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CS-CsiNet_train.py
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CS-CsiNet_train.py
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import tensorflow as tf
from keras.layers import Input, Dense, BatchNormalization, Reshape, Conv2D, add, LeakyReLU
from keras.models import Model
from keras.callbacks import TensorBoard, Callback
import scipy.io as sio
import numpy as np
import math
import time
tf.reset_default_graph()
envir = 'indoor' #'indoor' or 'outdoor'
# image params
img_height = 32
img_width = 32
img_channels = 2
img_total = img_height*img_width*img_channels
# network params
residual_num = 2
encoded_dim = 512 #compress rate=1/4->dim.=512, compress rate=1/16->dim.=128, compress rate=1/32->dim.=64, compress rate=1/64->dim.=32
# Bulid the decoder model of CS-CsiNet
def residual_network(encoded, residual_num, encoded_dim):
def add_common_layers(y):
y = BatchNormalization()(y)
y = LeakyReLU()(y)
return y
def residual_block_decoded(y):
shortcut = y
y = Conv2D(8, kernel_size=(3, 3), padding='same', data_format='channels_first')(y)
y = add_common_layers(y)
y = Conv2D(16, kernel_size=(3, 3), padding='same', data_format='channels_first')(y)
y = add_common_layers(y)
y = Conv2D(2, kernel_size=(3, 3), padding='same', data_format='channels_first')(y)
y = BatchNormalization()(y)
y = add([shortcut, y])
y = LeakyReLU()(y)
return y
decoded = Dense(img_total, activation='linear')(encoded)
decoded = Reshape((img_channels, img_height, img_width,))(decoded)
for i in range(residual_num):
decoded = residual_block_decoded(decoded)
decoded = Conv2D(2, (3, 3), activation='sigmoid', padding='same', data_format="channels_first")(decoded)
return decoded
image_tensor = Input(shape=(encoded_dim, ))
network_output = residual_network(image_tensor, residual_num, encoded_dim)
decoder = Model(inputs=[image_tensor], outputs=[network_output])
decoder.compile(optimizer='adam', loss='mse')
print(decoder.summary())
# Data loading
if envir == 'indoor':
mat = sio.loadmat('data/DATA_Htrainin.mat')
x_train = mat['HT'] # array
mat = sio.loadmat('data/DATA_Hvalin.mat')
x_val = mat['HT'] # array
mat = sio.loadmat('data/DATA_Htestin.mat')
x_test = mat['HT'] # array
elif envir == 'outdoor':
mat = sio.loadmat('data/DATA_Htrainout.mat')
x_train = mat['HT'] # array
mat = sio.loadmat('data/DATA_Hvalout.mat')
x_val = mat['HT'] # array
mat = sio.loadmat('data/DATA_Htestout.mat')
x_test = mat['HT'] # array
x_train = x_train.astype('float32')
x_val = x_val.astype('float32')
x_test = x_test.astype('float32')
# encoder with random projection
#A = np.random.uniform(low=-0.5, high=0.5, size = (x_train.shape[1], encoded_dim))
mat = sio.loadmat('data/A%d.mat'%(encoded_dim))
A = mat['A'] # array
y_train = np.dot(x_train, A.T)
y_val = np.dot(x_val, A.T)
y_test = np.dot(x_test, A.T)
x_train = np.reshape(x_train, (len(x_train), img_channels, img_height, img_width)) # adapt this if using `channels_first` image data format
x_val = np.reshape(x_val, (len(x_val), img_channels, img_height, img_width)) # adapt this if using `channels_first` image data format
x_test = np.reshape(x_test, (len(x_test), img_channels, img_height, img_width)) # adapt this if using `channels_first` image data format
class LossHistory(Callback):
def on_train_begin(self, logs={}):
self.losses_train = []
self.losses_val = []
def on_batch_end(self, batch, logs={}):
self.losses_train.append(logs.get('loss'))
def on_epoch_end(self, epoch, logs={}):
self.losses_val.append(logs.get('val_loss'))
history = LossHistory()
file = 'CS-CsiNet_'+(envir)+'_dim'+str(encoded_dim)+time.strftime('_%m_%d')
path = 'result/TensorBoard_%s' %file
decoder.fit(y_train, x_train,
epochs=1000,
batch_size=200,
shuffle=True,
validation_data=(y_val, x_val),
callbacks=[history,
TensorBoard(log_dir = path)])
filename = 'result/trainloss_%s.csv'%file
loss_history = np.array(history.losses_train)
np.savetxt(filename, loss_history, delimiter=",")
filename = 'result/valloss_%s.csv'%file
loss_history = np.array(history.losses_val)
np.savetxt(filename, loss_history, delimiter=",")
tStart = time.time()
x_hat = decoder.predict(y_test)
tEnd = time.time()
print ("It cost %f sec" % ((tEnd - tStart)/x_test.shape[0]))
# Calcaulating the NMSE and rho
if envir == 'indoor':
mat = sio.loadmat('data/DATA_HtestFin_all.mat')
X_test = mat['HF_all']# array
elif envir == 'outdoor':
mat = sio.loadmat('data/DATA_HtestFout_all.mat')
X_test = mat['HF_all']# array
X_test = np.reshape(X_test, (len(X_test), img_height, 125))
x_test_real = np.reshape(x_test[:, 0, :, :], (len(x_test), -1))
x_test_imag = np.reshape(x_test[:, 1, :, :], (len(x_test), -1))
x_test_C = x_test_real-0.5 + 1j*(x_test_imag-0.5)
x_hat_real = np.reshape(x_hat[:, 0, :, :], (len(x_hat), -1))
x_hat_imag = np.reshape(x_hat[:, 1, :, :], (len(x_hat), -1))
x_hat_C = x_hat_real-0.5 + 1j*(x_hat_imag-0.5)
x_hat_F = np.reshape(x_hat_C, (len(x_hat_C), img_height, img_width))
X_hat = np.fft.fft(np.concatenate((x_hat_F, np.zeros((len(x_hat_C), img_height, 257-img_width))), axis=2), axis=2)
X_hat = X_hat[:, :, 0:125]
n1 = np.sqrt(np.sum(np.conj(X_test)*X_test, axis=1))
n1 = n1.astype('float64')
n2 = np.sqrt(np.sum(np.conj(X_hat)*X_hat, axis=1))
n2 = n2.astype('float64')
aa = abs(np.sum(np.conj(X_test)*X_hat, axis=1))
rho = np.mean(aa/(n1*n2), axis=1)
X_hat = np.reshape(X_hat, (len(X_hat), -1))
X_test = np.reshape(X_test, (len(X_test), -1))
power = np.sum(abs(x_test_C)**2, axis=1)
power_d = np.sum(abs(X_hat)**2, axis=1)
mse = np.sum(abs(x_test_C-x_hat_C)**2, axis=1)
print("In "+envir+" environment")
print("When dimension is", encoded_dim)
print("NMSE is ", 10*math.log10(np.mean(mse/power)))
print("Correlation is ", np.mean(rho))
filename = "result/decoded_%s.csv"%file
x_hat1 = np.reshape(x_hat, (len(x_hat), -1))
np.savetxt(filename, x_hat1, delimiter=",")
filename = "result/rho_%s.csv"%file
np.savetxt(filename, rho, delimiter=",")
import matplotlib.pyplot as plt
'''abs'''
n = 10
plt.figure(figsize=(20, 4))
for i in range(n):
# display origoutal
ax = plt.subplot(2, n, i + 1 )
x_testplo = abs(x_test[i, 0, :, :]-0.5 + 1j*(x_test[i, 1, :, :]-0.5))
plt.imshow(np.max(np.max(x_testplo))-x_testplo.T)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.invert_yaxis()
# display reconstruction
ax = plt.subplot(2, n, i + 1 + n)
decoded_imgsplo = abs(x_hat[i, 0, :, :]-0.5
+ 1j*(x_hat[i, 1, :, :]-0.5))
plt.imshow(np.max(np.max(decoded_imgsplo))-decoded_imgsplo.T)
plt.gray()
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
ax.invert_yaxis()
plt.show()
# save
# serialize model to JSON
model_json = decoder.to_json()
outfile = "result/model_%s.json"%file
with open(outfile, "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
outfile = "result/model_%s.h5"%file
decoder.save_weights(outfile)