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<p>当我试图用SineRELU或PELU替换工作代码中的LeakyRELU或relu时。我一直收到这个错误:</p>
<blockquote>
<p>ValueError: Unknown activation function:PELU</p>
</blockquote>
<p>我正在使用<code>keras.contrib</code>。我附上了示例代码。我已经试过几次了。任何实现这一点的方法都将得到赞赏。在</p>
<pre><code>from keras.layers import Dense, Input, LeakyReLU, UpSampling2D, Conv2D, Concatenate
from keras_contrib.layers import SineReLU
from keras.models import Model,load_model, Sequential
from keras.optimizers import Adam
# Recommended method; requires knowledge of the underlying architecture of the model
from keras_contrib.layers import PELU
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# 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, activation='PELU'))
model.add(Dense(8, activation='PELU'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
# Create your first MLP in Keras
from keras.models import Sequential
from keras.layers import Dense
import numpy
# fix random seed for reproducibility
numpy.random.seed(7)
# 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, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# Compile model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# Fit the model
model.fit(X, Y, epochs=150, batch_size=10)
# evaluate the model
scores = model.evaluate(X, Y)
print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))
</code></pre>