def get_video_weights(video_id_batch):
video_id_to_index = tf.contrib.lookup.string_to_index_table_from_file(
vocabulary_file=FLAGS.sample_vocab_file, default_value=0)
indexes = video_id_to_index.lookup(video_id_batch)
weights, length = get_video_weights_array()
weights_input = tf.placeholder(tf.float32, shape=[length], name="sample_weights_input")
weights_tensor = tf.get_variable("sample_weights",
shape=[length],
trainable=False,
dtype=tf.float32,
initializer=tf.constant_initializer(weights))
weights_assignment = tf.assign(weights_tensor, weights_input)
tf.add_to_collection("weights_input", weights_input)
tf.add_to_collection("weights_assignment", weights_assignment)
video_weight_batch = tf.nn.embedding_lookup(weights_tensor, indexes)
return video_weight_batch
python类constant_initializer()的实例源码
def get_video_weights(video_id_batch):
video_id_to_index = tf.contrib.lookup.string_to_index_table_from_file(
vocabulary_file=FLAGS.sample_vocab_file, default_value=0)
indexes = video_id_to_index.lookup(video_id_batch)
weights, length = get_video_weights_array()
weights_input = tf.placeholder(tf.float32, shape=[length], name="sample_weights_input")
weights_tensor = tf.get_variable("sample_weights",
shape=[length],
trainable=False,
dtype=tf.float32,
initializer=tf.constant_initializer(weights))
weights_assignment = tf.assign(weights_tensor, weights_input)
tf.add_to_collection("weights_input", weights_input)
tf.add_to_collection("weights_assignment", weights_assignment)
video_weight_batch = tf.nn.embedding_lookup(weights_tensor, indexes)
return video_weight_batch
def get_video_weights(video_id_batch):
video_id_to_index = tf.contrib.lookup.string_to_index_table_from_file(
vocabulary_file=FLAGS.sample_vocab_file, default_value=0)
indexes = video_id_to_index.lookup(video_id_batch)
weights, length = get_video_weights_array()
weights_input = tf.placeholder(tf.float32, shape=[length], name="sample_weights_input")
weights_tensor = tf.get_variable("sample_weights",
shape=[length],
trainable=False,
dtype=tf.float32,
initializer=tf.constant_initializer(weights))
weights_assignment = tf.assign(weights_tensor, weights_input)
tf.add_to_collection("weights_input", weights_input)
tf.add_to_collection("weights_assignment", weights_assignment)
video_weight_batch = tf.nn.embedding_lookup(weights_tensor, indexes)
return video_weight_batch
def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None):
with tf.variable_scope(name):
stride_shape = [1, stride[0], stride[1], 1]
filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters]
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[:3])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = np.prod(filter_shape[:2]) * num_filters
# initialize weights with random weights
w_bound = np.sqrt(6. / (fan_in + fan_out))
w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound),
collections=collections)
b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.constant_initializer(0.0),
collections=collections)
return tf.nn.conv2d(x, w, stride_shape, pad) + b
def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None):
with tf.variable_scope(name):
stride_shape = [1, stride[0], stride[1], 1]
filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters]
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = np.prod(filter_shape[:3])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = np.prod(filter_shape[:2]) * num_filters
# initialize weights with random weights
w_bound = np.sqrt(6. / (fan_in + fan_out))
w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound),
collections=collections)
b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.constant_initializer(0.0),
collections=collections)
return tf.nn.conv2d(x, w, stride_shape, pad) + b
def batchnorm(x, name, phase, updates, gamma=0.96):
k = x.get_shape()[1]
runningmean = tf.get_variable(name+"/mean", shape=[1, k], initializer=tf.constant_initializer(0.0), trainable=False)
runningvar = tf.get_variable(name+"/var", shape=[1, k], initializer=tf.constant_initializer(1e-4), trainable=False)
testy = (x - runningmean) / tf.sqrt(runningvar)
mean_ = mean(x, axis=0, keepdims=True)
var_ = mean(tf.square(x), axis=0, keepdims=True)
std = tf.sqrt(var_)
trainy = (x - mean_) / std
updates.extend([
tf.assign(runningmean, runningmean * gamma + mean_ * (1 - gamma)),
tf.assign(runningvar, runningvar * gamma + var_ * (1 - gamma))
])
y = switch(phase, trainy, testy)
out = y * tf.get_variable(name+"/scaling", shape=[1, k], initializer=tf.constant_initializer(1.0), trainable=True)\
+ tf.get_variable(name+"/translation", shape=[1,k], initializer=tf.constant_initializer(0.0), trainable=True)
return out
# ================================================================
# Mathematical utils
# ================================================================
def test_without_residuals(self):
inputs = tf.constant(np.random.randn(1, 2))
state = (tf.constant(np.random.randn(1, 2)),
tf.constant(np.random.randn(1, 2)))
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
standard_cell = tf.contrib.rnn.MultiRNNCell(
[tf.contrib.rnn.GRUCell(2) for _ in range(2)], state_is_tuple=True)
res_standard = standard_cell(inputs, state, scope="standard")
test_cell = rnn_cell.ExtendedMultiRNNCell(
[tf.contrib.rnn.GRUCell(2) for _ in range(2)])
res_test = test_cell(inputs, state, scope="test")
with self.test_session() as sess:
sess.run([tf.global_variables_initializer()])
res_standard_, res_test_, = sess.run([res_standard, res_test])
# Make sure it produces the same results as the standard cell
self.assertAllClose(res_standard_[0], res_test_[0])
self.assertAllClose(res_standard_[1][0], res_test_[1][0])
self.assertAllClose(res_standard_[1][1], res_test_[1][1])
def _test_with_residuals(self, inputs, **kwargs):
"""Runs the cell in a session"""
inputs = tf.convert_to_tensor(inputs)
state = (tf.constant(np.random.randn(1, 2)),
tf.constant(np.random.randn(1, 2)))
with tf.variable_scope("root", initializer=tf.constant_initializer(0.5)):
test_cell = rnn_cell.ExtendedMultiRNNCell(
[tf.contrib.rnn.GRUCell(2) for _ in range(2)],
residual_connections=True,
**kwargs)
res_test = test_cell(inputs, state, scope="test")
with self.test_session() as sess:
sess.run([tf.global_variables_initializer()])
return sess.run(res_test)
def conv3d(input_, output_dim, f_size, is_training, scope='conv3d'):
with tf.variable_scope(scope) as scope:
# VGG network uses two 3*3 conv layers to effectively increase receptive field
w1 = tf.get_variable('w1', [f_size, f_size, f_size, input_.get_shape()[-1], output_dim],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv1 = tf.nn.conv3d(input_, w1, strides=[1, 1, 1, 1, 1], padding='SAME')
b1 = tf.get_variable('b1', [output_dim], initializer=tf.constant_initializer(0.0))
conv1 = tf.nn.bias_add(conv1, b1)
bn1 = tf.contrib.layers.batch_norm(conv1, is_training=is_training, scope='bn1',
variables_collections=['bn_collections'])
r1 = tf.nn.relu(bn1)
w2 = tf.get_variable('w2', [f_size, f_size, f_size, output_dim, output_dim],
initializer=tf.truncated_normal_initializer(stddev=0.1))
conv2 = tf.nn.conv3d(r1, w2, strides=[1, 1, 1, 1, 1], padding='SAME')
b2 = tf.get_variable('b2', [output_dim], initializer=tf.constant_initializer(0.0))
conv2 = tf.nn.bias_add(conv2, b2)
bn2 = tf.contrib.layers.batch_norm(conv2, is_training=is_training, scope='bn2',
variables_collections=['bn_collections'])
r2 = tf.nn.relu(bn2)
return r2
def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
tf_output_shape=tf.stack(output_shape)
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=tf_output_shape,
strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
#deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), tf_output_shape)
if with_w:
return deconv, w, biases
else:
return deconv
def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
shape = input_.get_shape().as_list()
#mat_shape=tf.stack([tf.shape(input_)[1],output_size])
mat_shape=[shape[1],output_size]
with tf.variable_scope(scope or "Linear"):
#matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
matrix = tf.get_variable("Matrix", mat_shape, tf.float32,
tf.random_normal_initializer(stddev=stddev))
bias = tf.get_variable("bias", [output_size],
initializer=tf.constant_initializer(bias_start))
if with_w:
return tf.matmul(input_, matrix) + bias, matrix, bias
else:
return tf.matmul(input_, matrix) + bias
#minibatch method that improves on openai
#because it doesn't fix batchsize:
#TODO: recheck when not sleepy
def _get_weight_variable(self, layer_name, name, shape, L2=1):
wname = '%s/%s:0'%(layer_name,name)
fanin, fanout = shape[-2:]
for dim in shape[:-2]:
fanin *= float(dim)
fanout *= float(dim)
sigma = self._xavi_norm(fanin, fanout)
if self.weights is None or wname not in self.weights:
w1 = tf.get_variable(name,initializer=tf.truncated_normal(shape = shape,
mean=0,stddev = sigma))
print('{:>23} {:>23}'.format(wname, 'randomly initialize'))
else:
w1 = tf.get_variable(name, shape = shape,
initializer=tf.constant_initializer(value=self.weights[wname],dtype=tf.float32))
self.loaded_weights[wname]=1
if wname != w1.name:
print(wname,w1.name)
assert False
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, tf.nn.l2_loss(w1)*L2)
return w1
def __call__(self, x, train=True):
shape = x.get_shape().as_list()
if train:
with tf.variable_scope(self.name) as scope:
self.beta = tf.get_variable("beta", [shape[-1]],
initializer=tf.constant_initializer(0.))
self.gamma = tf.get_variable("gamma", [shape[-1]],
initializer=tf.random_normal_initializer(1., 0.02))
batch_mean, batch_var = tf.nn.moments(x, [0, 1, 2], name='moments')
ema_apply_op = self.ema.apply([batch_mean, batch_var])
self.ema_mean, self.ema_var = self.ema.average(batch_mean), self.ema.average(batch_var)
with tf.control_dependencies([ema_apply_op]):
mean, var = tf.identity(batch_mean), tf.identity(batch_var)
else:
mean, var = self.ema_mean, self.ema_var
normed = tf.nn.batch_norm_with_global_normalization(
x, mean, var, self.beta, self.gamma, self.epsilon, scale_after_normalization=True)
return normed
# standard convolution layer
def Minibatch_Discriminator(input, num_kernels=100, dim_per_kernel=5, init=False, name='MD'):
num_inputs=df_dim*4
theta = tf.get_variable(name+"/theta",[num_inputs, num_kernels, dim_per_kernel], initializer=tf.random_normal_initializer(stddev=0.05))
log_weight_scale = tf.get_variable(name+"/lws",[num_kernels, dim_per_kernel], initializer=tf.constant_initializer(0.0))
W = tf.mul(theta, tf.expand_dims(tf.exp(log_weight_scale)/tf.sqrt(tf.reduce_sum(tf.square(theta),0)),0))
W = tf.reshape(W,[-1,num_kernels*dim_per_kernel])
x = input
x=tf.reshape(x, [batchsize,num_inputs])
activation = tf.matmul(x, W)
activation = tf.reshape(activation,[-1,num_kernels,dim_per_kernel])
abs_dif = tf.mul(tf.reduce_sum(tf.abs(tf.sub(tf.expand_dims(activation,3),tf.expand_dims(tf.transpose(activation,[1,2,0]),0))),2),
1-tf.expand_dims(tf.constant(np.eye(batchsize),dtype=np.float32),1))
f = tf.reduce_sum(tf.exp(-abs_dif),2)/tf.reduce_sum(tf.exp(-abs_dif))
print(f.get_shape())
print(input.get_shape())
return tf.concat(1,[x, f])
def deconv2d(input_, output_shape, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
#w = tf.get_variable('w', [k_h, k_h, output_shape[-1], input_.get_shape()[-1]], initializer=tf.random_normal_initializer(stddev=stddev))
w = tf.get_variable('w', [k_h, k_h, output_shape[-1], input_.get_shape()[-1]], initializer = tf.contrib.layers.xavier_initializer())
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
def make_moving_average(name, value, init, decay, log=True):
"""Creates an exp-moving average of `value` and an update op, which is added to UPDATE_OPS collection.
:param name: string, name of the created moving average tf.Variable
:param value: tf.Tensor, the value to be averaged
:param init: float, an initial value for the moving average
:param decay: float between 0 and 1, exponential decay of the moving average
:param log: bool, add a summary op if True
:return: tf.Tensor, the moving average
"""
var = tf.get_variable(name, shape=value.get_shape(),
initializer=tf.constant_initializer(init), trainable=False)
update = moving_averages.assign_moving_average(var, value, decay, zero_debias=False)
tf.add_to_collection(tf.GraphKeys.UPDATE_OPS, update)
if log:
tf.summary.scalar(name, var)
return var
def __call__(self, input_layer, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d"):
output_shape[0] = input_layer.shape[0]
ts_output_shape = tf.pack(output_shape)
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = self.variable('w', [k_h, k_w, output_shape[-1], input_layer.shape[-1]],
init=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_layer, w,
output_shape=ts_output_shape,
strides=[1, d_h, d_w, 1])
# Support for versions of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_layer, w, output_shape=ts_output_shape,
strides=[1, d_h, d_w, 1])
# biases = self.variable('biases', [output_shape[-1]], init=tf.constant_initializer(0.0))
# deconv = tf.reshape(tf.nn.bias_add(deconv, biases), [-1] + output_shape[1:])
deconv = tf.reshape(deconv, [-1] + output_shape[1:])
return deconv
def __call__(self, input_layer, output_size, scope=None, in_dim=None, stddev=0.02, bias_start=0.0):
shape = input_layer.shape
input_ = input_layer.tensor
try:
if len(shape) == 4:
input_ = tf.reshape(input_, tf.pack([tf.shape(input_)[0], np.prod(shape[1:])]))
input_.set_shape([None, np.prod(shape[1:])])
shape = input_.get_shape().as_list()
with tf.variable_scope(scope or "Linear"):
matrix = self.variable("Matrix", [in_dim or shape[1], output_size], dt=tf.float32,
init=tf.random_normal_initializer(stddev=stddev))
bias = self.variable("bias", [output_size], init=tf.constant_initializer(bias_start))
return input_layer.with_tensor(tf.matmul(input_, matrix) + bias, parameters=self.vars)
except Exception:
import ipdb; ipdb.set_trace()
def __call__(self, x, scope=None):
with tf.variable_scope(scope or type(self).__name__):
# Check if the input size exist.
input_size = x.get_shape().with_rank(2)[1]
if input_size is None:
raise ValueError("Expecting input_size to be set.")
maxout_Wo = tf.get_variable(name='Wo', shape=(input_size, 2*self._units),
initializer=gaussian_initializer(mean=0.0, std=0.01))
maxout_b = tf.get_variable(name='b', shape=(2*self._units,),
initializer=tf.constant_initializer(0.0))
# 1st. Compute on all the 2 channels and reshape.
t = tf.matmul(x, maxout_Wo) + maxout_b
t = tf.reshape(t, shape=(-1, self._units, 2))
# 2nd. Do maxout op, now has shape: (None, self._units)
maxout_t = tf.reduce_max(t, axis=-1)
return maxout_t
ops.py 文件源码
项目：Unsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks
作者: xtarx
项目源码
文件源码
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def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
ops.py 文件源码
项目：Unsupervised-Anomaly-Detection-with-Generative-Adversarial-Networks
作者: xtarx
项目源码
文件源码
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def minibatch_discrimination(input_layer, num_kernels, dim_per_kernel=5, name='minibatch_discrim'):
# batch_size = input_layer.shape[0]
# num_features = input_layer.shape[1]
batch_size = input_layer.get_shape().as_list()[0]
num_features = input_layer.get_shape().as_list()[1]
W = tf.get_variable('W', [num_features, num_kernels * dim_per_kernel],
initializer=tf.contrib.layers.xavier_initializer())
b = tf.get_variable('b', [num_kernels], initializer=tf.constant_initializer(0.0))
activation = tf.matmul(input_layer, W)
activation = tf.reshape(activation, [batch_size, num_kernels, dim_per_kernel])
tmp1 = tf.expand_dims(activation, 3)
tmp2 = tf.transpose(activation, perm=[1, 2, 0])
tmp2 = tf.expand_dims(tmp2, 0)
abs_diff = tf.reduce_sum(tf.abs(tmp1 - tmp2), reduction_indices=[2])
f = tf.reduce_sum(tf.exp(-abs_diff), reduction_indices=[2])
f = f + b
return f
def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
def __init__(self, sess, pred_network, env, dataset, conf):
self.sess = sess
self.pred_network = pred_network
self.env = env
self.dataset = dataset
self.ckpt_dir = conf.ckpt_dir
self.ckpt_path = conf.ckpt_path
self.max_iter = conf.max_iter
self.max_to_keep = conf.max_to_keep
self.batch_size = conf.batch_size
self.df_dim = 64
self.learning_rate = conf.learning_rate
self.learning_rate_minimum = conf.learning_rate_minimum
self.learning_rate_decay = conf.learning_rate_decay
self.learning_rate_decay_step = conf.learning_rate_decay_step
self.global_step = tf.get_variable('global_step', [],initializer=tf.constant_initializer(0), trainable=False)
self.d_bn1 = batch_norm(name='d_bn1')
self.d_bn2 = batch_norm(name='d_bn2')
self.d_bn3 = batch_norm(name='d_bn3')
self.build_opt()
def layer_norm_all(h, base, num_units, scope):
# Layer Norm (faster version)
#
# Performs layer norm on multiple base at once (ie, i, g, j, o for lstm)
#
# Reshapes h in to perform layer norm in parallel
with tf.variable_scope(scope):
h_reshape = tf.reshape(h, [-1, base, num_units])
mean = tf.reduce_mean(h_reshape, [2], keep_dims=True)
var = tf.reduce_mean(tf.square(h_reshape - mean), [2], keep_dims=True)
epsilon = tf.constant(1e-3)
rstd = tf.rsqrt(var + epsilon)
h_reshape = (h_reshape - mean) * rstd
# reshape back to original
h = tf.reshape(h_reshape, [-1, base * num_units])
alpha = tf.get_variable('layer_norm_alpha', [4 * num_units],
initializer=tf.constant_initializer(1.0), dtype=tf.float32)
bias = tf.get_variable('layer_norm_bias', [4 * num_units],
initializer=tf.constant_initializer(0.0), dtype=tf.float32)
return (h * alpha) + bias
def build_network(name_scope, env):
w_init_dense = tf.truncated_normal_initializer() #contrib.layers.xavier_initializer()
b_init = tf.constant_initializer(value=0.0)
with tf.variable_scope(name_scope):
input_tensor = tf.placeholder(tf.float32,
shape=tf_utils.get_input_tensor_shape(env),
name='policy_input_'+name_scope)
net = tf.contrib.layers.fully_connected(input_tensor,
32, #env.action_space.n, #32,
activation_fn=tf.nn.tanh, #sigmoid,
weights_initializer=w_init_dense,
biases_initializer=b_init,
scope='dense1_'+name_scope)
net = tf.contrib.layers.fully_connected(net,
env.action_space.n,
weights_initializer=w_init_dense,
biases_initializer=b_init,
scope='dense2_'+name_scope)
net = tf.contrib.layers.softmax(net)
return [input_tensor], [net]
def rnn_layers(features, sequence_length, num_classes):
"""Build a stack of RNN layers from input features"""
# Input features is [batchSize paddedSeqLen numFeatures]
logit_activation = tf.nn.relu
weight_initializer = tf.contrib.layers.variance_scaling_initializer()
bias_initializer = tf.constant_initializer(value=0.0)
with tf.variable_scope("rnn"):
# Transpose to time-major order for efficiency
rnn_sequence = tf.transpose(features, perm=[1, 0, 2], name='time_major')
rnn1 = rnn_layer(rnn_sequence, sequence_length, rnn_size, 'bdrnn1')
rnn2 = rnn_layer(rnn1, sequence_length, rnn_size, 'bdrnn2')
rnn_logits = tf.layers.dense( rnn2, num_classes+1,
activation=logit_activation,
kernel_initializer=weight_initializer,
bias_initializer=bias_initializer,
name='logits')
return rnn_logits
def new_fc_layer( self, bottom, output_size, name ):
shape = bottom.get_shape().as_list()
dim = np.prod( shape[1:] )
x = tf.reshape( bottom, [-1, dim])
input_size = dim
with tf.variable_scope(name):
w = tf.get_variable(
"W",
shape=[input_size, output_size],
initializer=tf.random_normal_initializer(0., 0.005))
b = tf.get_variable(
"b",
shape=[output_size],
initializer=tf.constant_initializer(0.))
fc = tf.nn.bias_add( tf.matmul(x, w), b)
return fc
def new_fc_layer( self, bottom, output_size, name ):
shape = bottom.get_shape().as_list()
dim = np.prod( shape[1:] )
x = tf.reshape( bottom, [-1, dim])
input_size = dim
with tf.variable_scope(name):
w = tf.get_variable(
"W",
shape=[input_size, output_size],
initializer=tf.random_normal_initializer(0., 0.005))
b = tf.get_variable(
"b",
shape=[output_size],
initializer=tf.constant_initializer(0.))
fc = tf.nn.bias_add( tf.matmul(x, w), b)
return fc
def linear(input, output_dim, scope=None, stddev=None):
if stddev:
norm = tf.random_normal_initializer(stddev=stddev)
else:
norm = tf.random_normal_initializer(
stddev=np.sqrt(2.0 / input.get_shape()[1].value)
)
const = tf.constant_initializer(0.0)
with tf.variable_scope(scope or 'linear'):
w = tf.get_variable(
'w',
[input.get_shape()[1], output_dim],
initializer=norm
)
b = tf.get_variable('b', [output_dim], initializer=const)
return tf.matmul(input, w) + b
def fc(self, input, num_out, name, relu=True):
with tf.variable_scope(name) as scope:
input_shape = input.get_shape()
if input_shape.ndims == 4:
# The input is spatial. Vectorize it first.
dim = 1
for d in input_shape[1:].as_list():
dim *= d
feed_in = tf.reshape(input, [-1, dim])
else:
feed_in, dim = (input, input_shape[-1].value)
weights = self.make_var('weights', shape=[dim, num_out], init_func=tf.truncated_normal_initializer(stddev = 0.1))
biases = self.make_var('biases', [num_out], init_func=tf.constant_initializer(0.1))
op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
fc = op(feed_in, weights, biases, name=scope.name)
return fc
