dataTrain, dataTest = data[train_index], data[test_index]
dTrain = bf.onlySampleSize(dataTrain, 1)
dTest = bf.onlySampleSize(dataTest, 1)
acc_xTrain = []
acc_yTrain = []
aud_xTrain = []
aud_yTrain = []
xTrain = []
yTrain = []
for d in dTrain:
acc_xTrain.append(d[0 : numTotalAcc])
aud_xTrain.append(d[numTotalAcc : numTotalAcc + numTotalAud])
xTrain.append(d[0 : numTotalAcc + numTotalAud])
acc_yTrain.append(bf.oneHotLabel(int(d[-1]), numLabel))
aud_yTrain.append(bf.oneHotLabel(int(d[-1]), numLabel))
yTrain.append(bf.oneHotLabel(int(d[-1]), numLabel))
print('acc_xTrain : ', len(acc_xTrain))
print('aud_xTrain : ', len(aud_xTrain))
acc_xTest = []
acc_yTest = []
aud_xTest = []
aud_yTest = []
for d in dTest:
acc_xTest.append(d[0 : numTotalAcc])
aud_xTest.append(d[numTotalAcc : numTotalAcc + numTotalAud])
acc_yTest.append(bf.oneHotLabel(int(d[-1]), numLabel))
aud_yTest.append(bf.oneHotLabel(int(d[-1]), numLabel))
kf.get_n_splits(data)
data = np.array(data)
for train_index, test_index in kf.split(data):
dataTrain, dataTest = data[train_index], data[test_index]
dTrain = bf.onlySampleSize(dataTrain, 1)
xTrain = []
yTrain = []
for d in dTrain:
xTrain.append(d[0:300])
#print("yTrain : ",(int(d[300])))
#print("d[0:301 : " , d[0:301])
#print("d : ",(d[300]))
yTrain.append(bf.oneHotLabel(int(d[300]), numLabel))
# on Imac the GPU is not working. so
with tf.device('/gpu:3'):
inputX = tf.placeholder(tf.float32, [None, 300])
outputY = tf.placeholder(tf.float32, [None, 10])
#1 * 100 * 3
W_conv1 = weight_variable([3, 1, 1, 9]) #width, height, channel input, channel output
b_conv1 = bias_variable([9])
x_image = tf.reshape(inputX, [-1, 100, 3, 1]) # -1, width, height, channel)
h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)
#1 * 150 * 9
W_conv2 = weight_variable([3, 1, 9, 18])
b_conv2 = bias_variable([18])