def generate_text(session,
model,
encode,
decode,
description,
d_len,
stop_length=25,
stop_tokens=[STOP, NONE],
temperature=1.0):
init_state = session.run(model["init_state"])
encoder_inputs = data_reader.pad(encode(description), d_len, 'left')
state, encoder_outputs = get_encoder_outputs(session, model, init_state,
encoder_inputs)
start = encode("")[0]
# state, predictions = predict(session, model,
# encoding, encoder_outputs,
# [[start]])
# output_ids = [data_reader.sample(predictions[0], temperature=temperature)]
# inputs = encode(decode(output_ids))
inputs = [start]
output_ids = []
for i in range(stop_length):
x = inputs[-1]
state, predictions = predict(session, model, state, encoder_outputs,
[[x]])
next_id = data_reader.sample(predictions[0], temperature=temperature)
output_ids.append(next_id)
output = decode([next_id])
inputs.append(next_id)
if stop_tokens and output in stop_tokens:
break
output_text = "[" + description + "]\n"
output_text += decode(output_ids)
return output_text
import numpy as np
import data_reader as dr
c9 = dr.sample(
r"C:\Users\Scott Reid\Documents\Masters\Research\Data\TbTe3_AB2")
c9.book_files()
import numpy as np import data_reader as dr c9 = dr.sample(r"C:\Users\Scott Reid\Documents\Masters\Research\Data\ErTe3 C9") c9.book_files()
import numpy as np import data_reader as dr c9 = dr.sample(r"C:\Users\Scott Reid\Documents\Masters\Research\Data\In_Situ_3-09-18") c9.book_files()