import models.ANN as model
from data.reader import Data
from data.vars import Vars
from loader import save_xp, load_xp_model
from utils.CER import CER
import datetime
from keras.callbacks import TensorBoard, ModelCheckpoint
from keras.optimizers import Adam
import numpy as np
import csv
import os
V = Vars()
def train_model(net, data, name,
validation_data,
learning_rate=0.001,
loss='categorical_crossentropy',
batch_size=1,
epoch=1,
steps_per_epoch=1):
tb = TensorBoard(log_dir=V.experiments_folder + "/keras/" + name + '/TensorBoard/',
histogram_freq=1,
write_graph=True,
write_images=False)
cp = ModelCheckpoint(filepath=V.experiments_folder + "/keras/" + name + '/weights/w.{epoch:02d}-{val_loss:.2f}.hdf5',
save_best_only=True,
monitor='val_loss',
from keras import Model
import json
import numpy as np
import cv2
from matplotlib.pyplot import imshow, show, figure
import scipy
import matplotlib.animation as animation
from models.custom_recurrents import AttentionDecoder
from data.reader import Data
from data.vars import Vars
import os
V = Vars(open('../../vars.json', 'r'))
os.chdir('..')
def create_net_attention_maps(net, name):
"""
:param net:
:param name:
:return: the same net which outputs the attention maps instead of the labels
"""
d = "./experiments/" + name
with open(d + '/model.json', 'r') as f:
params = json.load(f)
layers = params['config']['layers']