前提・実現したいこと

Deep Learning Coordinated Beamformingのプログラムの実装を試みており
以下の問題の対処を目指している。

発生している問題・エラーメッセージ

UnboundLocalError: local variable 'batch_index' referenced before assignment

該当のソースコード

Python

<from future import division
import os, keras
os.environ["KERAS_BACKEND"] = "theano"
os.environ["THEANO_FLAGS"] = "device=gpu%d"%(1)
import numpy as np
import theano as th
import theano.tensor as T
from keras.utils import np_utils
import keras.models as models
from keras.layers.core import Reshape,Dense,Dropout,Activation
from keras.optimizers import adam
from scipy.io import loadmat, savemat
import os.path
from keras import backend as K

Model training function

def train(In_train, Out_train, In_test, Out_test,
nb_epoch, batch_size,dr,
num_hidden_layers, nodes_per_layer,
loss_fn,n_BS,n_beams):

in_shp = list(In_train.shape[1:])

AP_models = []

for idx in range(0, n_BS*n_beams-2, n_beams):
    idx_str = str(idx / n_beams + 1)
    
    model = models.Sequential()
    model.add(Dense(nodes_per_layer, activation='relu', init='he_normal',
              name="dense" + idx_str + "1", input_shape=in_shp))
    model.add(Dropout(dr))
    for h in range(num_hidden_layers):
        model.add(Dense(nodes_per_layer, activation='relu',
                  init='he_normal', name="dense" + idx_str + "h" + str(h)))
        model.add(Dropout(dr))
    
    model.add(Dense(n_beams, activation='relu', init='he_normal',
              name="dense" + idx_str + "o"))
    model.compile(loss=loss_fn, optimizer='adam')
    model.summary()
    # perform training ...
    earlyStoppingCallback = \
        keras.callbacks.EarlyStopping(monitor='val_loss',
                                      patience=5,
                                      verbose=0,
                                      mode='auto')
    filepath = 'DLCB_code_output/Results_mmWave_ML'+str(idx)
    history = model.fit(In_train,
                        Out_train[:, idx:idx + n_beams],
                        batch_size=batch_size,
                        nb_epoch=nb_epoch,
                        verbose=2,
                        validation_data=(In_test, Out_test[:,idx:idx + n_beams]),
                        callbacks = [
                            keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=True, mode='auto'),
                            keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')
                        ])

    # we re-load the best weights once training is finished
    model.load_weights(filepath)
    
    AP_models.append(model)
    
    
return AP_models

Reading input and output sets generated from MATLAB

In_set_file=loadmat('DLCB_dataset/DLCB_input.mat')
Out_set_file=loadmat('DLCB_dataset/DLCB_output.mat')

In_set=In_set_file['DL_input']
Out_set=Out_set_file['DL_output']

Parameter initialization

num_user_tot=In_set.shape[0]
n_DL_size=[.001,.05,.1,.15,.2,.25,.3,.35,.4,.45,.5,.55,.6,.65,.7,.75,.8]
count=0
num_tot_TX=4
num_beams=128

for DL_size_ratio in n_DL_size:

print (DL_size_ratio)
count=count+1
DL_size=int(num_user_tot*DL_size_ratio)

np.random.seed(2016)
n_examples = DL_size
num_train  = int(DL_size * 0.8)
num_test   = int(num_user_tot*.2)

train_index = np.random.choice(range(0,num_user_tot), size=num_train, replace=False)
rem_index = set(range(0,num_user_tot))-set(train_index)
test_index= list(set(np.random.choice(list(rem_index), size=num_test, replace=False)))

In_train = In_set[train_index]
In_test =  In_set[test_index] 
    
Out_train = Out_set[train_index]
Out_test = Out_set[test_index]


# Learning model parameters
nb_epoch = 10     
batch_size = 100  
dr = 0.05                  # dropout rate  
num_hidden_layers=4
nodes_per_layer=In_train.shape[1]
loss_fn='mean_squared_error'

# Model training
AP_models = train(In_train, Out_train, In_test, Out_test,
                                      nb_epoch, batch_size,dr,
                                      num_hidden_layers, nodes_per_layer,
                                      loss_fn,num_tot_TX,num_beams)


# Model running/testing
DL_Result={}
for id in range(0,num_tot_TX,1): 
    beams_predicted=AP_models[id].predict( In_test, batch_size=10, verbose=0)

    DL_Result['TX'+str(id+1)+'Pred_Beams']=beams_predicted
    DL_Result['TX'+str(id+1)+'Opt_Beams']=Out_test[:,id*num_beams:(id+1)*num_beams]

DL_Result['user_index']=test_index
savemat('DLCB_code_output/DL_Result'+str(count),DL_Result)>