I'm trying to draw mini-batches from cifar10 binary files. When implementing my code shown below (see [source code]), the machine (python 3.6) keeps showing the message (see [console]) ans stops.
Does anyone can tell me what is the problem of my source code?
P.S. I'm new to tensorflow..
[source code]------------------------------------------------------------- import tensorflow as tf import numpy as np import os import matplotlib.pyplot as plt
def _get_image_and_label():
# directory where binary files are stored
data_dir = '/tmp/cifar10_data/cifar-10-batches-bin'
# Step1) make filename Queue
filenames = [os.path.join(data_dir, 'data_batch_%d.bin' % i) for i in range(1, 6)]
filename_queue = tf.train.string_input_producer(filenames)
# Step2) read files
label_bytes = 1 # 2 for CIFAR-100
height = 32
width = 32
depth = 3
image_bytes = height * width * depth
record_bytes = label_bytes + image_bytes
reader = tf.FixedLengthRecordReader(record_bytes=record_bytes)
key, value = reader.read(filename_queue)
# Step3) decode the file in a unit of 1 byte
record_bytes = tf.decode_raw(value, tf.uint8)
# The first bytes represent the label, which we convert from uint8->int32.
label = tf.cast(tf.strided_slice(record_bytes, [0], [label_bytes]), tf.int32)
# The remaining bytes after the label represent the image, which we reshape from [depth * height * width] to [depth, height, width].
depth_major = tf.reshape(tf.strided_slice(record_bytes, [label_bytes], [label_bytes + image_bytes]),
[depth, height, width])
# Convert from [depth, height, width] to [height, width, depth].
uint8image = tf.transpose(depth_major, [1, 2, 0])
# set shape ( image: tf.float32, label: tf.int32 )
image = tf.cast(uint8image, tf.float32)
image.set_shape([height, width, 3])
label.set_shape([1])
# collect batch from the files
# train_x_batch, train_y_batch = tf.train.batch([image, label], batch_size=1)
# return train_x_batch, train_y_batch
return image, label
with tf.Session() as sess: sess.run(tf.global_variables_initializer())
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
image, label = _get_image_and_label()
for i in range(10):
image_batch, lable_batch = tf.train.batch([image, label], batch_size=1)
image_batch_uint8 = tf.cast(image_batch, tf.uint8)
final_image = sess.run(image_batch_uint8)
imgplot = plt.imshow(final_image[0])
coord.request_stop()
coord.join(threads)
sess.close()
[Console]-----------------------------------------------------------------/home/dooseop/anaconda3/bin/python /home/dooseop/pycharm-community-2016.3.3/helpers/pydev/pydevd.py --multiproc --qt-support --client 127.0.0.1 --port 40623 --file /home/dooseop/PycharmProjects/Tensorflow/CIFAR10_main.py warning: Debugger speedups using cython not found. Run '"/home/dooseop/anaconda3/bin/python" "/home/dooseop/pycharm-community-2016.3.3/helpers/pydev/setup_cython.py" build_ext --inplace' to build. Connected to pydev debugger (build 163.15188.4) pydev debugger: process 10992 is connecting
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate (GHz) 1.683 pciBusID 0000:01:00.0 Total memory: 7.92GiB Free memory: 7.17GiB I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0)
Process finished with exit code 137 (interrupted by signal 9: SIGKILL)
