def test_dsrnn():
l = SupervisedRecurrentNetwork(2, [3] * 2, 1, ['sigmoid'] * 2, 'identity', 'squared')
f = l.function(['inpt', 'target'], 'loss', mode='FAST_COMPILE')
d_loss_wrt_pars = T.grad(l.exprs['loss'], l.parameters.flat)
fprime = l.function(['inpt', 'target'], d_loss_wrt_pars,
mode='FAST_COMPILE')
X = np.random.random((10, 3, 2)).astype(theano.config.floatX)
Z = np.random.random((10, 3, 1)).astype(theano.config.floatX)
f(X, Z)
fprime(X, Z)
def test_srnn():
l = SupervisedRecurrentNetwork(2, 3, 1, 'sigmoid', 'identity', 'squared')
f = l.function(['inpt', 'target'], 'loss', mode='FAST_COMPILE')
d_loss_wrt_pars = T.grad(l.exprs['loss'], l.parameters.flat)
fprime = l.function(['inpt', 'target'],
d_loss_wrt_pars,
mode='FAST_COMPILE')
X = np.random.random((10, 3, 2)).astype(theano.config.floatX)
Z = np.random.random((10, 3, 1)).astype(theano.config.floatX)
f(X, Z)
fprime(X, Z)
def test_pooling_rnn():
l = SupervisedRecurrentNetwork(2, [3], 1, hidden_transfers=['sigmoid'],
pooling='mean', loss='ncac')
l = SupervisedRecurrentNetwork(2, 3, 1, 'sigmoid', 'identity', 'ncac', 'mean')
f = l.function(['inpt', 'target'], 'loss', mode='FAST_COMPILE')
d_loss_wrt_pars = T.grad(l.exprs['loss'], l.parameters.flat)
fprime = l.function(['inpt', 'target'], d_loss_wrt_pars,
mode='FAST_COMPILE')
X = np.random.random((10, 30, 2)).astype(theano.config.floatX)
Z = np.random.random((30, 1)).astype(theano.config.floatX)
f(X, Z)
fprime(X, Z)
def test_pooling_rnn():
l = SupervisedRecurrentNetwork(2, [3],
1,
hidden_transfers=['sigmoid'],
pooling='mean',
loss='ncac')
l = SupervisedRecurrentNetwork(2, 3, 1, 'sigmoid', 'identity', 'ncac',
'mean')
f = l.function(['inpt', 'target'], 'loss', mode='FAST_COMPILE')
d_loss_wrt_pars = T.grad(l.exprs['loss'], l.parameters.flat)
fprime = l.function(['inpt', 'target'],
d_loss_wrt_pars,
mode='FAST_COMPILE')
X = np.random.random((10, 30, 2)).astype(theano.config.floatX)
Z = np.random.random((30, 1)).astype(theano.config.floatX)
f(X, Z)
fprime(X, Z)