def load(lstm_opts=None):
if not lstm_opts: lstm_opts = {"class": "lstm2"}
lstm_opts = lstm_opts.copy()
lstm_opts.update({"n_out": 10, "from": "in"})
num_inputs = 9
num_outputs = 2
net_topo = {
"in": {
"class": "dump",
"filename": "in"
},
"lstm": lstm_opts,
"lstm_dump": {
"class": "dump",
"from": "lstm",
"filename": "lstm"
},
"output": {
"class": "softmax",
"loss": "ce",
"from": "lstm_dump"
}
}
collected_data = {}
DumpLayer.global_debug_container = collected_data
net = Network.LayerNetwork.from_json(json_content=net_topo,
n_in=num_inputs,
n_out={"classes": (num_outputs, 1)},
train_flag=True)
net.declare_train_params()
# Init dataset and prepare one minibatch.
epoch = 1
dataset = Task12AXDataset(num_seqs=1000,
seq_ordering="random",
chunking="200:200")
dataset.init_seq_order(epoch=epoch)
batch_gen = dataset.generate_batches(recurrent_net=net.recurrent,
batch_size=5000,
max_seqs=10)
batches = batch_gen.peek_next_n(1)
# We need the DummyDevice for assign_dev_data.
dev = DummyDevice()
assign_success, _ = EngineUtil.assign_dev_data(device=dev,
dataset=dataset,
batches=batches)
assert assign_success
dev.initialize(net)
dev.update_data()
givens = [(net.y[k], dev.y[k]) for k in dev.used_data_keys]
givens += [(net.j[k], dev.j[k]) for k in dev.used_data_keys]
# Now gradients, updates and compile everything.
gradients = {
p: T.grad(net.get_objective(), p, known_grads=net.known_grads)
for p in net.train_params_vars
}
updater = Updater(adam=True)
updater.initVars(net, gradients)
updater.setLearningRate(learning_rate=0.01)
trainer = theano.function(inputs=[],
outputs=[net.total_cost],
givens=givens,
updates=updater.getUpdateList(),
on_unused_input='warn',
name="train_and_updater")
for p in net.train_params_vars:
collected_data["param:%s" % p.name] = p.get_value()
# And finally, run it.
cost = trainer()
collected_data["cost"] = cost
return collected_data
def load(lstm_opts=None):
if not lstm_opts: lstm_opts = {"class": "lstm2"}
lstm_opts = lstm_opts.copy()
lstm_opts.update({"n_out": 10, "from": "in"})
num_inputs = 9
num_outputs = 2
net_topo = {
"in": {"class": "dump", "filename": "in"},
"lstm": lstm_opts,
"lstm_dump": {"class": "dump", "from": "lstm", "filename": "lstm"},
"output": {"class": "softmax", "loss": "ce", "from": "lstm_dump"}
}
collected_data = {}
DumpLayer.global_debug_container = collected_data
net = Network.LayerNetwork.from_json(
json_content=net_topo,
n_in=num_inputs,
n_out={"classes": (num_outputs, 1)},
train_flag=True
)
net.declare_train_params()
# Init dataset and prepare one minibatch.
epoch = 1
dataset = Task12AXDataset(num_seqs=1000, seq_ordering="random", chunking="200:200")
dataset.init_seq_order(epoch=epoch)
batch_gen = dataset.generate_batches(
recurrent_net=net.recurrent,
batch_size=5000,
max_seqs=10)
batches = batch_gen.peek_next_n(1)
# We need the DummyDevice for assign_dev_data.
dev = DummyDevice()
assign_success, _ = EngineUtil.assign_dev_data(device=dev, dataset=dataset, batches=batches)
assert assign_success
dev.initialize(net)
dev.update_data()
givens = [(net.y[k], dev.y[k]) for k in dev.used_data_keys]
givens += [(net.j[k], dev.j[k]) for k in dev.used_data_keys]
# Now gradients, updates and compile everything.
gradients = {p: T.grad(net.get_objective(), p, known_grads=net.known_grads)
for p in net.train_params_vars}
updater = Updater(adam=True)
updater.initVars(net, gradients)
updater.setLearningRate(learning_rate=0.01)
trainer = theano.function(
inputs=[],
outputs=[net.total_cost],
givens=givens,
updates=updater.getUpdateList(),
on_unused_input='warn',
name="train_and_updater")
for p in net.train_params_vars:
collected_data["param:%s" % p.name] = p.get_value()
# And finally, run it.
cost = trainer()
collected_data["cost"] = cost
return collected_data
