Question

这是我的代码：

java.lang.SecurityException: Permission denied (missing INTERNET permission?)

当我使用cpu模式时，结果如下：

%tensorflow_version 2.x
import tensorflow as tf
import timeit

dataset = tf.keras.datasets.mnist

(train_images, train_labels), (test_images, test_labels) = dataset.load_data()


train_images = train_images/255.0

test_images = test_images/255.0

def gpu(): 
  with tf.device("/cpu:0"):
  # with tf.device("/device:GPU:0"):
    network = tf.keras.models.Sequential([
        tf.keras.layers.Flatten(input_shape = (28,28)),
        tf.keras.layers.Dense(128,activation = 'relu'),
        tf.keras.layers.Dropout(0.2),
        tf.keras.layers.Dense(10,activation = 'softmax')

    ])

    network.compile(optimizer=tf.keras.optimizers.Adam(0.0001),
                    loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                    metrics=['accuracy'])

    # network.evaluate(test_images,test_labels,verbose=2)

    callbacks = [
        tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_accuracy')
        # tf.keras.callbacks.TensorBoard(log_dir='./log')
    ]
    history = network.fit(train_images,train_labels,batch_size=32,epochs = 1,validation_data=(test_images,test_labels),validation_batch_size=32,validation_freq=1,callbacks=callbacks)
    network.evaluate(test_images,  test_labels, verbose=2)

gpu_time = timeit.timeit('gpu()', number=1, setup="from __main__ import gpu")
print(gpu_time)

当我更改为GPU模式时，结果如下：

1875/1875 [==============================] - 3s 2ms/step - loss: 0.6937 - accuracy: 0.8148 - val_loss: 0.3347 - val_accuracy: 0.9113
313/313 - 0s - loss: 0.3347 - accuracy: 0.9113
3.9582922519998647

我想知道为什么在我的代码中使用gpu的速度更慢。当我使用colab提供的代码时，GPU的速度大约是CPU的10倍。我确信GPU可以在我的环境中正常工作。

使用CoLab时GPU加速失败

0 个答案: