我刚刚安装了tensorflow和keras。我有一个简单的演示，如下所示：
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？
正如Matias在评论中所说，这非常简单……Keras昨天将其API更新为2.0版本。显然，您已经下载了该版本，并且该演示仍然使用“旧” API。他们创建了警告，以使“旧” API在2.0版中仍然可以使用，但是说它将更改，因此请从现在开始使用2.0 API。
使代码适应API 2.0的方法是将所有Dense()层的“ init”参数更改为“ kernel_initializer”，并将fit()函数中的“ nb_epoch”更改为“ epochs” 。
Dense()
fit()
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))
这不应该发出任何警告，它是代码的keras 2.0版本。
