def run(self):
"""Run evaluation."""
# Create logging directory if not exists.
if not os.path.isdir(self._eval_log_dir):
os.makedirs(self._eval_log_dir)
# Compute loss function and other evaluating metrics.
self._initialize()
# Visualize input images in Tensorboard.
self._summary_ops.append(tf.image_summary("Eval_Image", self._observations, max_images=5))
# Use `slim.evaluation.evaluation_loop` to evaluate the model periodically.
slim.evaluation.evaluation_loop(
master='',
checkpoint_dir=self._train_log_dir,
logdir=self._eval_log_dir,
num_evals=self._config.num_batches,
eval_op=self._metrics_to_updates.values(),
summary_op=tf.merge_summary(self._summary_ops),
eval_interval_secs=self._config.eval_interval_secs)
python类merge_summary()的实例源码
def run(self):
"""Run training."""
# Create logging directory if not exists.
if not os.path.isdir(self._train_log_dir):
os.makedirs(self._train_log_dir)
# Load data and compute loss function
self._initialize()
# Visualize input images in Tensorboard.
self._summary_ops.append(tf.image_summary("Image_Train", self._observations, max_images=5))
# Initialize optimizer.
optimizer = tf.train.AdadeltaOptimizer(self._config.learning_rate)
train_op = slim.learning.create_train_op(self._loss, optimizer)
# Use `slim.learning.train` to manage training.
slim.learning.train(train_op=train_op,
logdir=self._train_log_dir,
graph=self._graph,
number_of_steps=self._config.train_steps,
summary_op=tf.merge_summary(self._summary_ops),
save_summaries_secs=self._config.save_summaries_secs,
save_interval_secs=self._config.save_interval_secs)
def define_summaries(self):
'''Helper function for init_opt'''
all_sum = {'g': [], 'd': [], 'hr_g': [], 'hr_d': [], 'hist': []}
for k, v in self.log_vars:
if k.startswith('g'):
all_sum['g'].append(tf.scalar_summary(k, v))
elif k.startswith('d'):
all_sum['d'].append(tf.scalar_summary(k, v))
elif k.startswith('hr_g'):
all_sum['hr_g'].append(tf.scalar_summary(k, v))
elif k.startswith('hr_d'):
all_sum['hr_d'].append(tf.scalar_summary(k, v))
elif k.startswith('hist'):
all_sum['hist'].append(tf.histogram_summary(k, v))
self.g_sum = tf.merge_summary(all_sum['g'])
self.d_sum = tf.merge_summary(all_sum['d'])
self.hr_g_sum = tf.merge_summary(all_sum['hr_g'])
self.hr_d_sum = tf.merge_summary(all_sum['hr_d'])
self.hist_sum = tf.merge_summary(all_sum['hist'])
def visualization(self, n):
fake_sum_train, superimage_train =\
self.visualize_one_superimage(self.fake_images[:n * n],
self.images[:n * n],
n, "train")
fake_sum_test, superimage_test =\
self.visualize_one_superimage(self.fake_images[n * n:2 * n * n],
self.images[n * n:2 * n * n],
n, "test")
self.superimages = tf.concat(0, [superimage_train, superimage_test])
self.image_summary = tf.merge_summary([fake_sum_train, fake_sum_test])
hr_fake_sum_train, hr_superimage_train =\
self.visualize_one_superimage(self.hr_fake_images[:n * n],
self.hr_images[:n * n, :, :, :],
n, "hr_train")
hr_fake_sum_test, hr_superimage_test =\
self.visualize_one_superimage(self.hr_fake_images[n * n:2 * n * n],
self.hr_images[n * n:2 * n * n],
n, "hr_test")
self.hr_superimages =\
tf.concat(0, [hr_superimage_train, hr_superimage_test])
self.hr_image_summary =\
tf.merge_summary([hr_fake_sum_train, hr_fake_sum_test])
def summarize_variables(train_vars=None, summary_collection="tflearn_summ"):
""" summarize_variables.
Arguemnts:
train_vars: list of `Variable`. The variable weights to monitor.
summary_collection: A collection to add this summary to and
also used for returning a merged summary over all its elements.
Default: 'tflearn_summ'.
Returns:
`Tensor`. Merge of all summary in 'summary_collection'
"""
if not train_vars: train_vars = tf.trainable_variables()
summaries.add_trainable_vars_summary(train_vars, "", "", summary_collection)
return merge_summary(tf.get_collection(summary_collection))
def summarize(value, type, name, summary_collection="tflearn_summ"):
""" summarize.
A custom summarization op.
Arguemnts:
value: `Tensor`. The tensor value to monitor.
type: `str` among 'histogram', 'scalar'. The data monitoring type.
name: `str`. A name for this summary.
summary_collection: A collection to add this summary to and
also used for returning a merged summary over all its elements.
Default: 'tflearn_summ'.
Returns:
`Tensor`. Merge of all summary in 'summary_collection'.
"""
if tf012:
name = name.replace(':', '_')
summaries.get_summary(type, name, value, summary_collection)
return merge_summary(tf.get_collection(summary_collection))
def create_summaries(self, verbose=2):
""" Create summaries with `verbose` level """
summ_collection = self.name + "_training_summaries"
if verbose in [3]:
# Summarize activations
activations = tf.get_collection(tf.GraphKeys.ACTIVATIONS)
summarize_activations(activations, summ_collection)
if verbose in [2, 3]:
# Summarize variable weights
summarize_variables(self.train_vars, summ_collection)
if verbose in [1, 2, 3]:
# Summarize gradients
summarize_gradients(self.grad, summ_collection)
self.summ_op = merge_summary(tf.get_collection(summ_collection))
def define_summaries(self):
'''Helper function for init_opt'''
all_sum = {'g': [], 'd': [], 'hr_g': [], 'hr_d': [], 'hist': []}
for k, v in self.log_vars:
if k.startswith('g'):
all_sum['g'].append(tf.scalar_summary(k, v))
elif k.startswith('d'):
all_sum['d'].append(tf.scalar_summary(k, v))
elif k.startswith('hr_g'):
all_sum['hr_g'].append(tf.scalar_summary(k, v))
elif k.startswith('hr_d'):
all_sum['hr_d'].append(tf.scalar_summary(k, v))
elif k.startswith('hist'):
all_sum['hist'].append(tf.histogram_summary(k, v))
self.g_sum = tf.merge_summary(all_sum['g'])
self.d_sum = tf.merge_summary(all_sum['d'])
self.hr_g_sum = tf.merge_summary(all_sum['hr_g'])
self.hr_d_sum = tf.merge_summary(all_sum['hr_d'])
self.hist_sum = tf.merge_summary(all_sum['hist'])
def visualization(self, n):
fake_sum_train, superimage_train =\
self.visualize_one_superimage(self.fake_images[:n * n],
self.images[:n * n],
n, "train")
fake_sum_test, superimage_test =\
self.visualize_one_superimage(self.fake_images[n * n:2 * n * n],
self.images[n * n:2 * n * n],
n, "test")
self.superimages = tf.concat(0, [superimage_train, superimage_test])
self.image_summary = tf.merge_summary([fake_sum_train, fake_sum_test])
hr_fake_sum_train, hr_superimage_train =\
self.visualize_one_superimage(self.hr_fake_images[:n * n],
self.hr_images[:n * n, :, :, :],
n, "hr_train")
hr_fake_sum_test, hr_superimage_test =\
self.visualize_one_superimage(self.hr_fake_images[n * n:2 * n * n],
self.hr_images[n * n:2 * n * n],
n, "hr_test")
self.hr_superimages =\
tf.concat(0, [hr_superimage_train, hr_superimage_test])
self.hr_image_summary =\
tf.merge_summary([hr_fake_sum_train, hr_fake_sum_test])
def add_summaries(summaries, learning_rate, grads):
""" Add summary ops"""
# Track quantities for Tensorboard display
summaries.append(tf.scalar_summary('learning_rate', learning_rate))
# Add histograms for gradients.
for grad, var in grads:
if grad is not None:
summaries.append(
tf.histogram_summary(var.op.name +
'/gradients', grad))
# Add histograms for trainable variables.
for var in tf.trainable_variables():
summaries.append(tf.histogram_summary(var.op.name, var))
# Build the summary operation from the last tower summaries.
summary_op = tf.merge_summary(summaries)
return summary_op
region_proposal.py 文件源码
项目：lstm-rcnn-pedestrian-detection
作者: buffer51
项目源码
文件源码
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def create_train_summaries(learning_rate, clas_loss, reg_loss, rpn_loss, clas_accuracy, clas_positive_percentage, clas_positive_accuracy, VGG16D_activations, clas_activations):
with tf.name_scope('train'):
learning_rate_summary = tf.scalar_summary('learning_rate', learning_rate)
loss_clas_summary = tf.scalar_summary('loss/clas', clas_loss)
loss_reg_summary = tf.scalar_summary('loss/reg', reg_loss)
loss_rpn_summary = tf.scalar_summary('loss/rpn', rpn_loss)
stat_accuracy_summary = tf.scalar_summary('stat/accuracy', clas_accuracy)
stat_positive_percentage_summary = tf.scalar_summary('stat/positive_percentage', clas_positive_percentage)
stat_positive_accuracy_summary = tf.scalar_summary('stat/positive_accuracy', clas_positive_accuracy)
VGG16D_histogram = tf.histogram_summary('activations/VGG16D', VGG16D_activations)
clas_histogram = tf.histogram_summary('activations/clas', clas_activations)
return tf.merge_summary([learning_rate_summary, loss_clas_summary, loss_reg_summary, loss_rpn_summary, stat_accuracy_summary, stat_positive_percentage_summary, stat_positive_accuracy_summary, VGG16D_histogram, clas_histogram])
def build_train(self):
self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=tf.get_variable_scope().name)
regularization = tf.contrib.layers.apply_regularization(
tf.contrib.layers.l2_regularizer(0.01),
self.variables,
)
self.op_grad_actions = tf.placeholder(tf.float32, [None, self.action_dim])
self.op_loss = tf.reduce_sum(-self.op_grad_actions * self.op_actions) # + regularization
self.op_summary = tf.merge_summary([
tf.scalar_summary("actor loss", self.op_loss),
tf.histogram_summary("actor", self.op_actions),
])
self.op_train = tf.train.AdamOptimizer(self.learning_rate).minimize(self.op_loss)
# def get_op_train(self):
# self.op_grads = tf.gradients(self.op_actions, self.variables, -self.op_grad_actions)
# self.op_grads2 = tf.gradients(self.op_loss, self.variables)
# return tf.train.AdamOptimizer(1e-4).apply_gradients(zip(self.op_grads2, self.variables))
def build_train(self):
self.op_rewards = tf.placeholder(tf.float32, [None])
self.variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=tf.get_variable_scope().name)
regularization = tf.contrib.layers.apply_regularization(
tf.contrib.layers.l2_regularizer(0.01),
self.variables,
)
self.op_loss = tf.reduce_mean((self.op_rewards - self.op_critic)**2) + regularization
self.op_summary = tf.merge_summary([
tf.scalar_summary("critic loss", self.op_loss),
tf.histogram_summary("critic", self.op_critic),
])
self.op_grad_actions = tf.gradients(self.op_critic, self.op_actions)[0]
self.op_train = tf.train.AdamOptimizer(self.learning_rate).minimize(self.op_loss)
def define_summaries(self):
'''Helper function for init_opt'''
all_sum = {'g': [], 'd': [], 'hist': []}
for k, v in self.log_vars:
if k.startswith('g'):
all_sum['g'].append(tf.scalar_summary(k, v))
elif k.startswith('d'):
all_sum['d'].append(tf.scalar_summary(k, v))
elif k.startswith('hist'):
all_sum['hist'].append(tf.histogram_summary(k, v))
self.g_sum = tf.merge_summary(all_sum['g'])
self.d_sum = tf.merge_summary(all_sum['d'])
self.hist_sum = tf.merge_summary(all_sum['hist'])
def visualization(self, n):
fake_sum_train, superimage_train = \
self.visualize_one_superimage(self.fake_images[:n * n],
self.images[:n * n],
n, "train")
fake_sum_test, superimage_test = \
self.visualize_one_superimage(self.fake_images[n * n:2 * n * n],
self.images[n * n:2 * n * n],
n, "test")
self.superimages = tf.concat(0, [superimage_train, superimage_test])
self.image_summary = tf.merge_summary([fake_sum_train, fake_sum_test])
def summarize_activations(activations, summary_collection="tflearn_summ"):
""" summarize_activations.
Arguemnts:
activations: list of `Tensor`. The activations to monitor.
summary_collection: A collection to add this summary to and
also used for returning a merged summary over all its elements.
Default: 'tflearn_summ'.
Returns:
`Tensor`. Merge of all summary in 'summary_collection'
"""
summaries.add_activations_summary(activations, "", "", summary_collection)
return merge_summary(tf.get_collection(summary_collection))
def summarize_gradients(grads, summary_collection="tflearn_summ"):
""" summarize_gradients.
Arguemnts:
grads: list of `Tensor`. The gradients to monitor.
summary_collection: A collection to add this summary to and
also used for returning a merged summary over all its elements.
Default: 'tflearn_summ'.
Returns:
`Tensor`. Merge of all summary in 'summary_collection'
"""
summaries.add_gradients_summary(grads, "", "", summary_collection)
return merge_summary(tf.get_collection(summary_collection))
def define_summaries(self):
'''Helper function for init_opt'''
all_sum = {'g': [], 'd': [], 'hist': []}
for k, v in self.log_vars:
if k.startswith('g'):
all_sum['g'].append(tf.scalar_summary(k, v))
elif k.startswith('d'):
all_sum['d'].append(tf.scalar_summary(k, v))
elif k.startswith('hist'):
all_sum['hist'].append(tf.histogram_summary(k, v))
self.g_sum = tf.merge_summary(all_sum['g'])
self.d_sum = tf.merge_summary(all_sum['d'])
self.hist_sum = tf.merge_summary(all_sum['hist'])
def visualization(self, n):
fake_sum_train, superimage_train = \
self.visualize_one_superimage(self.fake_images[:n * n],
self.images[:n * n],
n, "train")
fake_sum_test, superimage_test = \
self.visualize_one_superimage(self.fake_images[n * n:2 * n * n],
self.images[n * n:2 * n * n],
n, "test")
self.superimages = tf.concat(0, [superimage_train, superimage_test])
self.image_summary = tf.merge_summary([fake_sum_train, fake_sum_test])
def __init__(self, config, scope):
self.scope = scope
self.config = config
self.global_step = tf.get_variable('global_step', shape=[], dtype='int32',
initializer=tf.constant_initializer(0), trainable=False)
# Define forward inputs here
N, M, JX, JQ, VW, VC, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.max_word_size
self.x = tf.placeholder('int32', [N, M, None], name='x')
self.cx = tf.placeholder('int32', [N, M, None, W], name='cx')
self.x_mask = tf.placeholder('bool', [N, M, None], name='x_mask')
self.q = tf.placeholder('int32', [N, JQ], name='q')
self.cq = tf.placeholder('int32', [N, JQ, W], name='cq')
self.q_mask = tf.placeholder('bool', [N, JQ], name='q_mask')
self.y = tf.placeholder('bool', [N, M, JX], name='y')
self.is_train = tf.placeholder('bool', [], name='is_train')
self.new_emb_mat = tf.placeholder('float', [None, config.word_emb_size], name='new_emb_mat')
# Define misc
self.tensor_dict = {}
# Forward outputs / loss inputs
self.logits = None
self.yp = None
self.var_list = None
# Loss outputs
self.loss = None
self._build_forward()
self._build_loss()
if config.mode == 'train':
self._build_ema()
self.summary = tf.merge_all_summaries()
self.summary = tf.merge_summary(tf.get_collection("summaries", scope=self.scope))
def __init__(self, config, scope):
self.scope = scope
self.config = config
self.global_step = tf.get_variable('global_step', shape=[], dtype='int32',
initializer=tf.constant_initializer(0), trainable=False)
# Define forward inputs here
N, M, JX, JQ, VW, VC, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.max_word_size
self.x = tf.placeholder('int32', [N, M, None], name='x')
self.cx = tf.placeholder('int32', [N, M, None, W], name='cx')
self.x_mask = tf.placeholder('bool', [N, M, None], name='x_mask')
self.q = tf.placeholder('int32', [N, JQ], name='q')
self.cq = tf.placeholder('int32', [N, JQ, W], name='cq')
self.q_mask = tf.placeholder('bool', [N, JQ], name='q_mask')
self.y = tf.placeholder('bool', [N, M, JX], name='y')
self.is_train = tf.placeholder('bool', [], name='is_train')
self.new_emb_mat = tf.placeholder('float', [None, config.word_emb_size], name='new_emb_mat')
# Define misc
self.tensor_dict = {}
# Forward outputs / loss inputs
self.logits = None
self.yp = None
self.var_list = None
# Loss outputs
self.loss = None
self._build_forward()
self._build_loss()
if config.mode == 'train':
self._build_ema()
self.summary = tf.merge_all_summaries()
self.summary = tf.merge_summary(tf.get_collection("summaries", scope=self.scope))
def __init__(self, conf, images=None, scores=None, goal_pos=None, desig_pos=None):
batchsize = int(conf['batch_size'])
if goal_pos is None:
self.goal_pos = goal_pos= tf.placeholder(tf.float32, name='goalpos', shape=(batchsize, 2))
if desig_pos is None:
self.desig_pos = desig_pos = tf.placeholder(tf.float32, name='desig_pos_pl', shape=(batchsize, 2))
if scores is None:
self.scores = scores = tf.placeholder(tf.float32, name='score_pl', shape=(batchsize, 1))
if images is None:
self.images = images = tf.placeholder(tf.float32, name='images_pl', shape=(batchsize, 1, 64,64,3))
self.prefix = prefix = tf.placeholder(tf.string, [])
from value_model import construct_model
summaries = []
inf_scores = construct_model(conf, images, goal_pos, desig_pos)
self.inf_scores = inf_scores
self.loss = loss = mean_squared_error(inf_scores, scores)
summaries.append(tf.scalar_summary(prefix + '_loss', loss))
self.lr = tf.placeholder_with_default(conf['learning_rate'], ())
self.train_op = tf.train.AdamOptimizer(self.lr).minimize(loss)
self.summ_op = tf.merge_summary(summaries)
def __init__(self, config, scope):
self.scope = scope
self.config = config
self.global_step = tf.get_variable('global_step', shape=[], dtype='int32',
initializer=tf.constant_initializer(0), trainable=False)
# Define forward inputs here
N, M, JX, JQ, VW, VC, W = \
config.batch_size, config.max_num_sents, config.max_sent_size, \
config.max_ques_size, config.word_vocab_size, config.char_vocab_size, config.max_word_size
self.x = tf.placeholder('int32', [N, M, None], name='x')
self.cx = tf.placeholder('int32', [N, M, None, W], name='cx')
self.x_mask = tf.placeholder('bool', [N, M, None], name='x_mask')
self.q = tf.placeholder('int32', [N, JQ], name='q')
self.cq = tf.placeholder('int32', [N, JQ, W], name='cq')
self.q_mask = tf.placeholder('bool', [N, JQ], name='q_mask')
self.y = tf.placeholder('bool', [N, M, JX], name='y')
self.is_train = tf.placeholder('bool', [], name='is_train')
self.new_emb_mat = tf.placeholder('float', [None, config.word_emb_size], name='new_emb_mat')
# Define misc
self.tensor_dict = {}
# Forward outputs / loss inputs
self.logits = None
self.yp = None
self.var_list = None
# Loss outputs
self.loss = None
self._build_forward()
self._build_loss()
if config.mode == 'train':
self._build_ema()
self.summary = tf.merge_all_summaries()
self.summary = tf.merge_summary(tf.get_collection("summaries", scope=self.scope))
region_proposal.py 文件源码
项目：lstm-rcnn-pedestrian-detection
作者: buffer51
项目源码
文件源码
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def create_test_summaries(test_placeholders):
with tf.name_scope('test'):
accuracy_summary = tf.scalar_summary('accuracy', test_placeholders[0])
positive_recall_summary = tf.scalar_summary('recall/positive', test_placeholders[1])
negative_recall_summary = tf.scalar_summary('recall/negative', test_placeholders[2])
recall_summary = tf.scalar_summary('recall/global', test_placeholders[3])
positive_precision_summary = tf.scalar_summary('precision/positive', test_placeholders[4])
negative_precision_summary = tf.scalar_summary('precision/negative', test_placeholders[5])
precision_summary = tf.scalar_summary('precision/global', test_placeholders[6])
F_score_summary = tf.scalar_summary('F-score', test_placeholders[7])
return tf.merge_summary([accuracy_summary, positive_recall_summary, negative_recall_summary, recall_summary, positive_precision_summary, negative_precision_summary,precision_summary, F_score_summary])
def grad_histograms(grads_and_vars):
s = []
for grad, var in grads_and_vars:
s.append(tf.histogram_summary(var.op.name + '', var))
s.append(tf.histogram_summary(var.op.name + '/gradients', grad))
return tf.merge_summary(s)
def _init_summaries(self):
self.accuracy = tf.placeholder_with_default(0.0, shape=(), name='Accuracy')
self.accuracy_summary = tf.scalar_summary('Accuracy summary', self.accuracy)
self.f_pos_summary = tf.histogram_summary('f_pos', self.f_pos)
self.f_neg_summary = tf.histogram_summary('f_neg', self.f_neg)
self.loss_summary = tf.scalar_summary('Mini-batch loss', self.loss)
self.summary_op = tf.merge_summary(
[
self.f_pos_summary,
self.f_neg_summary,
self.loss_summary
]
)
def construct(self, hidden_layer_size):
with self.session.graph.as_default():
with tf.name_scope("inputs"):
self.images = tf.placeholder(tf.float32, [None, self.WIDTH, self.HEIGHT, 1], name="images")
self.labels = tf.placeholder(tf.int64, [None], name="labels")
flattened_images = tf_layers.flatten(self.images, scope="preprocessing")
hidden_layer = tf_layers.fully_connected(flattened_images, num_outputs=hidden_layer_size, activation_fn=tf.nn.relu, scope="hidden_layer")
output_layer = tf_layers.fully_connected(hidden_layer, num_outputs=self.LABELS, activation_fn=None, scope="output_layer")
self.predictions = tf.argmax(output_layer, 1)
loss = tf_losses.sparse_softmax_cross_entropy(output_layer, self.labels, scope="loss")
self.global_step = tf.Variable(0, dtype=tf.int64, trainable=False, name="global_step")
self.training = tf.train.AdamOptimizer().minimize(loss, global_step=self.global_step)
self.accuracy = tf_metrics.accuracy(self.predictions, self.labels)
# Summaries
self.summaries = {"training": tf.merge_summary([tf.scalar_summary("train/loss", loss),
tf.scalar_summary("train/accuracy", self.accuracy)])}
for dataset in ["dev", "test"]:
self.summaries[dataset] = tf.scalar_summary(dataset+"/accuracy", self.accuracy)
# Initialize variables
self.session.run(tf.initialize_all_variables())
# Finalize graph and log it if requested
self.session.graph.finalize()
if self.summary_writer:
self.summary_writer.add_graph(self.session.graph)
def __init__(self, rnn_cell, rnn_cell_dim, method, words, logdir, expname, threads=1, seed=42):
# Create an empty graph and a session
graph = tf.Graph()
graph.seed = seed
self.session = tf.Session(graph = graph, config=tf.ConfigProto(inter_op_parallelism_threads=threads,
intra_op_parallelism_threads=threads))
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
self.summary_writer = tf.train.SummaryWriter("{}/{}-{}".format(logdir, timestamp, expname), flush_secs=10)
# Construct the graph
with self.session.graph.as_default():
if rnn_cell == "LSTM":
rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_cell_dim)
elif rnn_cell == "GRU":
rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_cell_dim)
else:
raise ValueError("Unknown rnn_cell {}".format(rnn_cell))
self.global_step = tf.Variable(0, dtype=tf.int64, trainable=False, name="global_step")
self.sentence_lens = tf.placeholder(tf.int32, [None])
self.forms = tf.placeholder(tf.int32, [None, None])
self.tags = tf.placeholder(tf.int32, [None, None])
# TODO
# loss = ...
# self.training = ...
# self.predictions = ...
# self.accuracy = ...
self.dataset_name = tf.placeholder(tf.string, [])
self.summary = tf.merge_summary([tf.scalar_summary(self.dataset_name+"/loss", loss),
tf.scalar_summary(self.dataset_name+"/accuracy", self.accuracy)])
# Initialize variables
self.session.run(tf.initialize_all_variables())
def __init__(self, logdir, experiment, threads):
# Construct the graph
with tf.name_scope("inputs"):
self.images = tf.placeholder(tf.float32, [None, WIDTH, HEIGHT, 1], name="images")
self.labels = tf.placeholder(tf.int64, [None], name="labels")
flattened_images = layers.flatten(self.images)
hidden_layer = layers.fully_connected(flattened_images, num_outputs=HIDDEN, activation_fn=tf.nn.relu, scope="hidden_layer")
output_layer = layers.fully_connected(hidden_layer, num_outputs=LABELS, activation_fn=None, scope="output_layer")
loss = losses.sparse_softmax_cross_entropy(output_layer, self.labels, scope="loss")
self.training = layers.optimize_loss(loss, None, None, tf.train.AdamOptimizer(), summaries=['loss', 'gradients', 'gradient_norm'], name='training')
with tf.name_scope("accuracy"):
predictions = tf.argmax(output_layer, 1, name="predictions")
accuracy = metrics.accuracy(predictions, self.labels)
tf.scalar_summary("training/accuracy", accuracy)
with tf.name_scope("confusion_matrix"):
confusion_matrix = metrics.confusion_matrix(predictions, self.labels, weights=tf.not_equal(predictions, self.labels), dtype=tf.float32)
confusion_image = tf.reshape(confusion_matrix, [1, LABELS, LABELS, 1])
# Summaries
self.summaries = {'training': tf.merge_all_summaries() }
for dataset in ["dev", "test"]:
self.summaries[dataset] = tf.merge_summary([tf.scalar_summary(dataset + "/accuracy", accuracy),
tf.image_summary(dataset + "/confusion_matrix", confusion_image)])
# Create the session
self.session = tf.Session(config=tf.ConfigProto(inter_op_parallelism_threads=threads,
intra_op_parallelism_threads=threads))
self.session.run(tf.initialize_all_variables())
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
self.summary_writer = tf.train.SummaryWriter("{}/{}-{}".format(logdir, timestamp, experiment), graph=self.session.graph, flush_secs=10)
self.steps = 0
def __init__(self, rnn_cell, rnn_cell_dim, num_chars, bow_char, eow_char, logdir, expname, threads=1, seed=42):
# Create an empty graph and a session
graph = tf.Graph()
graph.seed = seed
self.session = tf.Session(graph = graph, config=tf.ConfigProto(inter_op_parallelism_threads=threads,
intra_op_parallelism_threads=threads))
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
self.summary_writer = tf.train.SummaryWriter("{}/{}-{}".format(logdir, timestamp, expname), flush_secs=10)
# Construct the graph
with self.session.graph.as_default():
if rnn_cell == "LSTM":
rnn_cell = tf.nn.rnn_cell.LSTMCell(rnn_cell_dim)
elif rnn_cell == "GRU":
rnn_cell = tf.nn.rnn_cell.GRUCell(rnn_cell_dim)
else:
raise ValueError("Unknown rnn_cell {}".format(rnn_cell))
self.global_step = tf.Variable(0, dtype=tf.int64, trainable=False, name="global_step")
self.sentence_lens = tf.placeholder(tf.int32, [None])
self.form_ids = tf.placeholder(tf.int32, [None, None])
self.forms = tf.placeholder(tf.int32, [None, None])
self.form_lens = tf.placeholder(tf.int32, [None])
self.lemma_ids = tf.placeholder(tf.int32, [None, None])
self.lemmas = tf.placeholder(tf.int32, [None, None])
self.lemma_lens = tf.placeholder(tf.int32, [None])
# TODO
# loss = ...
# self.training = ...
# self.predictions = ...
# self.accuracy = ...
self.dataset_name = tf.placeholder(tf.string, [])
self.summary = tf.merge_summary([tf.scalar_summary(self.dataset_name+"/loss", loss),
tf.scalar_summary(self.dataset_name+"/accuracy", self.accuracy)])
# Initialize variables
self.session.run(tf.initialize_all_variables())
