plot.py

 

"""
Please note, this code is only for python 3+. If you are using python 2+, please modify the code accordingly.
"""
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import datetime,time
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]) + 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
'''
data=pd.read_csv('h:\\tensorflowDLInSE\\data1.csv')
x_data=data['fre'].tolist()
x_data=np.array(x_data).reshape((1001,1))
y_data=data['mag'].tolist()
y_data=np.array(y_data).reshape((1001,1))
##plt.scatter(x_data, y_data)
##plt.show()

# 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.sigmoid)
# 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(0.03).minimize(loss)
# important step
init = tf.global_variables_initializer()
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()

step_num=0
starttime=datetime.datetime.now()
batch_size=300
all_size=1001
for i in range(100000):
    # training
    start_index=(i*batch_size)%all_size
    end_index=min((start_index+batch_size),all_size)
    sess.run(train_step, feed_dict={xs: x_data[start_index:end_index],
                                    ys: y_data[start_index:end_index]})
    if i % 5000== 0:
        # to visualize the result and improvement
        step_num+=1
        try:
            ax.lines.remove(lines[0])
        except Exception:
            pass
        prediction_value = sess.run(prediction, feed_dict={xs: x_data})
        # plot the prediction
        print(step_num)
        lines = ax.plot(x_data, prediction_value, 'r-', lw=5)
        plt.pause(0.1)
endtime=datetime.datetime.now()
print('耗时： ',(endtime-starttime).seconds,' 秒')

#need to add a method which can be used to calculate diff curves