_bn_v4_1(
inputs,
n_filters_3x3_1=384, #n
n_reduce_3x3_2=192, #k
n_filters_3x3_2_1=224, #l
n_filters_3x3_2_2=256, #m
pool=max_pool2d,
pool_size=3,
padding="VALID",
is_training=False,
regularizer=None,
kernel_init=He_normal(seed=42),
kernel_init_reduce=He_normal(seed=42),
name="inception_v4_reduction_1"):
with tf.variable_scope(name):
# 3x3 stride 2
x_3x3_1 = conv2d_bn_relu(inputs,
size=3,
n_filters=n_filters_3x3_1,
stride=2,
padding=padding,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3_1")
# 3x3 stride 1 + 3x3 stride 2
reduce_3x3_2 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_3x3_2,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_3x3_2")
x_3x3_2_1 = conv2d_bn_relu(reduce_3x3_2,
size=3,
n_filters=n_filters_3x3_2_1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3_2_1")
x_3x3_2_2 = conv2d_bn_relu(x_3x3_2_1,
size=3,
n_filters=n_filters_3x3_2_2,
stride=2,
padding=padding,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3_2_2")
# pool
pool = pool(inputs,
size=pool_size,
stride=2,
padding=padding,
name="pool")
inception = tf.concat([x_3x3_1, x_3x3_2_2, pool], axis=3)
return inception
def expanded_filterbank_fact1d_bn(
inputs,
n_filters_1x1=256,
n_reduce_1x3_3x1=384,
n_filters_1x3_3x1=256,
n_reduce_1x3=384,
n_filters_1x3=448,
n_filters_3x1=512,
n_filters_3x3_1x3_3x1=256,
n_filters_pool=256,
pool=avg_pool2d,
pool_size=3,
is_training=False,
regularizer=None,
kernel_init=He_normal(seed=42),
kernel_init_reduce=He_normal(seed=42),
name="inception_expanded_filterbank_batchnorm"):
with tf.variable_scope(name):
# 1x1
x_1x1 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_filters_1x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_1x1")
# 1x3 + 3x1
reduce_1x3_3x1 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_1x3_3x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_1x3_3x1")
x_1x3 = conv2d_bn_relu(reduce_1x3_3x1,
size=[1, 3],
n_filters=n_filters_1x3_3x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_1x3_1")
x_3x1 = conv2d_bn_relu(reduce_1x3_3x1,
size=[3, 1],
n_filters=n_filters_1x3_3x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x1_1")
# 1x3 + 3x1 + (1x3 + 3x1)
reduce_1x3 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_1x3,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_1x3")
x_1x3_2 = conv2d_bn_relu(reduce_1x3,
size=[1, 3],
n_filters=n_filters_1x3,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_1x3_2")
x_3x1_2 = conv2d_bn_relu(x_1x3_2,
size=[3, 1],
n_filters=n_filters_3x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x1_2")
x_3x3_1x3 = conv2d_relu(x_3x1_2,
size=[1, 3],
n_filters=n_filters_3x3_1x3_3x1,
stride=1,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3_1x3")
x_3x3_3x1 = conv2d_bn_relu(x_3x1_2,
size=[3, 1],
n_filters=n_filters_3x3_1x3_3x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3_3x1")
# pool projection
pool = pool(inputs, size=pool_size, stride=1, name="pool")
proj_pool = conv2d_bn_relu(pool,
size=1,
n_filters=n_filters_pool,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_pool_proj")
inception = tf.concat(
[x_1x1, x_1x3, x_3x1, x_3x3_1x3, x_3x3_3x1, proj_pool], axis=3)
return inception
def grid_fact1d_bn(inputs,
n_filters_1x1=384,
n_reduce_7x7_1=192,
n_filters_7x7_1=[(224, 256)],
n_reduce_7x7_2=192,
n_filters_7x7_2=[(192, 224), (224, 256)],
n_filters_pool=128,
pool=max_pool2d,
pool_size=3,
is_training=False,
regularizer=None,
kernel_init=He_normal(seed=42),
kernel_init_reduce=He_normal(seed=42),
name="inception_grid_1d_factorized_batchnorm"):
with tf.variable_scope(name):
# 1x1
x_1x1 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_filters_1x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_1x1")
# 1x7 + 7x1
reduce_7x7_1 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_7x7_1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_7x7_1")
x_7x7_1 = factorized_conv2d_bn_relu(reduce_7x7_1,
size=7,
n_repeat=1,
n_filters=n_filters_7x7_1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_fact_7x7_1")
# 1x7 + 7x1 + 1x7 + 7x1
reduce_7x7_2 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_7x7_2,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_7x7_2")
x_7x7_2 = factorized_conv2d_bn_relu(reduce_7x7_2,
size=7,
n_repeat=2,
n_filters=n_filters_7x7_2,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_fact_7x7_2")
# pool projection
pool = pool(inputs, size=pool_size, stride=1, name="pool")
proj_pool = conv2d_bn_relu(pool,
size=1,
n_filters=n_filters_pool,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_pool_proj")
inception = tf.concat([x_1x1, x_7x7_1, x_7x7_2, proj_pool], axis=3)
return inception
def grid_fact2d_bn(inputs,
n_filters_1x1=64,
n_reduce_3x3=96,
n_filters_3x3=128,
n_reduce_5x5=16,
n_filters_5x5=32,
n_filters_pool=32,
pool=max_pool2d,
pool_size=3,
is_training=False,
regularizer=None,
kernel_init=He_normal(seed=42),
kernel_init_reduce=He_normal(seed=42),
name="inception_grid_2d_factorized_batchnorm"):
with tf.variable_scope(name):
# 1x1
x_1x1 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_filters_1x1,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_1x1")
# 3x3
reduce_3x3 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_3x3,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_3x3")
x_3x3 = conv2d_bn_relu(reduce_3x3,
size=3,
n_filters=n_filters_3x3,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="conv_3x3")
# factorized 5x5
reduce_5x5 = conv2d_bn_relu(inputs,
size=1,
n_filters=n_reduce_5x5,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_reduce_5x5")
fact_5x5_1 = conv2d_bn_relu(reduce_5x5,
size=3,
n_filters=n_filters_5x5,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="fact_conv_5x5_1")
fact_5x5_2 = conv2d_bn_relu(fact_5x5_1,
size=3,
n_filters=n_filters_5x5,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init,
name="fact_conv_5x5_2")
# pool projection
pool = pool(inputs, size=pool_size, stride=1, name="pool")
proj_pool = conv2d_bn_relu(pool,
size=1,
n_filters=n_filters_pool,
stride=1,
is_training=is_training,
regularizer=regularizer,
kernel_init=kernel_init_reduce,
name="conv_pool_proj")
inception = tf.concat([x_1x1, x_3x3, fact_5x5_2, proj_pool], axis=3)
return inception
def cifar10_sequential_cbn5d3_wd(x,
drop_rate=0.5,
weight_decay=0.001,
seed=42):
layers = []
variables = []
training = tf.placeholder(tf.bool, name="training")
variables.append(("training", training))
# 2x conv3x3 selu + pool
conv1 = conv2d_bn_relu(
x,
size=5,
n_filters=64,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 1),
name="conv_1")
layers.append(("conv_1", conv1))
pool1 = max_pool2d(conv1, size=3, stride=2, name="pool_1")
layers.append(("pool_1", pool1))
# 2x conv3x3 selu + pool
conv2 = conv2d_bn_relu(
pool1,
size=5,
n_filters=64,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 2),
name="conv_2")
layers.append(("conv_2", conv2))
pool2 = max_pool2d(conv2, size=3, stride=2, name="pool_2")
layers.append(("pool_2", pool2))
# 2x conv3x3 selu + pool
conv3 = conv2d_bn_relu(
pool2,
size=3,
n_filters=128,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 3),
name="conv_3")
layers.append(("conv_3", conv3))
conv4 = conv2d_bn_relu(
conv3,
size=3,
n_filters=128,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 4),
name="conv_4")
layers.append(("conv_4", conv4))
conv5 = conv2d_bn_relu(
conv4,
size=3,
n_filters=128,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 5),
name="conv_5")
layers.append(("conv_5", conv5))
pool3 = max_pool2d(conv5, size=2, stride=2, name="pool_3")
layers.append(("pool_3", pool3))
flat = flatten(pool3, name="flatten")
layers.append(("flatten", flat))
dense1 = dense_relu(
flat,
n_units=384,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 6),
name="dense_1")
layers.append(("dense_1", dense1))
if drop_rate > 0.0:
dense1 = tf.layers.dropout(dense1,
rate=drop_rate,
training=training,
seed=seed + 6,
name="dropout_1")
dense2 = dense_relu(
dense1,
n_units=192,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 7),
name="dense_2")
layers.append(("dense_2", dense2))
if drop_rate > 0.0:
dense2 = tf.layers.dropout(dense2,
rate=drop_rate,
training=training,
seed=seed + 7,
name="dropout_2")
# dense softmax
dense3 = dense(dense2,
n_units=10,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=Kumar_normal(activation=None,
mode="FAN_IN",
seed=seed + 8),
name="dense_3")
layers.append(("logit", dense3))
prob = tf.nn.softmax(dense3, name="prob")
layers.append(("prob", prob))
return layers, variables
def cifar10_sequential_cbn3d(x,
drop_rate_1=0.2,
drop_rate_2=0.3,
drop_rate_3=0.4,
seed=42):
layers = []
variables = []
training = tf.placeholder(tf.bool, name="training")
variables.append(("training", training))
# conv5x5 batch-norm relu + pool
conv1 = conv2d_bn_relu(x,
size=5,
n_filters=32,
is_training=training,
kernel_init=He_normal(seed=seed + 1),
name="conv_1")
layers.append(("conv_1", conv1))
pool1 = max_pool2d(conv1, size=3, stride=2, name="pool_1")
layers.append(("pool_1", pool1))
dropout1 = tf.layers.dropout(pool1,
rate=drop_rate_1,
training=training,
seed=seed + 1,
name="dropout_1")
layers.append(("dropout_1", dropout1))
# conv5x5 batch-norm relu + pool
conv2 = conv2d_bn_relu(dropout1,
size=5,
n_filters=64,
is_training=training,
kernel_init=He_normal(seed=seed + 2),
name="conv_2")
layers.append(("conv_2", conv2))
pool2 = max_pool2d(conv2, size=3, stride=2, name="pool_2")
layers.append(("pool_2", pool2))
dropout2 = tf.layers.dropout(pool2,
rate=drop_rate_2,
training=training,
seed=seed + 2,
name="dropout_2")
layers.append(("dropout_2", dropout2))
# conv3x3 batch-norm relu + pool
conv3 = conv2d_bn_relu(dropout2,
size=3,
n_filters=128,
is_training=training,
kernel_init=He_normal(seed=seed + 3),
name="conv_3")
layers.append(("conv_3", conv3))
pool3 = max_pool2d(conv3, size=2, stride=2, name="pool_3")
layers.append(("pool_3", pool3))
dropout3 = tf.layers.dropout(pool3,
rate=drop_rate_3,
training=training,
seed=seed + 3,
name="dropout_3")
layers.append(("dropout_3", dropout3))
flat = flatten(dropout3, name="flatten")
layers.append(("flatten", flat))
# dense softmax
dense1 = dense(flat,
n_units=10,
kernel_init=Kumar_normal(activation=None,
mode="FAN_IN",
seed=seed + 4),
name="dense_1")
layers.append(("logit", dense1))
prob = tf.nn.softmax(dense1, name="prob")
layers.append(("prob", prob))
return layers, variables
def cifar10_xception_wd(x, drop_rate=0.5, weight_decay=0.0001, seed=42):
layers = []
variables = []
training = tf.placeholder(tf.bool, name="training")
variables.append(("training", training))
# 32x32
conv = conv2d_bn_relu(
x,
size=3,
n_filters=64,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 1),
name="initial_conv")
layers.append(("initial_conv", conv))
entry = xception.entry_module(
conv,
n_filters=[128, 256, 728],
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 2),
name="entry")
layers.append(("entry", entry))
mid = xception.middle_module(
entry,
n_filters=728,
n_repeat=3,
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 3),
name="middle")
layers.append(("middle", mid))
exits = xception.exit_module(
mid,
n_filters_1=[728, 1024],
n_filters_2=[1536, 2048],
is_training=training,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=He_normal(seed=seed + 4),
name="exit")
layers.append(("exit", exits))
pool1 = global_avg_pool2d(exits, name="pool1")
layers.append(("pool1", pool1))
dropout1 = tf.layers.dropout(pool1,
rate=drop_rate,
training=training,
seed=seed + 5,
name="dropout")
layers.append(("dropout1", dropout1))
dense1 = dense(dropout1,
n_units=10,
regularizer=tf.contrib.layers.l2_regularizer(weight_decay),
kernel_init=Kumar_normal(activation=None,
mode="FAN_IN",
seed=seed + 6),
name="dense1")
layers.append(("logit", dense1))
prob = tf.nn.softmax(dense1, name="prob")
layers.append(("prob", prob))
return layers, variables
def cifar10_mobilenet(x, seed=42):
layers = []
variables = []
training = tf.placeholder(tf.bool, name="training")
variables.append(("training", training))
conv = conv2d_bn_relu(x,
size=3,
n_filters=32,
kernel_init=He_normal(seed=seed + 1),
is_training=training,
name="initial_conv")
layers.append(("initial_conv", conv))
mblock1 = mobilenet.mobilenet_block(conv,
n_filters=64,
stride=1,
kernel_init=He_normal(seed=seed + 2),
is_training=training,
name="mobilenet_block_1")
layers.append(("mobilenet_block_1", mblock1))
# 16x16
mblock2 = mobilenet.mobilenet_block(mblock1,
n_filters=128,
stride=2,
kernel_init=He_normal(seed=seed + 3),
is_training=training,
name="mobilenet_block_2")
layers.append(("mobilenet_block_2", mblock2))
mblock3 = mobilenet.mobilenet_block(mblock2,
n_filters=128,
stride=1,
kernel_init=He_normal(seed=seed + 4),
is_training=training,
name="mobilenet_block_3")
layers.append(("mobilenet_block_3", mblock3))
# 8x8
mblock4 = mobilenet.mobilenet_block(mblock3,
n_filters=256,
stride=2,
kernel_init=He_normal(seed=seed + 5),
is_training=training,
name="mobilenet_block_4")
layers.append(("mobilenet_block_4", mblock4))
mblock5 = mobilenet.mobilenet_block(mblock4,
n_filters=256,
stride=1,
kernel_init=He_normal(seed=seed + 6),
is_training=training,
name="mobilenet_block_5")
layers.append(("mobilenet_block_5", mblock5))
# 4x4
mblock6 = mobilenet.mobilenet_block(mblock5,
n_filters=512,
stride=2,
kernel_init=He_normal(seed=seed + 7),
is_training=training,
name="mobilenet_block_6")
layers.append(("mobilenet_block_6", mblock6))
mblock7 = mobilenet.mobilenet_block(mblock6,
n_filters=512,
stride=1,
kernel_init=He_normal(seed=seed + 8),
is_training=training,
name="mobilenet_block_7")
layers.append(("mobilenet_block_7", mblock7))
mblock8 = mobilenet.mobilenet_block(mblock7,
n_filters=512,
stride=1,
kernel_init=He_normal(seed=seed + 9),
is_training=training,
name="mobilenet_block_8")
layers.append(("mobilenet_block_8", mblock8))
mblock9 = mobilenet.mobilenet_block(mblock8,
n_filters=512,
stride=1,
kernel_init=He_normal(seed=seed + 10),
is_training=training,
name="mobilenet_block_9")
layers.append(("mobilenet_block_9", mblock9))
mblock10 = mobilenet.mobilenet_block(mblock9,
n_filters=512,
stride=1,
kernel_init=He_normal(seed=seed + 11),
is_training=training,
name="mobilenet_block_10")
layers.append(("mobilenet_block_10", mblock10))
mblock11 = mobilenet.mobilenet_block(mblock10,
n_filters=512,
stride=1,
kernel_init=He_normal(seed=seed + 12),
is_training=training,
name="mobilenet_block_11")
layers.append(("mobilenet_block_11", mblock11))
# 2x2
mblock12 = mobilenet.mobilenet_block(mblock11,
n_filters=1024,
stride=2,
kernel_init=He_normal(seed=seed + 13),
is_training=training,
name="mobilenet_block_12")
layers.append(("mobilenet_block_12", mblock12))
mblock13 = mobilenet.mobilenet_block(mblock12,
n_filters=1024,
stride=1,
kernel_init=He_normal(seed=seed + 14),
is_training=training,
name="mobilenet_block_13")
layers.append(("mobilenet_block_13", mblock13))
pool = global_avg_pool2d(mblock13)
layers.append(("pool", pool))
dense1 = dense(pool,
n_units=10,
kernel_init=Kumar_normal(activation=None,
mode="FAN_IN",
seed=seed + 15),
name="dense1")
layers.append(("logit", dense1))
prob = tf.nn.softmax(dense1, name="prob")
layers.append(("prob", prob))
return layers, variables
