代码详解：轻巧！低廉！为自动驾驶汽车实施端到端学习( 五 )_全文共3507字

\n \n

def weight_variable(shape):

initial = tf.truncated_normal(shape stddev=0.1)

return tf.Variable(initial)

\n \n

def bias_variable(shape):

initial = tf.constant(0.1 shape=shape)

return tf.Variable(initial)

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def conv2d(x W stride):

return tf.nn.conv2d(x W strides=[1 stride stride 1
padding='VALID')

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x = tf.placeholder(tf.float32 shape=[None 66 200 3
)

y_ = tf.placeholder(tf.float32 shape=[None 1
)

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x_image = x

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#first convolutional layer

W_conv1 = weight_variable([5 5 3 24

代码详解：轻巧！低廉！为自动驾驶汽车实施端到端学习( 五 )