class ConvWrapper(object): """ An example model to show how to make a wrapper object if you don't want to add a setup() method (optional) and an eval() method (required) to your existing model. The alternative to making a wrapper object is to add an eval() method directly to the existing model and give that model object (either the type or an instance) to grid_search(). """ def __init__(self): super(ConvWrapper, self).__init__() self.net = ConvolutionalNeuralNetwork() self.net.initialize_mnist() def setup(self): # all of this could've gone at the beginning of eval(), # but i wanted that to just be the accuracy evaluation, # so we'll train the model here in setup() self.net.create_model_functions(self.dropout_conv_prob, self.dropout_hidden_prob, self.learning_rate) for i in range(self.epochs): for start, end in zip(range(0, len(self.net.trX), self.batch_size), range(self.batch_size, len(self.net.trX), self.batch_size)): self.net.cost = self.net.train(self.net.trX[start:end], self.net.trY[start:end]) def eval(self): return np.mean(np.argmax(self.net.teY, axis = 1) == self.net.predict(self.net.teX))
class ConvWrapper(object):
""" An example model to show how to make a
wrapper object if you don't want to add
a setup() method (optional) and an eval()
method (required) to your existing model.
The alternative to making a wrapper object
is to add an eval() method directly to the
existing model and give that model object
(either the type or an instance) to grid_search().
"""
def __init__(self):
super(ConvWrapper, self).__init__()
self.net = ConvolutionalNeuralNetwork()
self.net.initialize_mnist()
def setup(self):
# all of this could've gone at the beginning of eval(),
# but i wanted that to just be the accuracy evaluation,
# so we'll train the model here in setup()
self.net.create_model_functions(self.dropout_conv_prob,
self.dropout_hidden_prob,
self.learning_rate)
for i in range(self.epochs):
for start, end in zip(
range(0, len(self.net.trX), self.batch_size),
range(self.batch_size, len(self.net.trX),
self.batch_size)):
self.net.cost = self.net.train(self.net.trX[start:end],
self.net.trY[start:end])
def eval(self):
return np.mean(
np.argmax(self.net.teY, axis=1) == self.net.predict(self.net.teX))
