TensorFlow 的 hello world！入门首选

r囧r小猫 2021-11-11 22:02 258阅读 0赞

import tensorflow as tf
    import matplotlib.pyplot as plt
    import numpy as np
    
    # 训练数据
    x_data = np.linspace(-0.5,0.5,200)[:,np.newaxis]
    noise = np.random.normal(0,0.02,x_data.shape)
    y_data = np.square(x_data) + noise
    print x_data.shape
    print y_data.shape
    
    # 定义两个 placeholder
    x = tf.placeholder(tf.float32,shape=[None,1])        # None 代表不确定大小
    y = tf.placeholder(tf.float32,shape=[None,1])        # None 代表不确定大小
    
    # 定义网络的中间层
    weight_L1 = tf.Variable(tf.random_normal([1,10]))
    biases_L1 = tf.Variable(tf.zeros([1,10]))            # 偏执个数等于神经元个数！！！
    Wx_plus_b_L1 = tf.matmul(x,weight_L1) + biases_L1
    L1 = tf.nn.tanh(Wx_plus_b_L1)
    
    # 定义网络的输出层
    weight_L2 = tf.Variable(tf.random_normal([10,1]))
    biases_L2 = tf.Variable(tf.zeros([1,1]))
    Wx_plus_b_L2 = tf.matmul(L1,weight_L2) + biases_L2
    prediction = tf.nn.tanh(Wx_plus_b_L2)                # 激活函数的值域范围 tanh(-1,1) ,sigmoid(0,1), relu(0,+inf)
    
    # 定义损失函数
    loss = tf.reduce_mean(tf.square(prediction-y))
    # 使用梯度下降法训练
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
    
    
    # 建立会话
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for _ in range(2000):
            sess.run(train_step,feed_dict={x:x_data,y:y_data})
        
        prediction_value = sess.run(prediction,feed_dict={x:x_data})
        
        plt.figure()
        plt.scatter(x_data,y_data)
        plt.plot(x_data,prediction_value,'r-',lw=5)
        plt.show()
        
        print sess.run(tf.shape(weight_L1))
        print weight_L1[0,1].eval()
        print weight_L1.eval()

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