def __init__(self,input_size,output_size,layer_sizes=[500],learning_rate=0.01,
L1_reg=0.00, L2_reg=0.0001,
n_epochs=1000,batch_size=20):
self.learning_rate = learning_rate
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.n_epochs = n_epochs
self.batch_size=batch_size
self.x = T.matrix('x')
self.mlp = MultiMLP(input = self.x, n_in = input_size, \
layer_sizes = layer_sizes, n_out = output_size)
class MultiMLPClassifier(object) :
def __init__(self,input_size,output_size,layer_sizes=[500],learning_rate=0.01,
L1_reg=0.00, L2_reg=0.0001,
n_epochs=1000,batch_size=20):
self.learning_rate = learning_rate
self.L1_reg = L1_reg
self.L2_reg = L2_reg
self.n_epochs = n_epochs
self.batch_size=batch_size
self.x = T.matrix('x')
self.mlp = MultiMLP(input = self.x, n_in = input_size, \
layer_sizes = layer_sizes, n_out = output_size)
def fit(self,X,y):
X_train, X_valid, y_train, y_valid = self.splitData(X,y)
train_model(self.mlp,self.x, X_train,X_valid,y_train,
y_valid, self.L1_reg,
self.L2_reg, self.learning_rate,
self.n_epochs, self.batch_size)
#
def predict(self,X, y = None):
fit_model = theano.function(
inputs=[],
outputs=self.mlp.predict_class,
givens={self.x : X}
)
if(y != None):
validate_model = theano.function(inputs=[],
outputs=self.mlp.errors(y),
givens={
self.x: X,
y: y})
print((' validation error %f %%') % (validate_model() * 100.))
return fit_model()
def predict_proba(self,X, y = None):
fit_model = theano.function(
inputs=[],
outputs=self.mlp.predict_proba,
givens={self.x : X}
)
if(y != None):
validate_model = theano.function(inputs=[],
outputs=self.mlp.errors(y),
givens={
self.x: X,
y: y})
print((' validation error %f %%') % (validate_model() * 100.))
return fit_model()
def getSharedInstance(self,array):
return theano.shared(np.asarray(array, dtype=theano.config.floatX))
def splitData(self,X,y):
r = np.random.rand(X.shape[0])
X_train = self.getSharedInstance(X[r<0.9])
X_valid = self.getSharedInstance(X[r>=0.9])
y_train = T.as_tensor_variable(y[r<0.9])
y_valid = T.as_tensor_variable(y[r>=0.9])
# First 90% train, Lirst 10% validation
return X_train, \
X_valid, \
T.cast(y_train,'int32'), \
T.cast(y_valid,'int32')
