我刚刚为我的桌面安装了新的 GTX 1070 Founders Addition，我正在尝试在这个新 GPU 上运行 tensorflow。我正在使用 tensorflow.device() 在我的 GPU 上运行 tensorflow，但似乎没有发生这种情况。相反，它使用的是 cpu，几乎所有我的系统都是 8GB 的 ram。这是我的代码:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import matplotlib.image as mpimg
import math
print("\n\n")
# os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#
with tf.device("/gpu:0"):
# Helper Function To Print Percentage
def showPercent(num, den, roundAmount):
print( str( round((num / den) * roundAmount )/roundAmount ) + " % ", end="\r")
# Defince The Number Of Images To Get
def getFile(dir, getEveryNthLine):
allFiles = list(os.listdir(dir))
fileNameList = []
numOfFiles = len(allFiles)
i = 0
for fichier in allFiles:
if(i % 100 == 0):
showPercent(i, numOfFiles, 100)
if(i % getEveryNthLine == 0):
if(fichier.endswith(".png")):
fileNameList.append(dir + "/" + fichier[0:-4])
i += 1
return fileNameList
# Other Helper Functions
def init_weights(shape):
init_random_dist = tf.truncated_normal(shape, stddev=0.1, dtype=tf.float16)
return tf.Variable(init_random_dist)
def init_bias(shape):
init_bias_vals = tf.constant(0.1, shape=shape, dtype=tf.float16)
return tf.Variable(init_bias_vals)
def conv2d(x, W):
# x --> [batch, H, W, Channels]
# W --> [filter H, filter W, Channels IN, Channels Out]
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
def max_pool_2by2(x):
# x --> [batch, H, W, Channels]
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
def convolutional_layer(input_x, shape):
W = init_weights(shape)
b = init_bias([ shape[3] ])
return tf.nn.relu(conv2d(input_x, W) + b)
def normal_full_layer(input_layer, size):
input_size = int(input_layer.get_shape()[1])
W = init_weights([input_size, size])
b = init_bias([size])
return tf.matmul(input_layer, W) + b
print("Getting Images")
fileNameList = getFile("F:\cartoonset10k-small", 1000)
print("\nloaded " + str(len(fileNameList)) + " files")
print("Defining Placeholders")
x_ph = tf.placeholder(tf.float16, shape=[None, 400, 400, 4])
y_ph = tf.placeholder(tf.float16, shape=[None])
print("Defining Conv and Pool layer 1")
convo_1 = convolutional_layer(x_ph, shape=[5, 5, 4, 32])
convo_1_pooling = max_pool_2by2(convo_1)
print("Defining Conv and Pool layer 2")
convo_2 = convolutional_layer(convo_1_pooling, shape=[5, 5, 32, 64])
convo_2_pooling = max_pool_2by2(convo_2)
print("Define Flat later and a Full layer")
convo_2_flat = tf.reshape(convo_2_pooling, [-1, 400 * 400 * 64])
full_layer_one = tf.nn.relu(normal_full_layer(convo_2_flat, 1024))
y_pred = full_layer_one # Add Dropout Later
def getLabels(filePath):
df = []
with open(filePath, "r") as file:
for line in list(file):
tempList = line.replace("\n", "").replace('"', "").replace(" ", "").split(",")
df.append({
"attr": tempList[0],
"value":int(tempList[1]),
"maxValue":int(tempList[2])
})
return df
print("\nSplitting And Formating X, and Y Data")
x_data = []
y_data = []
numOfFiles = len(fileNameList)
i = 0
for file in fileNameList:
if i % 10 == 0:
showPercent(i, numOfFiles, 100)
x_data.append(mpimg.imread(file + ".png"))
y_data.append(pd.DataFrame(getLabels(file + ".csv"))["value"][0])
i += 1
print("\nConveting x_data to list")
i = 0
for indx in range(len(x_data)):
if i % 10 == 0:
showPercent(i, numOfFiles, 100)
x_data[indx] = x_data[indx].tolist()
i += 1
print("\n\nPerforming Train Test Split")
train_x, test_x, train_y, test_y = train_test_split(x_data, y_data, test_size=0.2)
print("Defining Loss And Optimizer")
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=y_ph,
logits=y_pred
)
)
optimizer = tf.train.AdadeltaOptimizer(learning_rate=0.001)
train = optimizer.minimize(cross_entropy)
print("Define Var Init")
init = tf.global_variables_initializer()
with tf.Session() as sess:
print("Checkpoint Before Initializer")
sess.run(init)
print("Checkpoint After Initializer")
batch_size = 8
steps = 1
i = 0
for i in range(steps):
if i % 10:
print(i / 100, end="\r")
batch_x = []
i = 0
for i in np.random.randint(len(train_x), size=batch_size):
showPercent(i, len(train_x), 100)
train_x[i]
batch_x = [train_x[i] for i in np.random.randint(len(train_x), size=batch_size) ]
batch_y = [train_y[i] for i in np.random.randint(len(train_y), size=batch_size) ]
print(sess.run(train, {
x_ph:train_x,
y_ph:train_y,
}))
如果你运行它，当我运行 global_variable_initializer() 时，这个程序似乎退出了。它还在终端打印: 20971520000 的分配超过了系统内存的 10%。查看我的任务管理器时，我看到了这个:
The program is using a lot of my CPU.
The program is using a lot of my Memory.
The program is using none of my GPU.
我不明白为什么会这样。我使用的是anaconda环境，安装了tensorflow-gpu。我非常感谢任何人的建议和帮助。
此外，当我运行它时，程序在 global_variable_initializer() 之后停止。我不确定这是否与上述问题有关。
Tensorflow 是 1.12 版。 CUDA 版本为 10.0.130。
帮助将不胜感激。
请您参考如下方法:
尝试用这个简单的例子比较时间(GPU 与 CPU):
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
def create_model():
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
return model
epoch = 3
print('GPU:')
with tf.device('/gpu:0'):
model = create_model()
model.fit(x_train, y_train, epochs=epoch)
print('\nCPU:')
with tf.device('/cpu:0'):
model = create_model()
model.fit(x_train, y_train, epochs=epoch)
