import numpy as np

 import matplotlib.pyplot as plt

 import tensorflow as tf

 import tflearn

 from tflearn.data_utils import to_categorical

 #matplotlib inline

 plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots

 plt.rcParams['image.interpolation'] = 'nearest'

 plt.rcParams['image.cmap'] = 'gray'

 # for auto-reloading extenrnal modules

 # see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython

 #%load_ext autoreload

 #%autoreload 2

 # 模块1-1 画图 展示样本形式

 np.random.seed(0)

 N = 100 # number of points per class

 D = 2 # dimensionality

 K = 3 # number of classes

 X = np.zeros((N*K,D))

 y = np.zeros(N*K, dtype='uint8')

 for j in range(K):

   ix = range(N*j,N*(j+1))

   r = np.linspace(0.0,1,N) # radius

   t = np.linspace(j*4,(j+1)*4,N) + np.random.randn(N)*0.2 # theta

   X[ix] = np.c_[r*np.sin(t), r*np.cos(t)]

   y[ix] = j

 fig = plt.figure()

 plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)

 plt.xlim([-1,1])

 plt.ylim([-1,1])

 #fig.savefig('spiral_raw.png')

 #模块1-2 训练 两层

 import tensorflow as tf

 import tflearn

 from tflearn.data_utils import to_categorical

 with tf.Graph().as_default():

     net = tflearn.input_data([None, 2])

     net = tflearn.fully_connected(net, 100, activation='relu', weights_init='normal',

                                   regularizer='L2', weight_decay=0.001)

     net = tflearn.fully_connected(net, 3, activation='softmax')

     sgd = tflearn.SGD(learning_rate=1.0, lr_decay=0.96, decay_step=500)

     net = tflearn.regression(net, optimizer=sgd, loss='categorical_crossentropy')

 #    gd=tf.train.GradientDescentOptimizer(learning_rate=1.0)

 #    net = tflearn.regression(net, optimizer=gd, loss='categorical_crossentropy')

     Y = to_categorical(y, 3)

     model = tflearn.DNN(net)

     model.fit(X, Y, show_metric=True, batch_size=len(X), n_epoch=100, snapshot_epoch=False)

     #print model.predict(X)

     Z = np.argmax(model.predict(X))

     #print Z

 # 模块1-3  画图展示效果 plot the resulting classifier

 h = 0.02

 x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1

 y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1

 xx, yy = np.meshgrid(np.arange(x_min, x_max, h),

                      np.arange(y_min, y_max, h))

 Z = np.argmax(model.predict(np.c_[xx.ravel(), yy.ravel()]), axis=1)

 Z = Z.reshape(xx.shape)

 fig = plt.figure()

 plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)

 plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)

 plt.xlim(xx.min(), xx.max())

 plt.ylim(yy.min(), yy.max())

 #fig.savefig('spiral_net.png')

 print("Accuracy: {}%".format(100 * np.mean(y == np.argmax(model.predict(X), axis=1))))

 import numpy as np

 import matplotlib.pyplot as plt

 import tensorflow as tf

 import tflearn

 from tflearn.data_utils import to_categorical

 import os

 label=[]

 feature=[]

 f_tain=open("smp_pub_50d",'r')

 for line in f_tain.readlines()[0:50000]:

     samp=eval(line)

     gender=int(samp[2])

     feature1=samp[5:]

     feature.append(feature1)

     label.append(gender)

 f_tain.close()

 X=np.array(feature)

 Y=np.array(label)

 #模块1-2 训练 两层

 import tensorflow as tf

 import tflearn

 from tflearn.data_utils import to_categorical

 with tf.Graph().as_default():

     net = tflearn.input_data([None, 50])

     net = tflearn.fully_connected(net, 100, activation='relu', weights_init='normal',

                                   regularizer='L2', weight_decay=0.001)

     net = tflearn.fully_connected(net, 2, activation='softmax')

     sgd = tflearn.SGD(learning_rate=1.0, lr_decay=0.96, decay_step=500)

     net = tflearn.regression(net, optimizer=sgd, loss='categorical_crossentropy')

 #    gd=tf.train.GradientDescentOptimizer(learning_rate=1.0)

 #    net = tflearn.regression(net, optimizer=gd, loss='categorical_crossentropy')

     Y = to_categorical(Y, 2)

     model = tflearn.DNN(net)

     model.fit(X, Y, validation_set=0.3,show_metric=True, batch_size=20000, n_epoch=100, snapshot_epoch=False)

     #print model.predict(X)

     Z = np.argmax(model.predict(X))

     #print Z