我已经在stackoverflow上看了一些关于这个的其他帖子，但似乎没有任何帮助。我只安装了tensorflow-gpu版本并安装了keras。
我检查了gpu是否被识别，它是：
print(device_lib.list_local_devices())
print('Tensorflow: ', tf.__version__)
出
[name: "/cpu:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 17474054933763055451
, name: "/gpu:0"
device_type: "GPU"
memory_limit: 52848230
locality {
bus_id: 1
}
incarnation: 1990869997540085603
physical_device_desc: "device: 0, name: GeForce GTX 1070, pci bus id:
0000:01:00.0"
]
Tensorflow: 1.3.0
但是当我使用tensorflow后端运行keras模型时，它似乎没有比我使用tensorflow-cpu在没有nvidia的其他计算机上运行它时更快。还看着任务经理，我看到了CPU使用率的飙升。我是新手并尝试全力以赴，但模型/交易的运行速度是否应该更快？如果它使用GPU，我不应该看到CPU使用率的上升吗？
如果有人可以提供帮助，我们将不胜感激。我按照所有步骤安装了tensorflow-gpu，CUDA，cudnn等。
以下是我所拥有的：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 387.92 Driver Version: 387.92 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| 0 GeForce GTX 1070 WDDM | 00000000:01:00.0 On | N/A |
| N/A 49C P8 9W / N/A | 7028MiB / 8192MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
| 0 3740 C C:\Users\Jason\Anaconda3\pythonw.exe N/A |
| 0 3968 C+G ...dows.Cortana_cw5n1h2txyewy\SearchUI.exe N/A |
| 0 4392 C+G C:\Windows\explorer.exe N/A |
| 0 7760 C+G ...0.0_x64__8wekyb3d8bbwe\WinStore.App.exe N/A |
| 0 8976 C+G ...6)\Google\Chrome\Application\chrome.exe N/A |
| 0 9932 C+G ...rosoft Office\root\Office16\WINWORD.EXE N/A |
| 0 12632 C+G Insufficient Permissions N/A |
| 0 14168 C+G Insufficient Permissions N/A |
| 0 14548 C+G ...t_cw5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 15648 C+G Insufficient Permissions N/A |
+-----------------------------------------------------------------------------+
以及何时接受培训：
C:\Program Files\NVIDIA Corporation\NVSMI>nvidia-smi
Fri Oct 13 13:18:23 2017
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 387.92 Driver Version: 387.92 |
|-------------------------------+----------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| 0 GeForce GTX 1070 WDDM | 00000000:01:00.0 On | N/A |
| N/A 51C P2 34W / N/A | 7126MiB / 8192MiB | 15% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
| 0 3740 C C:\Users\Jason\Anaconda3\pythonw.exe N/A |
| 0 3968 C+G ...dows.Cortana_cw5n1h2txyewy\SearchUI.exe N/A |
| 0 4392 C+G C:\Windows\explorer.exe N/A |
| 0 7760 C+G ...0.0_x64__8wekyb3d8bbwe\WinStore.App.exe N/A |
| 0 8976 C+G ...6)\Google\Chrome\Application\chrome.exe N/A |
| 0 9932 C+G ...rosoft Office\root\Office16\WINWORD.EXE N/A |
| 0 12632 C+G Insufficient Permissions N/A |
| 0 14168 C+G Insufficient Permissions N/A |
| 0 14548 C+G ...t_cw5n1h2txyewy\ShellExperienceHost.exe N/A |
| 0 15648 C+G Insufficient Permissions N/A |
+-----------------------------------------------------------------------------+
Anaconda Python 3.6; Tensorflow-gpu 1.3
更新：
这是我正在运行的代码。我试图为时代和批次获得最好的超参数。该模型采用Keras后端tensorflow。
import numpy as np
from keras import models
from keras import layers
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import GridSearchCV
import datetime
# Set random seed
np.random.seed(7)
# Create function returning a compiled network
def create_network(optimizer='adam',activation='relu'):
# Start neural network
network = models.Sequential()
# Add fully connected layer with a ReLU activation function
network.add(layers.Dense(units=dim, activation=activation, input_shape=(dim,)))
# Add fully connected layer with a ReLU activation function
network.add(layers.Dense(units=20, activation=activation))
# Add fully connected layer with a sigmoid activation function
network.add(layers.Dense(units=1, activation='linear'))
# Compile neural network
network.compile(loss='mse', #
optimizer=optimizer, # Optimizer
metrics=['mae']) # Accuracy performance metric
# Return compiled network
return network
# Wrap Keras model so it can be used by scikit-learn
neural_network = KerasRegressor(build_fn=create_network, verbose=2)
# Create hyperparameter space
epochs = [5, 50, 100, 150]
batches = [1, 5, 10, 100]
# Create hyperparameter options
hyperparameters = dict(epochs=epochs, batch_size=batches)
# Time the process
start_time = datetime.datetime.now()
# Create grid search
grid = GridSearchCV(estimator=neural_network, param_grid=hyperparameters)
# Fit grid search
grid_result = grid.fit(X, Y)
# View hyperparameters of best neural network
grid_result.best_params_
# summarize results
print("Best: %f using %s" % (grid_result.best_score_,
grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
print("%f (%f) with: %r" % (mean, stdev, param))
end_time = datetime.datetime.now()
processing_time = end_time - start_time
print(str(processing_time).split('.')[0])
更新2： 我一直在寻找答案，也许还能找到一些东西......也许这就是原因......有人可以验证谁比我更了解吗？
我正在使用scikit学习实现GridSearchCV。即使我使用具有张量流后端的keras，scikit-learn也不使用GPU，那么这可能是原因吗？因此，除非我专门使用keras，否则我不会看到GPU开始使用？