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用于实现卷积神经网络的Keras

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我刚刚安装了tensorflow和keras . 我有如下简单的演示：

from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, init='uniform', activation='relu'))
model.add(Dense(8, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, nb_epoch=10, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

我有这个警告：

/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py:86: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(12, activation="relu", kernel_initializer="uniform", input_dim=8)` '` call to the Keras 2 API: ' + signature)
/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py:86: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(8, activation="relu", kernel_initializer="uniform")` '` call to the Keras 2 API: ' + signature)
/usr/local/lib/python2.7/dist-packages/keras/legacy/interfaces.py:86: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(1, activation="sigmoid", kernel_initializer="uniform")` '` call to the Keras 2 API: ' + signature)
/usr/local/lib/python2.7/dist-packages/keras/models.py:826: UserWarning: The `nb_epoch` argument in `fit` has been renamed `epochs`. warnings.warn('The `nb_epoch` argument in `fit` '

那么，我该如何处理呢？

python deep-learning keras

2 回答

  • 28

    正如Matias在评论中所说，这非常简单...... Keras昨天将他们的API更新为2.0版本 . 显然你已经下载了该版本，并且该演示仍然使用“旧”API . 他们已经创建了警告，以便“旧”API仍然可以在2.0版本中运行，但是它会改变，所以请从现在开始使用2.0 API .

    使代码适应API 2.0的方法是将"init"参数的"init"参数更改为"kernel_initializer"，将"nb_epoch"更改为"epochs"中的"nb_epoch" .

    from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load pima indians dataset
dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",")
# split into input (X) and output (Y) variables
X = dataset[:,0:8]
Y = dataset[:,8]
# create model
model = Sequential()
model.add(Dense(12, input_dim=8, kernel_initializer ='uniform', activation='relu'))
model.add(Dense(8, kernel_initializer ='uniform', activation='relu'))
model.add(Dense(1, kernel_initializer ='uniform', activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=10, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

    这不应该抛出任何警告，这是代码的keras 2.0版本 .

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  • 0

    我只想添加Nassim Ben's答案，通常Keras会提示并提出如下建议：

    Hyperparameters_optimisation_youtube.py:36: UserWarning: Update your
`Dense` call to the Keras 2 API: `Dense(activation="sigmoid", units=2)`
  model.add(Dense(output_dim=2, activation='sigmoid'))

    因为我输入 output_dim=... 而不是 units=...

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