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# pylint: disable=C0111,too-many-arguments,too-many-instance-attributes,too-many-locals,redefined-outer-name,fixme | |
# pylint: disable=superfluous-parens, no-member, invalid-name | |
import sys | |
sys.path.insert(0, "../../python") | |
import mxnet as mx | |
import numpy as np | |
import cv2, random | |
from io import BytesIO | |
from captcha.image import ImageCaptcha |
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//used for training | |
def bi_lstm_unroll(seq_len, input_size,num_hidden, num_embed, num_label, dropout=0.): | |
embed_weight = mx.sym.Variable("embed_weight") | |
cls_weight = mx.sym.Variable("cls_weight") | |
cls_bias = mx.sym.Variable("cls_bias") | |
last_states = [] | |
last_states.append(LSTMState(c = mx.sym.Variable("l0_init_c"), h = mx.sym.Variable("l0_init_h"))) | |
last_states.append(LSTMState(c = mx.sym.Variable("l1_init_c"), h = mx.sym.Variable("l1_init_h"))) | |
forward_param = LSTMParam(i2h_weight=mx.sym.Variable("l0_i2h_weight"), |
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""" | |
This is a batched LSTM forward and backward pass | |
""" | |
import numpy as np | |
import code | |
class LSTM: | |
@staticmethod | |
def init(input_size, hidden_size, fancy_forget_bias_init = 3): |