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fourier.py
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fourier.py
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# -*- coding: utf-8 -*-
"""
Created on Wed Aug 5 14:20:00 2020
@author: Qiuhao Wu
"""
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
import numpy as np
from scipy.misc import imresize
from tqdm import tqdm
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
learning_rate = 0.001
batch_size=128
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)
size=28
#4f system can be considered as a linear interconection
def sf_system(u,w):
U=tf.fft2d(u)
#W=tf.fft2d(w)
W=tf.fft2d(tf.cast(w,dtype=tf.complex64))
return tf.ifft2d(U*W)
def make_random(shape):
return np.random.random(size = shape).astype('float32')
def rang(arr,shape,size=size,base =28):
x0 = shape[0] * size // base
y0 = shape[2] * size // base
delta = (shape[1]-shape[0])* size // base
return arr[x0:x0+delta,y0:y0+delta]
#return arr[shape[0]*size//base:shape[1]*size//base,shape[2]*size//base:shape[3]*size//base]
def reduce_mean(tf_):
return tf.reduce_mean(tf_)
def _ten_regions(a):
return tf.map_fn(reduce_mean,tf.convert_to_tensor([
rang(a,(6,9,6,9)),
rang(a,(6,9,12,15)),
rang(a,(6,9,18,21)),
rang(a,(12,15,3,6)),
rang(a,(12,15,10,13)),
rang(a,(12,15,17,20)),
rang(a,(12,15,23,26)),
rang(a,(18,21,6,9)),
rang(a,(18,21,12,15)),
rang(a,(18,21,18,21))
]))
def ten_regions(logits):
return tf.map_fn(_ten_regions,tf.abs(logits),dtype=tf.float32)
def download_text(msg,epoch,MIN=1,MAX=7,name=''):
for i in range(1,7):
np.savetxt("{}_Time_{}_layer_{}.txt".format(name,epoch+1,i),msg[i-1])
print("Done")
def download_image(msg,epoch,MIN=1,MAX=7,name=''):
print(f"Plot images-[{MIN}:{MAX}]")
for i in range(MIN,MAX):
#print("Image {}:".format(i))
plt.figure(dpi=650.24)
plt.axis('off')
plt.grid('off')
plt.imshow(msg[i-1])
plt.savefig("{}_Time_{}_layer_{}.jpg".format(name,epoch+1,i))
#print("Done")
#def my_activation(x):
# return 1-0.0249*tf.exp(-x/11.5732)-0.1173
data_x = tf.placeholder(tf.float32,shape=(None,size,size))
data_y = tf.placeholder(tf.float32,shape=(None,10))
#define 3 layers and include 3 relu activation,
#however,data type is error,so not consider activation.
##w1_real=tf.Variable(make_random(shape=(size,size)),dtype=tf.float32)
#w1_imag=tf.Variable(np.zeros(shape=(size,size)).astype('float32'))
#w1=tf.Variable(tf.complex(w1_real,w1_imag))
#w2_real=tf.Variable(make_random(shape=(size,size)),dtype=tf.float32)
#w2_imag=tf.Variable(np.zeros(shape=(size,size)).astype('float32'))
#w2=tf.Variable(tf.complex(w2_real,w2_imag))
#w3_real=tf.Variable(make_random(shape=(size,size)),dtype=tf.float32)
#w3_imag=tf.Variable(np.zeros(shape=(size,size)).astype('float32'))
#w3=tf.Variable(tf.complex(w3_real,w3_imag))
#w4_real=tf.Variable(make_random(shape=(size,size)),dtype=tf.float32)
#w4_imag=tf.Variable(np.zeros(shape=(size,size)).astype('float32'))
#w4=tf.Variable(tf.complex(w4_real,w4_imag))
weight=[
tf.Variable(make_random(shape=(size,size)),dtype=tf.float32),
tf.Variable(make_random(shape=(size,size)),dtype=tf.float32),
tf.Variable(make_random(shape=(size,size)),dtype=tf.float32),
tf.Variable(make_random(shape=(size,size)),dtype=tf.float32),
tf.Variable(make_random(shape=(size,size)),dtype=tf.float32),
tf.Variable(make_random(shape=(size,size)),dtype=tf.float32)]
#forward propagation
layer1=tf.cast(tf.nn.softmax(tf.abs(sf_system(tf.cast(data_x,dtype=tf.complex64),weight[0]))),dtype=tf.complex64)
layer2=tf.cast(tf.nn.softmax(tf.abs(sf_system(layer1,weight[1]))),dtype=tf.complex64)
layer3=tf.cast(tf.nn.softmax(tf.abs(sf_system(layer2,weight[2]))),dtype=tf.complex64)
layer4=tf.cast(tf.nn.softmax(tf.abs(sf_system(layer3,weight[3]))),dtype=tf.complex64)
layer5=tf.cast(tf.nn.softmax(tf.abs(sf_system(layer4,weight[4]))),dtype=tf.complex64)
out=sf_system(layer5,weight[5])
logits_abs =ten_regions(out)
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits_abs,labels=data_y))
train_op = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
pre_correct = tf.equal(tf.argmax(data_y,1),tf.argmax(logits_abs,1))
accuracy = tf.reduce_mean(tf.cast(pre_correct,tf.float32))
init = tf.global_variables_initializer()
train_epochs =50
loss_list=[]
acc_list=[]
session = tf.Session()
with tf.device('/gpu:0'):
session.run(init)
total_batch = int(mnist.train.num_examples / batch_size)
for epoch in tqdm(range(train_epochs)):
for batch in tqdm(range(total_batch)):
batch_x,batch_y = mnist.train.next_batch(batch_size)
batch_x=batch_x.reshape((batch_size,size,size))
session.run(train_op,feed_dict={data_x:batch_x,data_y:batch_y})
loss_,acc = session.run([loss,accuracy],feed_dict={data_x:batch_x,data_y:batch_y})
loss_list.append(loss_)
acc_list.append(acc)
print("epoch :{} loss:{:.4f} train_acc:{:.4f}".format(epoch+1,loss_,acc))
print("Optimizer finished")
fig,ax1=plt.subplots()
ax2=ax1.twinx()
Ins1=ax1.plot(np.arange(50),loss_list,label='Loss')
Ins2=ax2.plot(np.arange(50),acc_list,'r',label='Accuracy')
ax1.set_xlabel('interation')
ax1.set_ylabel('training loss')
ax2.set_ylabel('training accuracy')
Ins=Ins1+Ins2
labels=['Loss','Accuracy']
plt.legend(Ins,labels,loc='best')
plt.show()
weight_= np.array(session.run(weight))
download_text(weight_,epoch,name='weight')
download_image(weight_,epoch,name='weight')
test_data=mnist.test.images.reshape((-1,size,size))
test_label=mnist.test.labels
print("test_accuracy",\
session.run(accuracy,feed_dict={data_x:test_data,data_y:test_label}))
pred_y = session.run(tf.argmax(logits_abs, 1), feed_dict={data_x:test_data})
m=session.run(tf.argmax(test_label, 1))
cm=confusion_matrix(m,pred_y)
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.imshow(cm, interpolation='nearest')
plt.title('Confusion Matrix')
plt.colorbar()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()