tflearn 实现DNN 全连接

https://github.com/tflearn/tflearn/blob/master/examples/others/recommender_wide_and_deep.py

 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))))

代码2 自己的数据

 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