def test_binary(multiple_out=False, n_epochs=1000, optimizer='bfgs'):
""" Test RNN with binary outputs. """
n_hidden = 10
n_in = 5
if multiple_out:
n_out = 2
else:
n_out = 1
n_steps = 10
n_seq = 10 # per batch
n_batches = 50
np.random.seed(0)
# simple lag test
seq = np.random.randn(n_steps, n_seq * n_batches, n_in)
targets = np.zeros((n_steps, n_seq * n_batches, n_out))
# whether lag 1 (dim 3) is greater than lag 2 (dim 0)
targets[2:, :, 0] = np.cast[np.int](seq[1:-1, :, 3] > seq[:-2, :, 0])
# pdb.set_trace()
if multiple_out:
# whether product of lag 1 (dim 4) and lag 1 (dim 2)
# is less than lag 2 (dim 0)
targets[2:, :, 1] = np.cast[np.int](
(seq[1:-1, :, 4] * seq[1:-1, :, 2]) > seq[:-2, :, 0])
model = MetaRNN_minibatch(n_in=n_in, n_hidden=n_hidden, n_out=n_out,
learning_rate=0.005, learning_rate_decay=0.999,
n_epochs=n_epochs, batch_size=n_seq, activation='tanh',
output_type='binary')
model.fit(seq, targets, validate_every=100, compute_zero_one=True,
optimizer=optimizer)
seqs = xrange(10)
def test_binary(multiple_out=False, n_epochs=1000, optimizer='bfgs'):
""" Test RNN with binary outputs. """
n_hidden = 10
n_in = 5
if multiple_out:
n_out = 2
else:
n_out = 1
n_steps = 10
n_seq = 10 # per batch
n_batches = 50
np.random.seed(0)
# simple lag test
seq = np.random.randn(n_steps, n_seq * n_batches, n_in)
targets = np.zeros((n_steps, n_seq * n_batches, n_out))
# whether lag 1 (dim 3) is greater than lag 2 (dim 0)
targets[2:, :, 0] = np.cast[np.int](seq[1:-1, :, 3] > seq[:-2, :, 0])
# pdb.set_trace()
if multiple_out:
# whether product of lag 1 (dim 4) and lag 1 (dim 2)
# is less than lag 2 (dim 0)
targets[2:, :, 1] = np.cast[np.int](
(seq[1:-1, :, 4] * seq[1:-1, :, 2]) > seq[:-2, :, 0])
model = MetaRNN_minibatch(n_in=n_in,
n_hidden=n_hidden,
n_out=n_out,
learning_rate=0.005,
learning_rate_decay=0.999,
n_epochs=n_epochs,
batch_size=n_seq,
activation='tanh',
output_type='binary')
model.fit(seq,
targets,
validate_every=100,
compute_zero_one=True,
optimizer=optimizer)
seqs = xrange(10)
def train_RNN_minibatch(dataset=None, n_hidden=1500, steps=30, batch_size=30, n_epochs=100, optimizer='sgd'):
# train_set_x, train_set_y = dataset.get_batch_design(0, steps, dataset.phase2['train'], type='numpy_dense', isInt=True)
# valid_set_x, valid_set_y = dataset.get_batch_design(0, steps, dataset.phase2['valid'], type='numpy_dense', isInt=True)
train_set_x, train_set_y = dataset.phase2['train']['x'], dataset.phase2['train']['y']
valid_set_x, valid_set_y = dataset.phase2['valid']['x'], dataset.phase2['valid']['y']
test_set_x, test_set_y = dataset.phase2['test']['x'], dataset.phase2['test']['y']
# train_set_y = np.asarray(valid_set_x, dtype=np.int32)
# valid_set_x, valid_set_y = dataset.phase2['valid']['x'], dataset.phase2['valid']['y']
# test_set_x, test_set_y = dataset.get_batch_design(0, batch_size, dataset.phase2['test'], type='numpy_dense')
print train_set_x.shape[1]
model = MetaRNN_minibatch(n_in=train_set_x.shape[1], n_hidden=n_hidden, n_out=1,
learning_rate=0.01, learning_rate_decay=0.99,
n_epochs=n_epochs, activation='tanh', L2_reg=0.01,
batch_size=batch_size, output_type='binary'
)
model.fit([train_set_x], [train_set_y], X_test=[valid_set_x], Y_test=[valid_set_y], validate_every=1, compute_zero_one=True, optimizer=optimizer)
pdb.set_trace()
return model
def train_RNN_minibatch(dataset=None,
n_hidden=1500,
steps=30,
batch_size=30,
n_epochs=100,
optimizer='sgd'):
# train_set_x, train_set_y = dataset.get_batch_design(0, steps, dataset.phase2['train'], type='numpy_dense', isInt=True)
# valid_set_x, valid_set_y = dataset.get_batch_design(0, steps, dataset.phase2['valid'], type='numpy_dense', isInt=True)
train_set_x, train_set_y = dataset.phase2['train']['x'], dataset.phase2[
'train']['y']
valid_set_x, valid_set_y = dataset.phase2['valid']['x'], dataset.phase2[
'valid']['y']
test_set_x, test_set_y = dataset.phase2['test']['x'], dataset.phase2[
'test']['y']
# train_set_y = np.asarray(valid_set_x, dtype=np.int32)
# valid_set_x, valid_set_y = dataset.phase2['valid']['x'], dataset.phase2['valid']['y']
# test_set_x, test_set_y = dataset.get_batch_design(0, batch_size, dataset.phase2['test'], type='numpy_dense')
print train_set_x.shape[1]
model = MetaRNN_minibatch(n_in=train_set_x.shape[1],
n_hidden=n_hidden,
n_out=1,
learning_rate=0.01,
learning_rate_decay=0.99,
n_epochs=n_epochs,
activation='tanh',
L2_reg=0.01,
batch_size=batch_size,
output_type='binary')
model.fit([train_set_x], [train_set_y],
X_test=[valid_set_x],
Y_test=[valid_set_y],
validate_every=1,
compute_zero_one=True,
optimizer=optimizer)
pdb.set_trace()
return model
