tf_神经网络的简单搭建

    import tensorflow as tf    import numpy as np    def add_layer(inputs, in_size, out_size, activation_function=None):        Weights = tf.Variable(tf.random_normal([in_size,out_size]))        biases = tf.Variable(tf.zeros([1,out_size]))        Wx_plus_b = tf.matmul(inputs,Weights) + biases 

# 矩阵的广播机制

        if activation_function is None:            outputs = Wx_plus_b        else:            outputs = activation_function(Wx_plus_b)        return outputs    x_data = np.linspace(-1,1,300)[:,np.newaxis]    noise = np.random.normal(0,0.05,x_data.shape)    y_data = np.square(x_data) - 0.5 + noise    xs = tf.placeholder(tf.float32,[None,1])    ys = tf.placeholder(tf.float32,[None,1])    l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)    prediction = add_layer(l1, 10, 1, activation_function=None)    # loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),reduction_indices=[1]))  same as following    loss = tf.reduce_mean(tf.square(ys - prediction))    train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss)    init = tf.global_variables_initializer()    sess = tf.Session()    sess.run(init)    for i in range(1000):        sess.run(train_step,feed_dict={xs:x_data,ys:y_data})        if i%20 == 0:            print(i,sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

关于tf.reduce_mean,tf.reduce_sum等函数中reduction_indices的用法在这里

一目了然，当没有设置该参数时，该参数的默认值是None,将tensor降到0维，也就是一个数