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关于python损失函数代码的信息(12)

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self.session.run(self.trainer, feed_dict={self.input_layer: cases, self.label_layer: labels})12345
使用训练好的模型进行预测:
self.session.run(self.layers[-1], feed_dict={self.input_layer: case})1
完整代码:
import tensorflow as tfimport numpy as npdef make_layer(inputs, in_size, out_size, activate=None):
weights = tf.Variable(tf.random_normal([in_size, out_size]))
basis = tf.Variable(tf.zeros([1, out_size]) + 0.1)
result = tf.matmul(inputs, weights) + basisif activate is None:return resultelse:return activate(result)class BPNeuralNetwork:
def __init__(self):
self.session = tf.Session()
self.input_layer = None
self.label_layer = None
self.loss = None
self.optimizer = None
self.layers = []def __del__(self):
self.session.close()def train(self, cases, labels, limit=100, learn_rate=0.05):
# 构建网络
self.input_layer = tf.placeholder(tf.float32, [None, 2])
self.label_layer = tf.placeholder(tf.float32, [None, 1])
self.layers.append(make_layer(self.input_layer, 2, 10, activate=tf.nn.relu))
self.layers.append(make_layer(self.layers[0], 10, 2, activate=None))
self.loss = tf.reduce_mean(tf.reduce_sum(tf.square((self.label_layer - self.layers[1])), reduction_indices=[1]))
self.optimizer = tf.train.GradientDescentOptimizer(learn_rate).minimize(self.loss)
initer = tf.initialize_all_variables()# 做训练
self.session.run(initer)for i in range(limit):
self.session.run(self.optimizer, feed_dict={self.input_layer: cases, self.label_layer: labels})def predict(self, case):
return self.session.run(self.layers[-1], feed_dict={self.input_layer: case})def test(self):
x_data = https://www.04ip.com/post/np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y_data = https://www.04ip.com/post/np.array([[0, 1, 1, 0]]).transpose()
test_data = https://www.04ip.com/post/np.array([[0, 1]])
self.train(x_data, y_data)
print(self.predict(test_data))
nn = BPNeuralNetwork()
nn.test()12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152
上述模型虽然简单但是使用不灵活, 作者采用同样的思想实现了一个可以自定义输入输出维数以及多层隐含神经元的网络, 可以参见dynamic_bpnn.py
import tensorflow as tfimport numpy as npdef make_layer(inputs, in_size, out_size, activate=None):
weights = tf.Variable(tf.random_normal([in_size, out_size]))
basis = tf.Variable(tf.zeros([1, out_size]) + 0.1)
result = tf.matmul(inputs, weights) + basisif activate is None:return resultelse:return activate(result)class BPNeuralNetwork:
def __init__(self):
self.session = tf.Session()
self.loss = None
self.optimizer = None
self.input_n = 0
self.hidden_n = 0
self.hidden_size = []
self.output_n = 0
self.input_layer = None
self.hidden_layers = []
self.output_layer = None
self.label_layer = None
def __del__(self):
self.session.close()def setup(self, ni, nh, no):
# 设置参数个数
self.input_n = ni
self.hidden_n = len(nh)#隐藏层的数量
self.hidden_size = nh#每个隐藏层中的单元格数
self.output_n = no#构建输入层
self.input_layer = tf.placeholder(tf.float32, [None, self.input_n])#构建标签层
self.label_layer = tf.placeholder(tf.float32, [None, self.output_n])#构建隐藏层
in_size = self.input_n
out_size = self.hidden_size[0]
inputs = self.input_layer
self.hidden_layers.append(make_layer(inputs, in_size, out_size, activate=tf.nn.relu))for i in range(self.hidden_n-1):
in_size = out_size
out_size = self.hidden_size[i+1]
inputs = self.hidden_layers[-1]
self.hidden_layers.append(make_layer(inputs, in_size, out_size, activate=tf.nn.relu))#构建输出层
self.output_layer = make_layer(self.hidden_layers[-1], self.hidden_size[-1], self.output_n)def train(self, cases, labels, limit=100, learn_rate=0.05):

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