Iris Classification Neural Network
Neural Network
formula derivation
\[\begin{align}a & = ?x \cdot ?w_1 \y ?& = a \cdot w_2 \& = x \cdot w_1 \cdot w_2 \y & = softmax(y)\end{align}\]
code (training only)
\[a = x \cdot ?w_1 \y = a \cdot w_2\]
w1 = tf.Variable(tf.random_normal([4,5], stddev=1, seed=1))w2 = tf.Variable(tf.random_normal([5,3], stddev=1, seed=1))x = tf.placeholder(tf.float32, shape=(None, 4), name=‘x-input‘)a = tf.matmul(x, w1)y = tf.matmul(a, w2)既然是有监督学习，那就在训练阶段必须要给出 label，以此来计算交叉熵
# 用来存储数据的标签y_ = tf.placeholder(tf.float32, shape=(None, 3), name=‘y-input‘)隐藏层的激活函数是 sigmoid
y = tf.sigmoid(y)softmax 与 交叉熵(corss entropy) 的组合函数，损失函数是交叉熵的均值
# softmax & corss_entropycross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y)# meancross_entropy_mean = tf.reduce_mean(cross_entropy)为了防止神经网络过拟合，需加入正则化项，一般选取 “L2 正则化”
loss = cross_entropy_mean + \ ???tf.contrib.layers.l2_regularizer(regulation_lamda)(w1) + \ ???tf.contrib.layers.l2_regularizer(regulation_lamda)(w2)为了加速神经网络的训练过程，需加入“指数衰减”技术
表示训练过程的计算图，优化方法选择了 Adam 算法，本质是反向传播算法。还可以选择“梯度下降法”(GradientDescentOptimizer)
train_step = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)训练阶段
with tf.Session() as sess: ?# Session 最好在“上下文机制”中开启，以防资源泄露 ???init_op = tf.global_variables_initializer() ?# 初始化网络中节点的参数，主要是 w1,w2 ???sess.run(init_op) ???steps = 10000 ???for i in range(steps): ???????beg = (i * batch_size) % dataset_size ???# 计算 batch ???????end = min(beg+batch_size, dataset_size) ?# 计算 batch ???????????sess.run(train_step, feed_dict={x:X[beg:end], y_:Y[beg:end]}) ?# 反向传播，训练网络 ???????if i % 1000 == 0: ???????????total_corss_entropy = sess.run( ?# 计算交叉熵 ???????????????cross_entropy_mean, ?????????# 计算交叉熵 ???????????????feed_dict={x:X, y_:Y} ???????# 计算交叉熵 ???????????) ???????????print("After %d training steps, cross entropy on all data is %g" % (i, total_corss_entropy))在训练阶段中，需要引入“滑动平均模型”来提高模型在测试数据上的健壮性（这是书上的说法，而我认为是泛化能力）
全部代码
# -*- encoding=utf8 -*-from sklearn.datasets import load_irisimport tensorflow as tfdef label_convert(Y): ???l = list() ???for y in Y: ???????if y == 0: ???????????l.append([1,0,0]) ???????elif y == 1: ???????????l.append([0, 1, 0]) ???????elif y == 2: ???????????l.append([0, 0, 1]) ???return ldef load_data(): ???iris = load_iris() ???X = iris.data ???Y = label_convert(iris.target) ???return (X,Y)if __name__ == ‘__main__‘: ???X,Y = load_data() ???learning_rate = 0.001 ???batch_size = 10 ???dataset_size = 150 ???regulation_lamda = 0.001 ???w1 = tf.Variable(tf.random_normal([4,5], stddev=1, seed=1)) ???w2 = tf.Variable(tf.random_normal([5,3], stddev=1, seed=1)) ???x = tf.placeholder(tf.float32, shape=(None, 4), name=‘x-input‘) ???y_ = tf.placeholder(tf.float32, shape=(None, 3), name=‘y-input‘) ???a = tf.matmul(x, w1) ???y = tf.matmul(a, w2) ???y = tf.sigmoid(y) ???cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_, logits=y) ???cross_entropy_mean = tf.reduce_mean(cross_entropy) ???loss = cross_entropy_mean + \ ??????????tf.contrib.layers.l2_regularizer(regulation_lamda)(w1) + \ ??????????tf.contrib.layers.l2_regularizer(regulation_lamda)(w2) ???train_step = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss) ???with tf.Session() as sess: ???????init_op = tf.global_variables_initializer() ???????sess.run(init_op) ???????steps = 10000 ???????for i in range(steps): ???????????beg = (i * batch_size) % dataset_size ???????????end = min(beg+batch_size, dataset_size) ???????????sess.run(train_step, feed_dict={x:X[beg:end], y_:Y[beg:end]}) ???????????if i % 1000 == 0: ???????????????total_corss_entropy = sess.run( ???????????????????cross_entropy_mean, ???????????????????feed_dict={x:X, y_:Y} ???????????????) ???????????????print("After %d training steps, cross entropy on all data is %g" % (i, total_corss_entropy)) ???????print(sess.run(w1)) ???????print(sess.run(w2))Experiment Result
random split cross validation
Iris Classification Neural Network
原文地址：https://www.cnblogs.com/fengyubo/p/9060249.html