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Tensorflowを使用して構造化されたニューラルネットワークモデルを作成しようとしました。しかし、tfrecordのデータをグラフにフィードすることに問題がありました。コードは次のとおりですが、次の関数でハングアップしますが、どうすれば動作させることができますか？Tensorflow、グラフのないtfrecordを読み取る
画像、ラベル= network.load_tfrecord_data（1）
この機能は、私のデータ・ファイルから.tfrecordsをフィーチャー（画像）とラベルを取得することはできませんか？
ご了承ください。
from __future__ import division
from __future__ import print_function
import datetime
import numpy as np
import tensorflow as tf
layers = tf.contrib.layers
losses = tf.contrib.losses
metrics = tf.contrib.metrics
LABELS = 10
WIDTH = 28
HEIGHT = 28
HIDDEN = 100
def read_and_decode_single_example(filename):
filename_queue = tf.train.string_input_producer([filename], num_epochs=None)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
features={
'label': tf.FixedLenFeature([], tf.int64),
'image': tf.FixedLenFeature([50176], tf.int64)
})
label = features['label']
image = features['image']
image = tf.reshape(image, [-1, 224, 224, 1])
label = tf.one_hot(label - 1, 11, dtype=tf.int64)
return label, image
class Network:
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")
# self.keep_prob = keep_prob
self.keep_prob = tf.placeholder(tf.float32, name="keep_prob")
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(labels=self.labels, logits=output_layer, 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.summary.scalar("training/accuracy", accuracy)
self.accuracy = metrics.accuracy(predictions, self.labels)
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.summary.merge_all() }
for dataset in ["dev", "test"]:
self.summaries[dataset] = tf.summary.scalar(dataset + "/loss", loss)
self.summaries[dataset] = tf.summary.scalar(dataset + "/accuracy", accuracy)
self.summaries[dataset] = tf.summary.image(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.global_variables_initializer())
timestamp = datetime.datetime.now().strftime("%Y-%m-%d_%H%M%S")
self.summary_writer = tf.summary.FileWriter("{}/{}-{}".format(logdir, timestamp, experiment), graph=self.session.graph, flush_secs=10)
self.steps = 0
def train(self, images, labels, keep_prob):
self.steps += 1
feed_dict = {self.images: self.session.run(images), self.labels: self.session.run(labels), self.keep_prob: keep_prob}
if self.steps == 1:
metadata = tf.RunMetadata()
self.session.run(self.training, feed_dict, options=tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE), run_metadata=metadata)
self.summary_writer.add_run_metadata(metadata, 'step1')
elif self.steps % 100 == 0:
_, summary = self.session.run([self.training, self.summaries['training']], feed_dict)
self.summary_writer.add_summary(summary, self.steps)
else:
self.session.run(self.training, feed_dict)
def evaluate(self, dataset, images, labels):
feed_dict ={self.images: images, self.labels: labels, self.keep_prob: 1}
summary = self.summaries[dataset].eval({self.images: images, self.labels: labels, self.keep_prob: 1}, self.session)
self.summary_writer.add_summary(summary, self.steps)
def load_tfrecord_data(self, training):
training = training
if training:
label, image = read_and_decode_single_example("mhad_Op_train.tfrecords")
# print(self.session.run(image))
else:
label, image = read_and_decode_single_example("mhad_Op_test.tfrecords")
# image = tf.cast(image, tf.float32)/255.
images_batch, labels_batch = tf.train.shuffle_batch(
[image, label], batch_size=50, num_threads=2,
capacity=80,
min_after_dequeue=30)
return images_batch, labels_batch
if __name__ == '__main__':
# Fix random seed
np.random.seed(42)
tf.set_random_seed(42)
# Parse arguments
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--batch_size', default=256, type=int, help='Batch size.')
parser.add_argument('--epochs', default=50, type=int, help='Number of epochs.')
parser.add_argument('--logdir', default="logs", type=str, help='Logdir name.')
parser.add_argument('--exp', default="mnist-final-confusion_matrix_customized_loss", type=str, help='Experiment name.')
parser.add_argument('--threads', default=1, type=int, help='Maximum number of threads to use.')
args = parser.parse_args()
# Load the data
keep_prob = 1
# Construct the network
network = Network(logdir=args.logdir, experiment=args.exp, threads=args.threads)
# Train
for i in range(args.epochs):
images, labels = network.load_tfrecord_data(1)
network.train(images, labels, keep_prob)
print('current epoch', i)