Это код тензорного потока, который я написал для тестирования сверточной нейронной сети только с 1 слоем свертки и объединения и только с 1 полностью подключенным слоем из 512 нейронов.
В моем наборе данных всего 2 изображения: https://imgur.com/et1Sn1k и https://imgur.com/ZWxOGgO
Когда я обучаю свою сеть, в окнах появляется всплывающее окно с сообщением «Python остановился» (ss: https://imgur.com/tc5jWlA)
Это мой код:
from scipy.misc import imread
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image as Image
base_image = imread("base_image.jpg")
subject_image = imread("subject_image.jpg")
base_image = np.resize(base_image, [1024, 768, 3])
subject_image = np.resize(subject_image, [1024, 768, 3])
images = []
images.append(base_image)
images.append(subject_image)
# hyper parameters
epochs = 10
batch_size = 2
learning_rate = 0.01
n_classes = 2
import tensorflow as tf
# Model
x = tf.placeholder('float', [2, 1024, 768, 3])
y = tf.placeholder('float', [2])
weights = {
"conv": tf.random_normal([10, 10, 3, 32]),
"fc": tf.random_normal([-1, 512]), #7*7*64
"out": tf.Variable(tf.random_normal([512, n_classes]))
}
conv = tf.nn.conv2d(x, filter=weights['conv'], strides=[1, 1, 1, 1], padding="SAME")
conv = tf.nn.max_pool(value=conv, ksize=[1,2,2,1], strides=[1,2,2,1], padding="SAME")
fc = tf.reshape(conv, shape=[2,-1])
fc = tf.nn.relu(tf.matmul(fc, weights['fc']))
output = tf.matmul(fc, weights['out'])
loss = tf.reduce_mean((output - y)**2)
train = tf.train.AdamOptimizer(learning_rate).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
for i in range(epochs):
sess.run(train, feed_dict={x: images, y: [0, 1]})
print(i)
Выход:
2017-07-09 02:29:43.688699: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-07-09 02:29:43.689131: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\platform\cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-07-09 02:29:43.689504: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-07-09 02:29:43.689998: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-07-09 02:29:43.690380: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-07-09 02:29:43.690646: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-07-09 02:29:43.691117: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-07-09 02:29:43.691436: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\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.
2017-07-09 02:29:44.197766: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:940] Found device 0 with properties:
name: GeForce GTX 960M
major: 5 minor: 0 memoryClockRate (GHz) 1.176
pciBusID 0000:01:00.0
Total memory: 4.00GiB
Free memory: 3.35GiB
2017-07-09 02:29:44.198207: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:961] DMA: 0
2017-07-09 02:29:44.198391: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: Y
2017-07-09 02:29:44.198643: I c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 960M, pci bus id: 0000:01:00.0)
2017-07-09 02:29:44.881448: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\framework\op_kernel.cc:1158] Invalid argument: Dimension -1 must be >= 0
2017-07-09 02:29:44.881875: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\framework\op_kernel.cc:1158] Invalid argument: Dimension -1 must be >= 0
[[Node: random_normal_1/RandomStandardNormal = RandomStandardNormal[T=DT_INT32, dtype=DT_FLOAT, seed=0, seed2=0, _device="/job:localhost/replica:0/task:0/gpu:0"](random_normal_1/shape)]]
2017-07-09 02:29:44.882604: W c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\framework\op_kernel.cc:1158] Invalid argument: Dimension -1 must be >= 0
[[Node: random_normal_1/RandomStandardNormal = RandomStandardNormal[T=DT_INT32, dtype=DT_FLOAT, seed=0, seed2=0, _device="/job:localhost/replica:0/task:0/gpu:0"](random_normal_1/shape)]]
2017-07-09 02:29:45.362917: E c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\stream_executor\cuda\cuda_dnn.cc:352] Loaded runtime CuDNN library: 6021 (compatibility version 6000) but source was compiled with 5105 (compatibility version 5100). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
2017-07-09 02:29:45.364154: F c:\tf_jenkins\home\workspace\release-win\m\windows-gpu\py\35\tensorflow\core\kernels\conv_ops.cc:671] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)
[Finished in 22.7s with exit code 3221226505]
[shell_cmd: python -u "E:\workspace_py\convolotion_neural_net.py"]
[dir: E:\workspace_py]
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Технические характеристики моего компьютера (Lenovo Y50): Nvidia GTX 960m 4 ГБ памяти, Intel I7 4-го поколения, 8 ГБ оперативной памяти
Python 3.5 + Tensorflow с графическим процессором
