###### Set global Theano config #######
import os
t_flags = "mode=FAST_RUN,device=cpu,floatX=float32, optimizer='fast_run', allow_gc=False"
print("Theano Flags: " + t_flags)
os.environ["THEANO_FLAGS"] = t_flags
###### Imports ######
import numpy as np
import time
from past.builtins import xrange
import recnet
from .util import edit_distance
### 1. Step: Create new model
rn = recnet.rnnModel()
### 2. Step: Define parameters
rn.parameter["train_data_name"] = "numbers_image_train.klepto"
rn.parameter["valid_data_name"] = "numbers_image_valid.klepto"
rn.parameter["data_location"] = "data_set/"
rn.parameter["batch_size"] = 1
rn.parameter["net_size"] = [9, 10, 10 + 1]
rn.parameter["net_unit_type"] = ['input', 'conv', 'softmax']
rn.parameter["net_act_type"] = ['-', 'tanh', '-']
rn.parameter["net_arch"] = ['-', 'bi', 'ff']
rn.parameter["random_seed"] = 211
rn.parameter["epochs"] = 30
rn.parameter["learn_rate"] = 0.001
###### Set global Theano config #######
import os
t_flags = "mode=FAST_RUN,device=cpu,floatX=float32, optimizer='fast_run', allow_gc=False"
print("Theano Flags: " + t_flags)
os.environ["THEANO_FLAGS"] = t_flags
###### Imports ######
import numpy as np
import time
from past.builtins import xrange
import recnet
from .util import edit_distance
### 1. Step: Create new model
rn = recnet.rnnModel()
### 2. Step: Define parameters
rn.parameter["train_data_name"] = "numbers_image_train.klepto"
rn.parameter["valid_data_name"] = "numbers_image_valid.klepto"
rn.parameter["data_location" ] = "data_set/"
rn.parameter["batch_size" ] = 1
rn.parameter["net_size" ] = [ 9, 10, 10+1]
rn.parameter["net_unit_type" ] = ['input', 'conv', 'softmax']
rn.parameter["net_act_type" ] = [ '-', 'tanh', '-']
rn.parameter["net_arch" ] = [ '-', 'bi', 'ff']
rn.parameter["random_seed" ] = 211
rn.parameter["epochs" ] = 30
rn.parameter["learn_rate" ] = 0.001