#print("Created ComputationGraph, variables:");
#print(cg.variables)
print("Created ComputationGraph, parameters:")
#print(cg.parameters)
for p in cg.parameters:
print(str(p), p.shape, p.dtype)
print("Created ComputationGraph, inputs:")
print(cg.inputs)
# Strangely, all the examples use : DataStreamMonitoring in MainLoop
model = Model(labels)
print("Model.dict_of_inputs():")
print(model.dict_of_inputs())
print("Model list inputs:")
print([v.name for v in model.inputs])
## Model loading from saved file
model.set_parameter_values(load_parameter_values(save_state_path))
examine_embedding(lookup.W.get_value())
label_ner = model.get_theano_function()
print(model.inputs)
print("printed label_ner.params")
for test_data in data_stream.get_epoch_iterator():
ordered_batch = test_data[
0:3] # Explicitly strip off the pre-defined labels
#print("Created ComputationGraph, variables:");
#print(cg.variables)
print("Created ComputationGraph, parameters:");
#print(cg.parameters)
for p in cg.parameters:
print(str(p), p.shape, p.dtype)
print("Created ComputationGraph, inputs:");
print(cg.inputs)
# Strangely, all the examples use : DataStreamMonitoring in MainLoop
model = Model(labels)
print("Model.dict_of_inputs():");
print(model.dict_of_inputs())
print("Model list inputs:");
print([ v.name for v in model.inputs])
## Model loading from saved file
model.set_parameter_values(load_parameter_values(save_state_path))
examine_embedding(lookup.W.get_value())
label_ner = model.get_theano_function()
print(model.inputs)
print("printed label_ner.params")
for test_data in data_stream.get_epoch_iterator():
ordered_batch = test_data[0:3] # Explicitly strip off the pre-defined labels
#print(ordered_batch)
