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I'm trying to run the followin code on google colab:

import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from keras import models
from keras import layers
from keras.datasets import boston_housing
from sklearn.model_selection import KFold
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import GridSearchCV
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.metrics import mean_squared_error

sns.set()

(train_data,train_targets),(test_data,test_targets)=boston_housing.load_data()

mean=np.mean(train_data)
std=np.std(train_data)

train_data_norm=(train_data-mean)/std
test_data_norm=(test_data-mean)/std

def build_model():
    model=models.Sequential()
    model.add(layers.Dense(64,activation="relu",
                          input_shape=(train_data_norm.shape[1],)))
    model.add(layers.Dense(64,activation="relu"))
    model.add(layers.Dense(1))
    model.compile(optimizer='rmsprop',loss="mse",metrics=["mae"])
    return model 


model=KerasRegressor(build_fn=build_model,epochs=30,verbose=0)
param_grid = {"epochs":range(1,11)}

kf1=KFold(n_splits=5, random_state=None, shuffle=False) #n_splits = number of folds
kf2=KFold(n_splits=5, random_state=1, shuffle=True)
ss = ShuffleSplit(n_splits=5,test_size=0.20,random_state=1)

grid_model_KFFalse=GridSearchCV(model,param_grid,cv=kf1,n_jobs=-1,scoring='neg_mean_squared_error')
grid_model_KFTrue=GridSearchCV(model,param_grid,cv=kf2,n_jobs=-1,scoring='neg_mean_squared_error')
grid_model_SS=GridSearchCV(model,param_grid,cv=ss,n_jobs=-1,scoring='neg_mean_squared_error')

listKFFalse=[]
listKFTrue=[]
listSS=[]

for i in range(1,21):
  grid_model_KFFalse.fit(train_data, train_targets)
  grid_model_KFTrue.fit(train_data, train_targets)
  grid_model_SS.fit(train_data, train_targets)
  mseKFFalse=mean_squared_error(grid_model_KFFalse.predict(test_data),test_targets)
  mseKFTrue=mean_squared_error(grid_model_KFTrue.predict(test_data),test_targets)
  mseSS=mean_squared_error(grid_model_SS.predict(test_data),test_targets)
  listKFFalse=np.append(listKFFalse,[mseKFFalse])
  listKFTrue=np.append(listKFTrue,[mseKFTrue])
  listSS=np.append(listSS,[mseSS])

You can check it here.

I've done on my laptop 1 run on a jupyter notebook, instead of the 21, and the part where it's slowest is in the 'for' block, but that's to be expected.

The code compiles without error, since I've already managed to run it for 5 cycles/simulations without problems.

However, it's taking a very long time... I wonder why it is. Isn't google colab supposed to use GPU(or TPU) when learning neural networks with keras? I've already changed the Runtime type to GPU or TPU, and it's still very slow.

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2 回答 2

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从常见问题解答

似乎好得令人难以置信。有什么限制？

Colab 资源没有保证也不是无限的，使用限制有时会波动。这是 Colab 能够免费提供资源所必需的。有关更多详细信息，请参阅资源限制

于 2021-03-02T13:35:43.050 回答
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关于 TPU 的使用，如果您参考Colab中的 TPU，您会看到一些用于连接到 TPU 的代码片段：

tpu = tf.distribute.cluster_resolver.TPUClusterResolver() 
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)

在提供的代码片段/colab 中，没有使用 TPU，这就是为什么您看不到性能差异的原因。如果您想使用 TPU，则需要从基础迁移keras到并使用分发策略。tf.keras

于 2021-03-08T21:52:19.297 回答