def run():
text_in = generate_text_input()
NUM_EPOCS = 500
#train(full_layer_learner,text_in,text_in,NUM_EPOCS)
state_pred = full_layer_learner.get_stateful_predict()
[outs] = state_pred(text_in[:1000])
#np.set_printoptions(threshold=np.inf)
print(outs)
print(string_processing.out_list_to_str(outs))
[full_outs] = state_pred(text_in)
outstr = string_processing.out_list_to_str(full_outs)
save_text("sampled_outputs/deep_learner.txt", outstr)
def run():
text_in = generate_text_input()
NUM_EPOCS = 500
train(full_layer_learner, text_in, text_in, NUM_EPOCS)
[outs] = full_layer_learner.get_stateful_predict()(text_in[:1000])
#np.set_printoptions(threshold=np.inf)
print(outs)
print(string_processing.out_list_to_str(outs))
def test():
text_in = generate_text_input()
expected = text_in[:200]
[actual] = full_layer_learner.get_stateful_predict()(expected)
act_text = string_processing.out_list_to_str(actual)
my_error_fn = error_fn(calc_error_squared)
errors = []
skip = 1
for i in range(0, 198, skip):
[err] = my_error_fn(expected[i + 1:i + skip + 1], actual[i:i + skip])
print(err, act_text[i:i + skip])
def run_cumulative_lstm():
in_base_stack = np.load(lstm_framework.base_input_filename)
#in_first_stack = np.load(lstm_framework.first_stage_cell_filename)
in_second_stack = np.load(lstm_framework.second_stage_output_filename)
in_stack3 = np.concatenate((in_base_stack, in_second_stack), axis=1)
lstm_stage3 = lstm_framework.gen_lstm_with("huck_fin_stage3outsub_fixed",
in_stack3, in_base_stack, 200)
#lstm_stage3.train(in_stack3,in_base_stack,100)
[out_vals] = lstm_stage3.state_predict(in_stack3[:3000])
outstr = string_processing.out_list_to_str(out_vals)
save_text("sampled_outputs/stacked_output_full.txt", outstr)
all_idxs = lstm_stage3.stateful_gen
outstr = "".join(string_processing.GOOD_CHARS[idx] for idx in all_idxs)
print(outstr)
save_text("sampled_outputs/basic_generated_text.txt", outstr)
print(compare_text(outstr))
def stateful_predict():
[outs] = my_lstm.state_predict(in_stack)
return string_processing.out_list_to_str(outs)
cell_state = np.zeros(CELL_STATE_LEN)
output_vec = np.zeros(OUT_LEN)
np_cell_forget = NP_WeightBias(cell_forget_fn)
np_add_barrier = NP_WeightBias(add_barrier_fn)
np_add_cell = NP_WeightBias(add_cell_fn)
np_new_output = NP_WeightBias(to_new_output_fn)
np_full_output = NP_WeightBias(full_output_fn)
all_cell_states = []
all_outputs = []
i = 0
for inp_vec in input_list:
cell_state, output_vec = np_calc_output(inp_vec, cell_state,
output_vec, np_cell_forget,
np_add_barrier, np_add_cell,
np_new_output)
all_cell_states.append(cell_state)
all_outputs.append(np.tanh(np_full_output.calc_output(output_vec)))
if i % 100 == 0:
print(i, flush=True)
i += 1
return all_cell_states, all_outputs
train_str = string_processing.get_str("data/huck_fin.txt")[:1000]
in_vec_list = string_processing.in_vec(train_str)
out_cells, out_outputs = stateful_predict(in_vec_list)
outtxt = string_processing.out_list_to_str(out_outputs)
print(outtxt)
def save_full_prediction():
inp1, _ = get_inputs_unbatched(text_in)
pred_fn = full_layer_learner1.get_stateful_predict()
[outputs] = pred_fn(inp1)
str1 = string_processing.out_list_to_str(outputs)
save_text("sampled_outputs/model2_full.txt", str1)
def predict_model():
inp1, _ = get_inputs_unbatched(text_in[:1000])
pred_fn = full_layer_learner1.get_stateful_predict()
[outputs] = pred_fn(inp1)
print(string_processing.out_list_to_str(outputs))