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_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', proj_dim=n_hid,
random_state=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', proj_dim=n_out,
random_state=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
learning_rate = 0.001
opt = sgd(params, learning_rate)
updates = opt.updates(params, grads)
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,
train_indices, valid_indices,
checkpoint_dict,
[X, y], minibatch_size,
list_of_train_output_names=["cost"],
valid_output_name="valid_cost",
n_epochs=1)
l1_enc = softplus_layer([X_sym], graph, 'l1_enc', n_enc_layer[0], random_state)
l2_enc = softplus_layer([l1_enc], graph, 'l2_enc', n_enc_layer[1],
random_state)
code_mu = linear_layer([l2_enc], graph, 'code_mu', n_code, random_state)
code_log_sigma = linear_layer([l2_enc], graph, 'code_log_sigma', n_code,
random_state)
kl = gaussian_log_kl([code_mu], [code_log_sigma], graph, 'kl').mean()
samp = gaussian_log_sample_layer([code_mu], [code_log_sigma], graph, 'samp',
random_state)
# decode path aka p
l1_dec = softplus_layer([samp], graph, 'l1_dec', n_dec_layer[0], random_state)
l2_dec = softplus_layer([l1_dec], graph, 'l2_dec', n_dec_layer[1], random_state)
out = linear_layer([l2_dec], graph, 'out', n_input, random_state)
nll = squared_error(out, X_sym).mean()
# log p(x) = -nll so swap sign
# want to minimize cost in optimization so multiply by -1
cost = -1 * (-nll - kl)
params, grads = get_params_and_grads(graph, cost)
learning_rate = 0.0003
opt = adam(params)
updates = opt.updates(params, grads, learning_rate)
# Checkpointing
try:
checkpoint_dict = load_last_checkpoint()
fit_function = checkpoint_dict["fit_function"]
cost_function = checkpoint_dict["cost_function"]
encode_function = checkpoint_dict["encode_function"]
def test_squared_error():
graph = OrderedDict()
X_sym = add_datasets_to_graph([X], ["X"], graph)
cost = squared_error(.5 * X_sym, X_sym)
theano.function([X_sym], cost, mode="FAST_COMPILE")
def test_squared_error():
cost = squared_error(.5 * X_sym, X_sym)
theano.function([X_sym], cost, mode="FAST_COMPILE")
def test_squared_error():
cost = squared_error(.5 * X_sym, X_sym)
theano.function([X_sym], cost, mode="FAST_COMPILE")
