def run_lstm():
del_shared()
n_in = X.shape[-1]
n_hid = 20
n_out = y.shape[-1]
random_state = np.random.RandomState(42)
h_init = np.zeros((minibatch_size, 2 * n_hid)).astype("float32")
h0 = tensor.fmatrix()
l1 = lstm_fork([X_sym], [n_in], n_hid, name="l1",
random_state=random_state)
def step(in_t, h_tm1):
h_t = lstm(in_t, h_tm1, n_hid, name="rec", random_state=random_state)
return h_t
h, _ = theano.scan(step, sequences=[l1], outputs_info=[h0])
h_o = slice_state(h, n_hid)
pred = linear([h_o], [n_hid], n_out, name="l2", random_state=random_state)
cost = ((y_sym - pred) ** 2).sum()
params = list(get_params().values())
grads = tensor.grad(cost, params)
learning_rate = 0.000000000001
opt = sgd(params, learning_rate)
updates = opt.updates(params, grads)
f = theano.function([X_sym, y_sym, h0], [cost, h], updates=updates,
mode="FAST_COMPILE")
f(X, y, h_init)
def test_feedforward_theano_mix():
minibatch_size = 100
random_state = np.random.RandomState(1999)
graph = OrderedDict()
X_sym, y_sym = add_datasets_to_graph([X, y], ["X", "y"], graph)
l1_o = linear_layer([X_sym], graph, 'l1', proj_dim=20,
random_state=random_state)
l1_o = .999 * l1_o
y_pred = softmax_layer([l1_o], graph, 'pred', n_classes,
random_state=random_state)
cost = categorical_crossentropy(y_pred, y_sym).mean()
params, grads = get_params_and_grads(graph, cost)
learning_rate = 0.001
opt = sgd(params)
updates = opt.updates(params, grads, learning_rate)
fit_function = theano.function([X_sym, y_sym], [cost], updates=updates,
mode="FAST_COMPILE")
cost_function = theano.function([X_sym, y_sym], [cost],
mode="FAST_COMPILE")
checkpoint_dict = {}
train_indices = np.arange(len(X))
valid_indices = np.arange(len(X))
early_stopping_trainer(fit_function, cost_function, checkpoint_dict, [X, y],
minibatch_size,
train_indices, valid_indices,
fit_function_output_names=["cost"],
cost_function_output_name="valid_cost",
n_epochs=1)
def test_tanh_rnn():
# random state so script is deterministic
random_state = np.random.RandomState(1999)
# home of the computational graph
graph = OrderedDict()
# number of hidden features
n_hid = 10
# number of output_features = input_features
n_out = X.shape[-1]
# input (where first dimension is time)
datasets_list = [X, X_mask, y, y_mask]
names_list = ["X", "X_mask", "y", "y_mask"]
test_values_list = [X, X_mask, y, y_mask]
X_sym, X_mask_sym, y_sym, y_mask_sym = add_datasets_to_graph(
datasets_list, names_list, graph, list_of_test_values=test_values_list)
# Setup weights
l1 = linear_layer([X_sym], graph, 'l1_proj', n_hid, random_state)
h = tanh_recurrent_layer([l1], X_mask_sym, n_hid, graph, 'l1_rec',
random_state)
# linear output activation
y_hat = linear_layer([h], graph, 'l2_proj', n_out, random_state)
# error between output and target
cost = squared_error(y_hat, y_sym)
cost = masked_cost(cost, y_mask_sym).mean()
# Parameters of the model
params, grads = get_params_and_grads(graph, cost)
# Use stochastic gradient descent to optimize
opt = sgd(params)
learning_rate = 0.001
updates = opt.updates(params, grads, learning_rate)
fit_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym],
[cost],
updates=updates,
mode="FAST_COMPILE")
cost_function = theano.function([X_sym, X_mask_sym, y_sym, y_mask_sym],
[cost],
mode="FAST_COMPILE")
checkpoint_dict = {}
train_indices = np.arange(X.shape[1])
valid_indices = np.arange(X.shape[1])
early_stopping_trainer(fit_function,
cost_function,
checkpoint_dict, [X, y],
minibatch_size,
train_indices,
valid_indices,
fit_function_output_names=["cost"],
cost_function_output_name="valid_cost",
n_epochs=1)
def test_fixed_projection_layer():
random_state = np.random.RandomState(1999)
rand_projection = random_state.randn(64, 12)
graph = OrderedDict()
X_sym = add_datasets_to_graph([X], ["X"], graph)
out = fixed_projection_layer([X_sym], rand_projection, graph, 'proj')
out2 = fixed_projection_layer([X_sym],
rand_projection,
graph,
'proj',
pre=rand_projection[:, 0])
out3 = fixed_projection_layer([X_sym],
rand_projection,
graph,
'proj',
post=rand_projection[0])
final = linear_layer([out2],
graph,
'linear',
17,
random_state=random_state)
# Test that it compiles with and without bias
f = theano.function([X_sym], [out, out2, out3, final], mode="FAST_COMPILE")
# Test updates
params, grads = get_params_and_grads(graph, final.mean())
opt = sgd(params)
updates = opt.updates(params, grads, .1)
f2 = theano.function([X_sym], [out2, final], updates=updates)
ret = f(np.ones_like(X))[0]
assert ret.shape[1] != X.shape[1]
ret2 = f(np.ones_like(X))[1]
assert ret.shape[1] != X.shape[1]
out1, final1 = f2(X)
out2, final2 = f2(X)
# Make sure fixed basis is unchanged
assert_almost_equal(out1, out2)
# Make sure linear layer is updated
assert_raises(AssertionError, assert_almost_equal, final1, final2)
def test_feedforward_theano_mix():
del_shared()
minibatch_size = 100
random_state = np.random.RandomState(1999)
X_sym = tensor.fmatrix()
y_sym = tensor.fmatrix()
l1_o = linear([X_sym], [X.shape[1]],
proj_dim=20,
name='l1',
random_state=random_state)
l1_o = .999 * l1_o
y_pred = softmax([l1_o], [20],
proj_dim=n_classes,
name='out',
random_state=random_state)
cost = categorical_crossentropy(y_pred, y_sym).mean()
params = list(get_params().values())
grads = theano.grad(cost, params)
learning_rate = 0.001
opt = sgd(params, learning_rate)
updates = opt.updates(params, grads)
fit_function = theano.function([X_sym, y_sym], [cost],
updates=updates,
mode="FAST_COMPILE")
cost_function = theano.function([X_sym, y_sym], [cost],
mode="FAST_COMPILE")
train_itr = minibatch_iterator([X, y], minibatch_size, axis=0)
valid_itr = minibatch_iterator([X, y], minibatch_size, axis=0)
X_train, y_train = next(train_itr)
X_valid, y_valid = next(valid_itr)
fit_function(X_train, y_train)
cost_function(X_valid, y_valid)
def test_fixed_projection():
random_state = np.random.RandomState(1999)
rand_projection = random_state.randn(64, 12)
rand_dim = rand_projection.shape[1]
out = fixed_projection([X_sym], [X.shape[1]], rand_projection, 'proj1')
out2 = fixed_projection([X_sym], [X.shape[1]],
rand_projection,
'proj2',
pre=rand_projection[:, 0])
out3 = fixed_projection([X_sym], [X.shape[1]],
rand_projection,
'proj3',
post=rand_projection[0])
final = linear([out2], [rand_dim], 5, 'linear', random_state=random_state)
# Test that it compiles with and without bias
f = theano.function([X_sym], [out, out2, out3, final], mode="FAST_COMPILE")
# Test updates
params = list(get_params().values())
grads = tensor.grad(final.mean(), params)
opt = sgd(params, .1)
updates = opt.updates(params, grads)
f2 = theano.function([X_sym], [out2, final], updates=updates)
ret = f(np.ones_like(X))[0]
assert ret.shape[1] != X.shape[1]
ret2 = f(np.ones_like(X))[1]
assert ret.shape[1] != X.shape[1]
out1, final1 = f2(X)
out2, final2 = f2(X)
# Make sure fixed basis is unchanged
assert_almost_equal(out1, out2)
# Make sure linear layer is updated
assert_raises(AssertionError, assert_almost_equal, final1, final2)
minibatch_size = 20
n_hid = 500
l1 = tanh([X_sym], [X.shape[1]], proj_dim=n_hid, name='l1',
random_state=random_state)
y_pred = softmax_zeros([l1], [n_hid], proj_dim=n_targets, name='y_pred')
nll = categorical_crossentropy(y_pred, y_sym).mean()
weights = get_weights(skip_regex=None).values()
L2 = sum([(w ** 2).sum() for w in weights])
cost = nll + .0001 * L2
params = list(get_params().values())
grads = theano.grad(cost, params)
learning_rate = 0.01
opt = sgd(params, learning_rate)
updates = opt.updates(params, grads)
fit_function = theano.function([X_sym, y_sym], [cost], updates=updates)
cost_function = theano.function([X_sym, y_sym], [cost])
predict_function = theano.function([X_sym], [y_pred])
checkpoint_dict = create_checkpoint_dict(locals())
train_itr = minibatch_iterator([X, y], minibatch_size, axis=0,
stop_index=60000)
valid_itr = minibatch_iterator([X, y], minibatch_size, axis=0,
start_index=60000)
def test_vae():
minibatch_size = 10
random_state = np.random.RandomState(1999)
graph = OrderedDict()
X_sym = add_datasets_to_graph([X], ["X"], graph)
l1_enc = softplus_layer([X_sym],
graph,
'l1_enc',
proj_dim=100,
random_state=random_state)
mu = linear_layer([l1_enc],
graph,
'mu',
proj_dim=50,
random_state=random_state)
log_sigma = linear_layer([l1_enc],
graph,
'log_sigma',
proj_dim=50,
random_state=random_state)
samp = gaussian_log_sample_layer([mu], [log_sigma],
graph,
'gaussian_log_sample',
random_state=random_state)
l1_dec = softplus_layer([samp],
graph,
'l1_dec',
proj_dim=100,
random_state=random_state)
out = sigmoid_layer([l1_dec],
graph,
'out',
proj_dim=X.shape[1],
random_state=random_state)
kl = gaussian_log_kl([mu], [log_sigma], graph, 'gaussian_kl').mean()
cost = binary_crossentropy(out, X_sym).mean() + kl
params, grads = get_params_and_grads(graph, cost)
learning_rate = 0.00000
opt = sgd(params)
updates = opt.updates(params, grads, learning_rate)
fit_function = theano.function([X_sym], [cost],
updates=updates,
mode="FAST_COMPILE")
cost_function = theano.function([X_sym], [cost], mode="FAST_COMPILE")
checkpoint_dict = {}
train_indices = np.arange(len(X))
valid_indices = np.arange(len(X))
early_stopping_trainer(fit_function,
cost_function,
checkpoint_dict, [X],
minibatch_size,
train_indices,
valid_indices,
fit_function_output_names=["cost"],
cost_function_output_name="valid_cost",
n_epochs=1)