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<p>我正在研制一辆自动驾驶汽车。我想用tflearn中的CNN从图片中预测转向角。问题是它只输出0.1。你觉得问题是什么?图片大小为128x128,但我尝试将其调整为28x28,以便使用mnist示例中的代码。标签的转向角在0到180之间。我也可以说,在训练中损失并没有变小。在</p>
<p>在培训.py在</p>
<pre><code>import tflearn
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.estimator import regression
import tflearn.<a href="https://www.cnpython.com/pypi/dataset" class="inner-link">dataset</a>s.mnist as mnist
import numpy
from scipy import misc
import csv
nrOfFiles = 0
csv_list = []
with open('/Users/gustavoskarsson/Desktop/car/csvfile.csv', 'r') as f:
reader = csv.reader(f)
csv_list = list(reader)
nrOfFiles = len(csv_list)
pics = []
face = misc.face()
for i in range(0, nrOfFiles):
face = misc.imread('/Users/gustavoskarsson/Desktop/car/pics/' + str(i) + '.jpg')
face = misc.imresize(face[:,:,0], (28, 28))
pics.<a href="https://www.cnpython.com/list/append" class="inner-link">append</a>(face)
X = numpy.array(pics)
steer = []
throt = []
for i in range(0, nrOfFiles):
steer.append(csv_list[i][1])
throt.append(csv_list[i][2])
#y__ = numpy.array([steer, throt])
Y = numpy.array(steer)
Y = Y.reshape(-1, 1)
#Strunta i gasen till att börja med.
convnet = input_data(shape=[None, 28, 28, 1], name='input')
convnet = conv_2d(convnet, 32, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = conv_2d(convnet, 64, 2, activation='relu')
convnet = max_pool_2d(convnet, 2)
convnet = fully_connected(convnet, 1024, activation='relu')
convnet = dropout(convnet, 0.8)
convnet = fully_connected(convnet, 1, activation='softmax')
convnet = regression(convnet, optimizer='adam', learning_rate=0.01, loss='mean_square', name='targets')
model = tflearn.DNN(convnet)
model.fit(X, Y, n_epoch=6, batch_size=10, show_metric=True)
model.save('mod.model')
</code></pre>
<p>在预测.py在</p>
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