def construct_functions(free_vars,model,py_x,loss,learning_rate):
input_vars=free_vars.get_vars()
params=model.get_params()
update=deep.compute_updates(loss, params, learning_rate)
train = theano.function(inputs=input_vars,
outputs=loss, updates=update,
allow_input_downcast=True)
y_pred = T.argmax(py_x, axis=1)
prob_dist=theano.function(inputs=[free_vars.X], outputs=py_x,
allow_input_downcast=True)
test=theano.function(inputs=[free_vars.X], outputs=y_pred,
allow_input_downcast=True)
return train,test,prob_dist
def create_ae_fun(free_vars,model,rand,hyper_params):
learning_rate=hyper_params['learning_rate']
corruption_level=hyper_params['corruption_level']
tilde_x = get_corrupted_input(free_vars,corruption_level,rand)
x=free_vars.X
y = get_hidden_values(model,tilde_x)
z = get_reconstructed_input(model,y)
loss= tools.get_l2_loss(x,z)
input_vars=free_vars.get_vars()
params=model.get_params()
updates=deep.compute_updates(loss, params, learning_rate)
train = theano.function([x],loss,updates=updates)
test = theano.function([x],y)
get_image = theano.function([x],z)
return train,test,get_image
