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Я пытаюсь создать простую прямую нейронную сеть, использующую TensorFlow и его форматирование tfr. Я использовал учебники и примеры TensorFlow в качестве справки: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/how_tos/reading_dataПрямой сетевой сеанс Tensorflow не останавливается
Учитывая значения «плавающего» количества продуктов питания, я хочу предсказать, какое значение имеет значение «счастье», которое оно производит.
food_test.json - это файл JSON, содержащий значение «еда» и связанное с ним «счастье». Это формат, в котором хранятся данные.
food_to_record.py основан на файле convert_to_records.py от tensorflow. Он читает в файле food_test.json и преобразует его в файл food_record.tfr.
food_reader.py основан на полном_состоянии_события_ценщика. Он читает файл food_record.tfr и запускает данные через нейронную сеть.
Я запустить программу в следующем порядке: 1. food_to_record.py 2. food_reader.py
Когда food_reader.py запускается, он начинает TensorFlow сессии, но сессия никогда не заканчивается, кто-нибудь знает причина этого?
food_test.json:
[
{
"food": 1.0,
"happiness": 2.0
},
{
"food": 1.4,
"happiness": 5.4
}
]
food_to_record.py:
#based off of tensorflow's convert_to_records.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import sys
import json
import tensorflow as tf
FLAGS = None
#feature for integers
def _int64_feature(value):
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
#feature for floats
def _float_feature(value):
return tf.train.Feature(float_list = tf.train.FloatList(value= [value]))
#feature for strings and others
def _bytes_feature(value):
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def main(unused_argv):
print("food_to_record:main")
script_dir = os.path.dirname(__file__)
file_path = os.path.join(script_dir, 'food_test.json')
with open(file_path) as data_file:
data = json.load(data_file)
print(data)
num_examples = 2
name = 'food_record'
filename = os.path.join(FLAGS.directory, name + '.tfrecords')
print('Writing', filename)
writer = tf.python_io.TFRecordWriter(filename)
for index in range(num_examples):
example = tf.train.Example(features=tf.train.Features(feature={
'food': _float_feature(data[index]['food']),
'happiness': _float_feature(data[index]['happiness'])
}))
writer.write(example.SerializeToString())
writer.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--directory',
type=str,
default='.',
help='Directory to download data files and write the converted result'
)
parser.add_argument(
'--validation_size',
type=int,
default=5000,
help="""\
Number of examples to separate from the training data for the validation
set.\
"""
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)
food_reader.py:
#based off of tensorflow's fully_connected_reader
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os.path
import sys
import time
import tensorflow as tf
# Basic model parameters as external flags.
FLAGS = None
# Constants used for dealing with the files
TRAIN_FILE = 'food_record.tfrecords'
# For simple testing purposes, use training file for validation
VALIDATION_FILE = 'food_record.tfrecords'
def read_and_decode(filename_queue):
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(
serialized_example,
# Defaults are not specified since both keys are required.
features={
'food': tf.FixedLenFeature([], tf.float32),
'happiness': tf.FixedLenFeature([], tf.float32)
})
food = tf.cast(features['food'], tf.float32)
happiness = tf.cast(features['happiness'], tf.float32)
food = tf.expand_dims(food, -1)
print("food shape: ", tf.shape(food))
print("happiness shape: ", tf.shape(happiness))
return food, happiness
def inputs(train, batch_size, num_epochs):
"""Reads input data num_epochs times.
Args:
train: Selects between the training (True) and validation (False) data.
batch_size: Number of examples per returned batch.
num_epochs: Number of times to read the input data, or 0/None to
train forever.
Returns:
A tuple (images, labels), where:
* images is a float tensor with shape [batch_size, mnist.IMAGE_PIXELS]
in the range [-0.5, 0.5].
* labels is an int32 tensor with shape [batch_size] with the true label,
a number in the range [0, mnist.NUM_CLASSES).
Note that an tf.train.QueueRunner is added to the graph, which
must be run using e.g. tf.train.start_queue_runners().
"""
if not num_epochs: num_epochs = None
filename = os.path.join(FLAGS.train_dir,
TRAIN_FILE if train else VALIDATION_FILE)
with tf.name_scope('input'):
filename_queue = tf.train.string_input_producer(
[filename], num_epochs=num_epochs)
# Even when reading in multiple threads, share the filename
# queue.
food, happiness = read_and_decode(filename_queue)
# Shuffle the examples and collect them into batch_size batches.
# (Internally uses a RandomShuffleQueue.)
# We run this in two threads to avoid being a bottleneck.
foods, happinesses= tf.train.shuffle_batch(
[food, happiness], batch_size=batch_size, num_threads=2,
capacity=1000 + 3 * batch_size,
# Ensures a minimum amount of shuffling of examples.
min_after_dequeue=1000)
return foods, happinesses
def main(_):
with tf.Graph().as_default():
# Input images and labels.
foods, happinesses = inputs(train=True, batch_size=FLAGS.batch_size,
num_epochs=FLAGS.num_epochs)
HIDDEN_UNITS = 4
INPUTS = 1
OUTPUTS = 1
weights_1 = tf.Variable(tf.truncated_normal([INPUTS, HIDDEN_UNITS]))
biases_1 = tf.Variable(tf.zeros([HIDDEN_UNITS]))
layer_1_outputs = tf.nn.sigmoid(tf.matmul(foods, weights_1) + biases_1)
weights_2 = tf.Variable(tf.truncated_normal([HIDDEN_UNITS, OUTPUTS]))
biases_2 = tf.Variable(tf.zeros([OUTPUTS]))
logits = tf.nn.sigmoid(tf.matmul(layer_1_outputs, weights_2) + biases_2)
#loss = tf.reduce_mean(logits)
labels = tf.to_int64(happinesses)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits, name='xentropy')
#loss = tf.reduce_mean(cross_entropy, name='xentropy_mean')
loss = tf.reduce_sum(tf.sub(logits, happinesses))
learning_rate = 0.01
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
sess = tf.Session()
sess.run(init_op)
print('staring iteration', 0)
_, loss = sess.run([train_op, loss])
print(loss)
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--learning_rate',
type=float,
default=0.01,
help='Initial learning rate.'
)
parser.add_argument(
'--num_epochs',
type=int,
default=2,
help='Number of epochs to run trainer.'
)
parser.add_argument(
'--hidden1',
type=int,
default=128,
help='Number of units in hidden layer 1.'
)
parser.add_argument(
'--hidden2',
type=int,
default=32,
help='Number of units in hidden layer 2.'
)
parser.add_argument(
'--batch_size',
type=int,
default=100,
help='Batch size.'
)
parser.add_argument(
'--train_dir',
type=str,
default='.',
help='Directory with the training data.'
)
FLAGS, unparsed = parser.parse_known_args()
tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)