train.py

# -*- coding: utf-8 -*-
import os,random
os.environ["KERAS_BACKEND"] = "tensorflow"
import numpy as np
from keras.utils import np_utils
import keras.models as models
from keras.layers.core import Reshape,Dense,Dropout,Activation,Flatten
from keras.layers.noise import GaussianNoise
from keras.layers.convolutional import Conv2D, MaxPooling2D, ZeroPadding2D
from keras.regularizers import *
from keras.optimizers import adam
import matplotlib.pyplot as plt
import seaborn as sns
import pickle, random, sys, keras
import h5py

#%%
with open("/home/hzy/keras/RML2016.10a_dict.dat",'rb') as xd1: #这段执行对原始数据进行切片的任务，可在spyder下运行，查看变量
    Xd = pickle.load(xd1,encoding='latin1')
snrs,mods = map(lambda j: sorted(list(set(map(lambda x: x[j], Xd.keys())))), [1,0])
X = []  
lbl = []
for mod in mods:
    for snr in snrs:
        X.append(Xd[(mod,snr)])
        for i in range(Xd[(mod,snr)].shape[0]):  lbl.append((mod,snr))
X = np.vstack(X)
#%%
np.random.seed(2016)  #对预处理好的数据进行打包，制作成投入网络训练的格式，并进行one-hot编码
n_examples = X.shape[0]
n_train = n_examples * 0.5 #对半
train_idx = np.random.choice(range(0,n_examples), size=int(n_train), replace=False)
test_idx = list(set(range(0,n_examples))-set(train_idx)) #label
X_train = X[train_idx]
X_test =  X[test_idx]
def to_onehot(yy):
    yy1 = np.zeros([len(yy), max(yy)+1])
    yy1[np.arange(len(yy)),yy] = 1
    return yy1
trainy =list(map(lambda x: mods.index(lbl[x][0]), train_idx))
Y_train = to_onehot(trainy)
Y_test = to_onehot(list(map(lambda x: mods.index(lbl[x][0]), test_idx)))
#%%
in_shp = list(X_train.shape[1:])
print (X_train.shape, in_shp)
classes = mods
#%%
dr = 0.5 # dropout rate (%) 卷积层部分  https://keras-cn.readthedocs.io/en/latest/layers/convolutional_layer/#conv2d
model = models.Sequential() #这里使用keras的序贯模型  https://keras-cn.readthedocs.io/en/latest/models/sequential/
model.add(Reshape(([1]+in_shp), input_shape=in_shp))
model.add(ZeroPadding2D((0, 2)))
model.add(Conv2D(256, (1, 3),padding='valid', activation="relu", name="conv1", init='glorot_uniform',data_format="channels_first"))
model.add(Dropout(dr))
model.add(ZeroPadding2D((0, 2)))
model.add(Conv2D(80, (2, 3), padding="valid", activation="relu", name="conv2", init='glorot_uniform',data_format="channels_first"))
model.add(Dropout(dr))
model.add(Flatten())
model.add(Dense(256, activation='relu', init='he_normal', name="dense1"))
model.add(Dropout(dr))
model.add(Dense( len(classes), init='he_normal', name="dense2" ))
model.add(Activation('softmax'))
model.add(Reshape([len(classes)]))
model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
#%%
# Set up some params 
epochs = 100     # number of epochs to train on
batch_size = 1024  # training batch size default1024
#%%
filepath = "convmodrecnets_CNN2_0.5.wts.h5" #所要保存的文件名字，h5格式，不用写路径，默认在程序执行的文件夹内
history = model.fit(X_train,
    Y_train,
    batch_size=batch_size,
    epochs=epochs,
    verbose=2,
    validation_data=(X_test, Y_test),
    callbacks = [ #回调函数，https://keras-cn.readthedocs.io/en/latest/other/callbacks/
        keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=True, mode='auto'),
        keras.callbacks.EarlyStopping(monitor='val_loss', patience=5, verbose=0, mode='auto')
    ]) #EarlyStopping 当监测值不再改善时，该回调函数将中止训练，如去除本行将执行所有epoch
model.load_weights(filepath)
#%%
score = model.evaluate(X_test, Y_test, verbose=0, batch_size=batch_size)
print(score)

#%%
def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues, labels=[]):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(labels))
    plt.xticks(tick_marks, labels, rotation=45)
    plt.yticks(tick_marks, labels)
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
#%%
# Plot confusion matrix 画图
acc = {}
for snr in snrs:

    # extract classes @ SNR
    test_SNRs = list(map(lambda x: lbl[x][1], test_idx))
#    print(test_SNRs)
    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)
    conf = np.zeros([len(classes),len(classes)])
    confnorm = np.zeros([len(classes),len(classes)])
    for i in range(0,test_X_i.shape[0]):
        j = list(test_Y_i[i,:]).index(1)
        k = int(np.argmax(test_Y_i_hat[i,:]))
        conf[j,k] = conf[j,k] + 1
    for i in range(0,len(classes)):
        confnorm[i,:] = conf[i,:] / np.sum(conf[i,:])
    plt.figure()
    plot_confusion_matrix(confnorm, labels=classes, title="ConvNet Confusion Matrix (SNR=%d)"%(snr))
    
    cor = np.sum(np.diag(conf))
    ncor = np.sum(conf) - cor
    print ("Overall Accuracy: ", cor / (cor+ncor))
    acc[snr] = 1.0*cor/(cor+ncor)
#%%
# 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("CNN2 Classification Accuracy on RadioML 2016.10 Alpha")