def predict(model, batch, data, classes):
# Plot confusion matrix
test_Y_hat = model.predict([X_test, X1_test, X2_test], batch_size=batch)
confnorm, _, _ = mltools.calculate_confusion_matrix(
Y_test, test_Y_hat, classes)
mltools.plot_confusion_matrix(
confnorm,
labels=classes,
save_filename='figure/mclstm_total_confusion.png')
# Plot confusion matrix
acc = {}
acc_mod_snr = np.zeros((len(classes), len(snrs)))
i = 0
for snr in snrs:
# Extract classes @ SNR
test_SNRs = [lbl[x][1] for x in test_idx]
test_X1_i = X1_test[np.where(np.array(test_SNRs) == snr)]
test_X2_i = X2_test[np.where(np.array(test_SNRs) == snr)]
test_X_i = X_test[np.where(np.array(test_SNRs) == snr)]
test_Y_i = Y_test[np.where(np.array(test_SNRs) == snr)]
# Estimate classes
test_Y_i_hat = model.predict([test_X_i, test_X1_i, test_X2_i])
confnorm_i, cor, ncor = mltools.calculate_confusion_matrix(
test_Y_i, test_Y_i_hat, classes)
acc[snr] = 1.0 * cor / (cor + ncor)
mltools.plot_confusion_matrix(
confnorm_i,
labels=classes,
title="Confusion Matrix",
save_filename="figure/Confusion(SNR=%d)(ACC=%2f).png" %
(snr, 100.0 * acc[snr]))
acc_mod_snr[:, i] = np.round(
np.diag(confnorm_i) / np.sum(confnorm_i, axis=1), 3)
i = i + 1
# Save acc for mod per SNR
fd = open('predictresult/acc_for_mod.dat', 'wb')
pickle.dump((acc_mod_snr), fd)
fd.close()
# Save results to a pickle file for plotting later
print(acc)
fd = open('predictresult/acc.dat', 'wb')
pickle.dump((acc), fd)
# Plot accuracy curve
plt.plot(snrs, list(map(lambda x: acc[x], snrs)))
plt.xlabel("Signal to Noise Ratio")
plt.ylabel("Classification Accuracy")
plt.title(" Classification Accuracy on RadioML")
plt.tight_layout()
plt.savefig('figure/each_acc.png')
def predict(model,
weight_file='res-like-1024-1m.wts.h5',
test_filename='/media/XYZ_1024_128k.hdf5',
dis_acc=True,
dis_conf=True,
min_snr=0):
test_file = h5py.File(test_filename, 'r')
X = test_file['X']
Y = test_file['Y'][:]
Z = test_file['Z'][:]
global classes
model.load_weights(weight_file)
Y_hat = model.predict(X, batch_size=1024, verbose=1)
test_file.close()
#plot confusion matrix
cm, right, wrong = mltools.calculate_confusion_matrix(Y, Y_hat, classes)
acc = round(1.0 * right / (right + wrong), 4)
print('Overall Accuracy:%.2f%s / (%d + %d)' %
(100 * acc, '%', right, wrong))
if dis_conf:
mltools.plot_confusion_matrix(
cm, 'Confution matrix of {}'.format(test_filename), labels=classes)
#plot accuracy with erery snr
if dis_acc:
print(min_snr)
mltools.calculate_acc_cm_each_snr(Y,
Y_hat,
Z,
classes,
min_snr=min_snr)
def predict(model, filename="data/RML2016.10a_dict.pkl"):
(mods, snrs, lbl), (X_train, Y_train), (X_test, Y_test), (train_idx, test_idx) = \
rmldataset2016.load_data(filename, train_rate=0.5)
# Plot confusion matrix
test_Y_hat = model.predict(X_test, batch_size=batch_size)
confnorm, _, _ = mltools.calculate_confusion_matrix(
Y_test, test_Y_hat, classes)
mltools.plot_confusion_matrix(confnorm, labels=classes)
# Plot confusion matrix
acc = {}
for snr in snrs:
# extract classes @ SNR
# test_SNRs = map(lambda x: lbl[x][1], test_idx)
test_SNRs = [lbl[x][1] for x in test_idx]
test_X_i = X_test[np.where(np.array(test_SNRs) == snr)]
test_Y_i = Y_test[np.where(np.array(test_SNRs) == snr)]
# estimate classes
test_Y_i_hat = model.predict(test_X_i)
confnorm_i, cor, ncor = mltools.calculate_confusion_matrix(
test_Y_i, test_Y_i_hat, classes)
acc[snr] = 1.0 * cor / (cor + ncor)
mltools.plot_confusion_matrix(
confnorm_i,
labels=classes,
title="ConvNet Confusion Matrix (SNR=%d)(ACC=%2f)" %
(snr, 100.0 * acc[snr]))
# Save results to a pickle file for plotting later
print(acc)
fd = open('predictresult/cnn2_d0.5.dat', 'wb')
pickle.dump(("CNN2", 0.5, acc), fd)
# Plot accuracy curve
plt.plot(snrs, [acc[i] for i in snrs])
plt.xlabel("Signal to Noise Ratio")
plt.ylabel("Classification Accuracy")
plt.title("CNN2 Classification Accuracy on RadioML 2016.10 Alpha")
plt.show()
def Draw1(self):
weight_file = 'weights-ResNet-norm.h5'
test_filename = "F:/PyQT5学习/01.hdf5"
test_file = h5py.File(test_filename, 'r')
X = test_file['X'][:, :, :]
Y = test_file['Y'][:]
Z = test_file['Z'][:]
classes = [
'2FSK', '4FSK', '4ASK', '16QAM', 'MSK', 'OQPSK', 'BPSK', 'QPSK',
'8PSK', 'LFM'
]
if weight_file is not None:
model = load_model(weight_file)
Y_hat = model.predict(X, batch_size=1024, verbose=1)
test_file.close()
# plot confusion matrix
cm, right, wrong = mltools.calculate_confusion_matrix(
Y, Y_hat, classes)
acc = round(1.0 * right / (right + wrong), 4)
print('CNN网络模型在测试集上的总准确率:%.2f%s / (%d + %d)' %
(100 * acc, '%', right, wrong))
plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.colorbar()
self.tick_marks = np.arange(len(classes))
plt.xticks(self.tick_marks, classes, rotation=45)
plt.yticks(self.tick_marks, classes)
plt.tight_layout()
plt.ylabel('正确的标签')
plt.xlabel('预测的标签')
self.draw()
def predict(model,filename="data/RML2016.10a_dict.pkl"):
(mods, snrs, lbl), (X_train, Y_train), (X_test, Y_test), (train_idx, test_idx) = \
rmldataset2016.load_data(filename, train_rate=0.5)
# Plot confusion matrix
test_Y_hat = model.predict(X_test, batch_size=batch_size)
confnorm,_,_ = mltools.calculate_confusion_matrix(Y_test,test_Y_hat,classes)
mltools.plot_confusion_matrix(confnorm, labels=classes,save_filename='figure/total_confusion')
# Plot confusion matrix
acc = {}
acc_mod_snr = np.zeros( (len(classes),len(snrs)) )
i = 0
for snr in snrs:
# extract classes @ SNR
# test_SNRs = map(lambda x: lbl[x][1], test_idx)
test_SNRs = [lbl[x][1] for x in test_idx]
test_X_i = X_test[np.where(np.array(test_SNRs) == snr)]
test_Y_i = Y_test[np.where(np.array(test_SNRs) == snr)]
# estimate classes
test_Y_i_hat = model.predict(test_X_i)
confnorm_i,cor,ncor = mltools.calculate_confusion_matrix(test_Y_i,test_Y_i_hat,classes)
acc[snr] = 1.0 * cor / (cor + ncor)
mltools.plot_confusion_matrix(confnorm_i, labels=classes, title="ConvNet Confusion Matrix (SNR=%d)(ACC=%2f)" % (snr,100.0*acc[snr]),save_filename="figure/Confusion(SNR=%d)(ACC=%2f).png" % (snr,100.0*acc[snr]))
acc_mod_snr[:,i] = np.round(np.diag(confnorm_i)/np.sum(confnorm_i,axis=1),3)
i = i +1
#plot acc of each mod in one picture
dis_num=11
for g in range(int(np.ceil(acc_mod_snr.shape[0]/dis_num))):
assert (0 <= dis_num <= acc_mod_snr.shape[0])
beg_index = g*dis_num
end_index = np.min([(g+1)*dis_num,acc_mod_snr.shape[0]])
plt.figure(figsize=(12, 10))
plt.xlabel("Signal to Noise Ratio")
plt.ylabel("Classification Accuracy")
plt.title("Classification Accuracy for Each Mod")
for i in range(beg_index,end_index):
plt.plot(snrs, acc_mod_snr[i], label=classes[i])
# 设置数字标签
for x, y in zip(snrs, acc_mod_snr[i]):
plt.text(x, y, y, ha='center', va='bottom', fontsize=8)
plt.legend()
plt.grid()
plt.savefig('figure/acc_with_mod_{}.png'.format(g+1))
plt.close()
#save acc for mod per SNR
fd = open('predictresult/acc_for_mod_on_cldnn.dat', 'wb')
pickle.dump(('128','cldnn', acc_mod_snr), fd)
fd.close()
# Save results to a pickle file for plotting later
print(acc)
fd = open('predictresult/CLDNN_dr0.5.dat','wb')
pickle.dump( ("1D", 0.5, acc) , fd )
# Plot accuracy curve
plt.plot(snrs, list(map(lambda x: acc[x], snrs)))
plt.xlabel("Signal to Noise Ratio")
plt.ylabel("Classification Accuracy")
plt.title("CLDNN Classification Accuracy on RadioML 2016.10 Alpha")
plt.tight_layout()
plt.savefig('figure/each_acc.png')