• 总根 >计算机与教育 >课程 >高等教育课程 >本科课程 >模式识别与人工智能 >zstu-(2021-2022)-1 >学生作业目录 >2019330300081罗旭

  • 2019330300081_罗旭_作业9

    摘要

    通过tensorflow实现一个两层的神经网络。目的是实现一个二次函数的拟合。 from __future__ import print_function import tensorflow as tf import numpy as np import matplotlib.pyplot as plt def add_layer(inputs, in_size, out_size, activation_function=None): # add one more layer and return the output of this layer Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) Wx_plus_b = tf.matmul(

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    ————————————————
    通过tensorflow实现一个两层的神经网络。目的是实现一个二次函数的拟合。

    from __future__ import print_function
    import tensorflow as tf
    import numpy as np
    import matplotlib.pyplot as plt

    def add_layer(inputs, in_size, out_size, activation_function=None):
    # add one more layer and return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
    outputs = Wx_plus_b
    else:
    outputs = activation_function(Wx_plus_b)
    return outputs

    # Make up some real data
    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

    # define placeholder for inputs to network
    xs = tf.placeholder(tf.float32, [None, 1])
    ys = tf.placeholder(tf.float32, [None, 1])
    # add hidden layer
    l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
    # add output layer
    prediction = add_layer(l1, 10, 1, activation_function=None)

    # the error between prediciton and real data
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
    reduction_indices=[1]))
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

    # important step
    init = tf.initialize_all_variables()
    sess = tf.Session()
    sess.run(init)

    # plot the real data
    fig = plt.figure()
    ax = fig.add_subplot(1,1,1)
    ax.scatter(x_data, y_data)
    plt.ion()
    plt.show()

    for i in range(1000):
    # training
    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
    if i % 50 == 0:
    # to visualize the result and improvement
    try:
    ax.lines.remove(lines[0])
    except Exception:
    pass
    prediction_value = sess.run(prediction, feed_dict={xs: x_data})
    # plot the prediction
    lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
    plt.pause(0.1)



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