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Ich habe versucht, ein gut strukturiertes Neuronales Netzwerkmodell mit Tensorflow zu schreiben. Aber ich habe ein Problem mit dem Feed der Daten von tfrecord in den Graphen getroffen. Der Code ist wie unten, es hängt an der folgenden Funktion, wie kann ich es funktionieren lassen?Tensorflow, lesen tfrecord ohne eine Grafik
Bilder, Etiketten = network.load_tfrecord_data (1)
diese Funktion können die Funktionen (Bilder) und Labels aus meiner Daten-Datei nicht bekommen, .tfrecords?
Irgendeine Idee wird geschätzt?
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)