Keras для реализации сверточной нейронной сети

Я только что установил тензор и keras. И у меня есть простая демонстрация:

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))

И у меня есть это предупреждение:

/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' '

Итак, как я могу справиться с этим?

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))

Ответ 1