def fit(self, X, Y):
X4D = get4D(X)
Y4D = get4D(Y)
data_layer = self.dataset_adaptor.fit_transform(X4D,Y4D)
net = self.get_net(data_layer)
self.trainer.init_trainer(net,X4D, Y4D)
self.trainer.solve()
return self.trainer
def set_train_data(self, X, Y):
# TODO find better way to assign data, support batches
X4D = get4D(X)
Y4D = get4D(Y)
sh = Y4D.shape
self.net.blobs['label'].reshape (sh[0], sh[1], sh[2], sh[3])
self.net.blobs['data'].data[...] = X4D
self.net.blobs['label'].data[...] = Y4D
def fit(self, X, Y):
X4D = get4D(X)
Y4D = get4D(Y)
data_layer = self.dataset_adaptor.fit_transform(X4D,Y4D)
net = self.get_net(data_layer)
self.trainer.init_trainer(net,X4D, Y4D)
self.trainer.solve()
return self.trainer
def set_train_data(self, X, Y):
# TODO find better way to assign data, support batches
# self.net.set_input_arrays(X,Y)
X4D = get4D(X)
Y4D = get4D(Y)
sh = Y4D.shape
self.solver.net.blobs['label'].reshape(sh[0], sh[1], sh[2], sh[3])
self.solver.net.blobs['data'].data[...] = X4D
self.solver.net.blobs['label'].data[...] = Y4D
def predict(self, X):
X4D = get4D(X)
self.solver.set_test_data (X4D)
self.solver.test_nets[0].forward()
prob = self.solver.test_nets[0].blobs['output'].data
print prob.shape
pred = prob[:, 0] < prob[:, 1]
return pred
def predict(self, x_test):
X4D = get4D(x_test)
self.set_test_data(X4D)
self.solver.test_nets[0].forward()
def set_test_data(self, X):
X4D = get4D(X)
sh = X4D.shape
self.solver.test_nets[0].blobs['data'].reshape(sh[0], sh[1], sh[2],
sh[3])
self.solver.test_nets[0].blobs['data'].data[...] = X4D
def predict(self, x_test):
X4D = get4D(x_test)
self.set_test_data (X4D)
self.solver.test_nets[0].forward()
def set_test_data(self, X):
X4D= get4D(X)
sh = X4D.shape
self.solver.test_nets[0].blobs['data'].reshape (sh[0], sh[1], sh[2], sh[3])
self.solver.test_nets[0].blobs['data'].data[...] = X4D
