def model(x, y, is_training):
# %% We'll convert our MNIST vector data to a 4-D tensor:
# N x W x H x C
x_tensor = tf.reshape(x, [-1, 28, 28, 1])
# %% We'll use a new method called batch normalization.
# This process attempts to "reduce internal covariate shift"
# which is a fancy way of saying that it will normalize updates for each
# batch using a smoothed version of the batch mean and variance
# The original paper proposes using this before any nonlinearities
h_1 = lrelu(batch_norm(conv2d(x_tensor, 32, name='conv1'),
is_training,
scope='bn1'),
name='lrelu1')
h_2 = lrelu(batch_norm(conv2d(h_1, 64, name='conv2'),
is_training,
scope='bn2'),
name='lrelu2')
h_3 = lrelu(batch_norm(conv2d(h_2, 64, name='conv3'),
is_training,
scope='bn3'),
name='lrelu3')
h_3_flat = tf.reshape(h_3, [-1, 64 * 4 * 4])
h_4 = linear(h_3_flat, 10)
y_pred = tf.nn.softmax(h_4)
# %% Define loss/eval/training functions
cross_entropy = -tf.reduce_sum(y * tf.log(y_pred))
train_step = tf.train.AdamOptimizer().minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
return [train_step, accuracy]
def residual_network(x, n_outputs, activation=tf.nn.relu):
LayerBlock = namedtuple('LayerBlock', ['num_repeats', 'num_filters', 'bottleneck_size'])
blocks = [
LayerBlock(3, 128, 32),
LayerBlock(3, 256, 64),
LayerBlock(3, 512, 128),
LayerBlock(3, 1024, 256)]
# 如果数据是二维的，则转一下，对标mnist
input_shape = x.get_shape().as_list()
if len(input_shape) == 2:
ndim = int(sqrt(input_shape[1]))
if ndim * ndim != input_shape[1]:
raise ValueError('input_shape should be square')
x = tf.reshape(x, [-1, ndim, ndim, 1])
net = conv2d(x, 64, k_h=7, k_w=7, name='conv1', activation=activation)
net = tf.nn.max_pool(net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
net = conv2d(net, blocks[0].num_filters, k_h=1, k_w=1,stride_h=1, stride_w=1, padding='VALID', name='conv2')
for block_i, block in enumerate(blocks):
for repeat_i in range(block.num_repeats):
name = 'block_%d/repeat_%d' % (block_i, repeat_i)
conv = conv2d(net, block.bottleneck_size, k_h=1, k_w=1, padding='VALID', stride_h=1, stride_w=1, activation=activation, name=name + '/conv_in')
conv = conv2d(conv, block.bottleneck_size, k_h=3, k_w=3, padding='SAME', stride_h=1, stride_w=1, activation=activation, name=name + '/conv_bottleneck')
conv = conv2d(conv, block.num_filters, k_h=1, k_w=1, padding='VALID', stride_h=1, stride_w=1, activation=activation, name=name + '/conv_out')
net = conv + net
try:
next_block = blocks[block_i + 1]
net = conv2d(net, next_block.num_filters, k_h=1, k_w=1, padding='SAME', stride_h=1, stride_w=1, bias=False, name='block_%d/conv_upscale' % block_i)
except IndexError:
pass
net = tf.nn.avg_pool(net, ksize=[1, net.get_shape().as_list()[1], net.get_shape().as_list()[2], 1], strides=[1, 1, 1, 1], padding='VALID')
net = tf.reshape(net, [-1, net.get_shape().as_list()[1] * net.get_shape().as_list()[2] * net.get_shape().as_list()[3]])
net = linear(net, n_outputs, activation=tf.nn.softmax)
return net
def residual_network(x, n_outputs,
activation=tf.nn.relu):
"""Builds a residual network.
Parameters
----------
x : Placeholder
Input to the network
n_outputs : TYPE
Number of outputs of final softmax
activation : Attribute, optional
Nonlinearity to apply after each convolution
Returns
-------
net : Tensor
Description
Raises
------
ValueError
If a 2D Tensor is input, the Tensor must be square or else
the network can't be converted to a 4D Tensor.
"""
# %%
LayerBlock = namedtuple(
'LayerBlock', ['num_repeats', 'num_filters', 'bottleneck_size'])
blocks = [LayerBlock(3, 128, 32),
LayerBlock(3, 256, 64),
LayerBlock(3, 512, 128),
LayerBlock(3, 1024, 256)]
# %%
input_shape = x.get_shape().as_list()
if len(input_shape) == 2:
ndim = int(sqrt(input_shape[1]))
if ndim * ndim != input_shape[1]:
raise ValueError('input_shape should be square')
x = tf.reshape(x, [-1, ndim, ndim, 1])
# %%
# First convolution expands to 64 channels and downsamples
net = conv2d(x, 64, k_h=7, k_w=7,
name='conv1',
activation=activation)
# %%
# Max pool and downsampling
net = tf.nn.max_pool(
net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# %%
# Setup first chain of resnets
net = conv2d(net, blocks[0].num_filters, k_h=1, k_w=1,
stride_h=1, stride_w=1, padding='VALID', name='conv2')
# %%
# Loop through all res blocks
for block_i, block in enumerate(blocks):
for repeat_i in range(block.num_repeats):
name = 'block_%d/repeat_%d' % (block_i, repeat_i)
conv = conv2d(net, block.bottleneck_size, k_h=1, k_w=1,
padding='VALID', stride_h=1, stride_w=1,
activation=activation,
name=name + '/conv_in')
conv = conv2d(conv, block.bottleneck_size, k_h=3, k_w=3,
padding='SAME', stride_h=1, stride_w=1,
activation=activation,
name=name + '/conv_bottleneck')
conv = conv2d(conv, block.num_filters, k_h=1, k_w=1,
padding='VALID', stride_h=1, stride_w=1,
activation=activation,
name=name + '/conv_out')
net = conv + net
try:
# upscale to the next block size
next_block = blocks[block_i + 1]
net = conv2d(net, next_block.num_filters, k_h=1, k_w=1,
padding='SAME', stride_h=1, stride_w=1, bias=False,
name='block_%d/conv_upscale' % block_i)
except IndexError:
pass
# %%
net = tf.nn.avg_pool(net,
ksize=[1, net.get_shape().as_list()[1],
net.get_shape().as_list()[2], 1],
strides=[1, 1, 1, 1], padding='VALID')
net = tf.reshape(
net,
[-1, net.get_shape().as_list()[1] *
net.get_shape().as_list()[2] *
net.get_shape().as_list()[3]])
net = linear(net, n_outputs, activation=tf.nn.softmax)
# %%
return net
def residual_network(x, n_outputs,
activation=tf.nn.relu):
"""Builds a residual network.
Parameters
----------
x : Placeholder Input to the network
n_outputs : TYPE Number of outputs of final softmax
activation : Attribute, optional Nonlinearity to apply after each convolution
Returns
-------
net : Tensor Description
Raises
------
ValueError
If a 2D Tensor is input, the Tensor must be square or else
the network can't be converted to a 4D Tensor.
"""
#
LayerBlock = namedtuple(
'LayerBlock', ['num_repeats', 'num_filters', 'bottleneck_size'])
blocks = [LayerBlock(3, 128, 32),
LayerBlock(3, 256, 64),
LayerBlock(3, 512, 128),
LayerBlock(3, 1024, 256)]
#
input_shape = x.get_shape().as_list()
if len(input_shape) == 2:
ndim = int(sqrt(input_shape[1]))
if ndim * ndim != input_shape[1]:
raise ValueError('input_shape should be square')
x = tf.reshape(x, [-1, ndim, ndim, 1])
# First convolution expands to 64 channels and downsamples
net = conv2d(x, 64, k_h=7, k_w=7, name='conv1', activation=activation)
# Max pool and downsampling
net = tf.nn.max_pool(net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# Setup first chain of resnets
net = conv2d(net, blocks[0].num_filters, k_h=1, k_w=1, stride_h=1, stride_w=1, padding='VALID', name='conv2')
# Loop through all res blocks
for block_i, block in enumerate(blocks):
for repeat_i in range(block.num_repeats):
name = 'block_%d/repeat_%d' % (block_i, repeat_i)
conv = conv2d(net, block.bottleneck_size, k_h=1, k_w=1,
padding='VALID', stride_h=1, stride_w=1,
activation=activation,
name=name + '/conv_in')
conv = conv2d(conv, block.bottleneck_size, k_h=3, k_w=3,
padding='SAME', stride_h=1, stride_w=1,
activation=activation,
name=name + '/conv_bottleneck')
conv = conv2d(conv, block.num_filters, k_h=1, k_w=1,
padding='VALID', stride_h=1, stride_w=1,
activation=activation,
name=name + '/conv_out')
net = conv + net
try:
# upscale to the next block size
next_block = blocks[block_i + 1]
net = conv2d(net, next_block.num_filters, k_h=1, k_w=1,
padding='SAME', stride_h=1, stride_w=1, bias=False,
name='block_%d/conv_upscale' % block_i)
except IndexError:
pass
#
net = tf.nn.avg_pool(net,
ksize=[1, net.get_shape().as_list()[1],
net.get_shape().as_list()[2], 1],
strides=[1, 1, 1, 1], padding='VALID')
net = tf.reshape(
net,
[-1, net.get_shape().as_list()[1] *
net.get_shape().as_list()[2] *
net.get_shape().as_list()[3]])
net = linear(net, n_outputs, activation=tf.nn.softmax)
#
return net
# training/testing.
is_training = tf.placeholder(tf.bool, name='is_training')
# %% We'll convert our MNIST vector data to a 4-D tensor:
# N x W x H x C
x_tensor = tf.reshape(x, [-1, 28, 28, 1])
#ema.apply([batch_mean, batch_var])
# %% We'll use a new method called batch normalization.
# This process attempts to "reduce internal covariate shift"
# which is a fancy way of saying that it will normalize updates for each
# batch using a smoothed version of the batch mean and variance
'''
# The original paper proposes using this before any nonlinearities!!!!!!!!!!!!!!!
'''
# The original paper proposes using this before any nonlinearities!!!!!!!!!!!!!!!
h_1 = lrelu(batch_norm(conv2d(x_tensor, 32, name='conv1'),
is_training,
scope='bn1'),
name='lrelu1')
h_2 = lrelu(batch_norm(conv2d(h_1, 64, name='conv2'), is_training,
scope='bn2'),
name='lrelu2')
h_3 = lrelu(batch_norm(conv2d(h_2, 64, name='conv3'), is_training,
scope='bn3'),
name='lrelu3')
h_3_flat = tf.reshape(h_3, [-1, 64 * 4 * 4])
h_4 = linear(h_3_flat, 10)
y_pred = tf.nn.softmax(h_4)
# %% Define loss/eval/training functions
cross_entropy = -tf.reduce_sum(y * tf.log(y_pred))
# %% We add a new type of placeholder to denote when we are training.
# This will be used to change the way we compute the network during
# training/testing.
is_training = tf.placeholder(tf.bool, name='is_training')
# %% We'll convert our MNIST vector data to a 4-D tensor:
# N x W x H x C
x_tensor = tf.reshape(x, [-1, 28, 28, 1])
# %% We'll use a new method called batch normalization.
# This process attempts to "reduce internal covariate shift"
# which is a fancy way of saying that it will normalize updates for each
# batch using a smoothed version of the batch mean and variance
# The original paper proposes using this before any nonlinearities
h_1 = lrelu(batch_norm(conv2d(x_tensor, 32, name='conv1'),
is_training, scope='bn1'), name='lrelu1')
h_2 = lrelu(batch_norm(conv2d(h_1, 64, name='conv2'),
is_training, scope='bn2'), name='lrelu2')
h_3 = lrelu(batch_norm(conv2d(h_2, 64, name='conv3'),
is_training, scope='bn3'), name='lrelu3')
h_3_flat = tf.reshape(h_3, [-1, 64 * 4 * 4])
h_4 = linear(h_3_flat, 10)
y_pred = tf.nn.softmax(h_4)
# %% Define loss/eval/training functions
cross_entropy = -tf.reduce_sum(y * tf.log(y_pred))
train_step = tf.train.AdamOptimizer().minimize(cross_entropy)
correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))
epochs = 100
batch_size = 100
X = tf.placeholder(tf.float32, [None, 784])
X_img = tf.reshape(X, [-1, 28, 28, 1])
Y = tf.placeholder(tf.float32, [None, 10])
# ResNet 블록 구조(bottleneck 구조)
LayerBlock = namedtuple('LayerBlock',
['num_repeats', 'num_filters', 'bottleneck_size'])
blocks = [
LayerBlock(3, 128, 32),
LayerBlock(3, 256, 64),
LayerBlock(3, 512, 128),
LayerBlock(3, 1024, 256)
]
# 채널수 64의 합성곱 출력을 만들고 다운샘플링
net = conv2d(X_img, 64, k_h=7, k_w=7, name='conv1', activation=tf.nn.relu)
net = tf.nn.max_pool(net, [1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME')
# ResNet 블록구조의 입력 생성
net = conv2d(net,
blocks[0].num_filters,
k_h=1,
k_w=1,
stride_h=1,
stride_w=1,
padding='VALID',
name='conv2')
# ResNet 블록 반복
for block_i, block in enumerate(blocks):
for repeat_i in range(block.num_repeats):
name = 'block_%d/repeat_%d' % (block_i, repeat_i)
conv1 = conv2d(net,
def residual_network(x, n_outputs, activation=tf.nn.relu):
LayerBlock = namedtuple('LayerBlock',
['num_repeats', 'num_filters', 'bottleneck_size'])
blocks = [
LayerBlock(3, 128, 32),
LayerBlock(3, 256, 64),
LayerBlock(3, 512, 128),
LayerBlock(3, 1024, 256)
]
# 如果数据是二维的，则转一下，对标mnist
input_shape = x.get_shape().as_list()
if len(input_shape) == 2:
ndim = int(sqrt(input_shape[1]))
if ndim * ndim != input_shape[1]:
raise ValueError('input_shape should be square')
x = tf.reshape(x, [-1, ndim, ndim, 1])
net = conv2d(x, 64, k_h=7, k_w=7, name='conv1', activation=activation)
net = tf.nn.max_pool(net, [1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME')
net = conv2d(net,
blocks[0].num_filters,
k_h=1,
k_w=1,
stride_h=1,
stride_w=1,
padding='VALID',
name='conv2')
for block_i, block in enumerate(blocks):
for repeat_i in range(block.num_repeats):
name = 'block_%d/repeat_%d' % (block_i, repeat_i)
conv = conv2d(net,
block.bottleneck_size,
k_h=1,
k_w=1,
padding='VALID',
stride_h=1,
stride_w=1,
activation=activation,
name=name + '/conv_in')
conv = conv2d(conv,
block.bottleneck_size,
k_h=3,
k_w=3,
padding='SAME',
stride_h=1,
stride_w=1,
activation=activation,
name=name + '/conv_bottleneck')
conv = conv2d(conv,
block.num_filters,
k_h=1,
k_w=1,
padding='VALID',
stride_h=1,
stride_w=1,
activation=activation,
name=name + '/conv_out')
net = conv + net
try:
next_block = blocks[block_i + 1]
net = conv2d(net,
next_block.num_filters,
k_h=1,
k_w=1,
padding='SAME',
stride_h=1,
stride_w=1,
bias=False,
name='block_%d/conv_upscale' % block_i)
except IndexError:
pass
net = tf.nn.avg_pool(net,
ksize=[
1,
net.get_shape().as_list()[1],
net.get_shape().as_list()[2], 1
],
strides=[1, 1, 1, 1],
padding='VALID')
net = tf.reshape(net, [
-1,
net.get_shape().as_list()[1] * net.get_shape().as_list()[2] *
net.get_shape().as_list()[3]
])
net = linear(net, n_outputs, activation=tf.nn.softmax)
return net
Parag K. Mital, Jan 2016. """ # %% import tensorflow as tf from libs.batch_norm import batch_norm from libs.activations import lrelu from libs.connections import conv2d, linear import cmtf.data.data_mnist as data_mnist mnist = data_mnist.read_data_sets(one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) is_training = tf.placeholder(tf.bool, name='is_training') x_tensor = tf.reshape(x, [-1, 28, 28, 1]) h_1 = lrelu(batch_norm(conv2d(x_tensor, 32, name='conv1'), is_training, scope='bn1'), name='lrelu1') h_2 = lrelu(batch_norm(conv2d(h_1, 64, name='conv2'), is_training, scope='bn2'), name='lrelu2') h_3 = lrelu(batch_norm(conv2d(h_2, 64, name='conv3'), is_training, scope='bn3'), name='lrelu3') h_3_flat = tf.reshape(h_3, [-1, 64 * 4 * 4]) h_4 = linear(h_3_flat, 10) y_pred = tf.nn.softmax(h_4) # %% Define loss/eval/training functions cross_entropy = -tf.reduce_sum(y * tf.log(y_pred)) train_step = tf.train.AdamOptimizer().minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) # %% We now create a new session to actually perform the initialization the # variables:
# %% We'll convert our MNIST vector data to a 4-D tensor:
# N x W x H x C
x_tensor = tf.reshape(x, [-1, 1, 26, 1]) # FOR MFCC-26 dim
#x_tensor = tf.reshape(x, [-1, 1, 40, 1]) # FOR CONVAE
# %% We'll use a new method called batch normalization.
# This process attempts to "reduce internal covariate shift"
# which is a fancy way of saying that it will normalize updates for each
# batch using a smoothed version of the batch mean and variance
# The original paper proposes using this before any nonlinearities
h_1 = lrelu(batch_norm(conv2d(x_tensor,
32,
name='conv1',
stride_h=1,
k_h=1,
k_w=3,
pool_size=[1, 1, 2, 1],
pool_stride=[1, 1, 1, 1]),
phase_train=is_training,
scope='bn1'),
name='lrelu1')
h_2 = lrelu(batch_norm(conv2d(h_1,
64,
name='conv2',
stride_h=1,
k_h=1,
k_w=3,
pool_size=[1, 1, 2, 1],
pool_stride=[1, 1, 1, 1]),
from libs.connections import conv2d, linear from libs.datasets import MNIST # %% Setup input to the network and true output label. These are # simply placeholders which we'll fill in later. mnist = MNIST() x = tf.placeholder(tf.float32, [None, 784]) y = tf.placeholder(tf.float32, [None, 10]) x_tensor = tf.reshape(x, [-1, 28, 28, 1]) # %% Define the network: bn1 = batch_norm(-1, name='bn1') bn2 = batch_norm(-1, name='bn2') bn3 = batch_norm(-1, name='bn3') h_1 = lrelu(bn1(conv2d(x_tensor, 32, name='conv1')), name='lrelu1') h_2 = lrelu(bn2(conv2d(h_1, 64, name='conv2')), name='lrelu2') h_3 = lrelu(bn3(conv2d(h_2, 64, name='conv3')), name='lrelu3') h_3_flat = tf.reshape(h_3, [-1, 64 * 4 * 4]) h_4 = linear(h_3_flat, 10) y_pred = tf.nn.softmax(h_4) # %% Define loss/eval/training functions cross_entropy = -tf.reduce_sum(y * tf.log(y_pred)) train_step = tf.train.AdamOptimizer().minimize(cross_entropy) correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float')) # %% We now create a new session to actually perform the initialization the # variables:
