tensorflow构建神经网络文本分类2

完成前面博客中配置好的环境：ubuntu14.04，tensorflow1.1 ，python3,matplotlib 2.02

#coding:utf-8
from create_sentiment_featuresets import create_feature_sets_and_labels
import numpy as np
import tensorflow as tf
import pickle
import matplotlib.pyplot as plt

#从pickle文件读取数据
pkl_file = open('sentiment_set.pickle', 'rb')
train_x, train_y, test_x, test_y = pickle.load(pkl_file)

n_nodes_hl1 = 128
n_nodes_hl2 = 256
n_nodes_hl3 = 512
n_classes = 2
batch_size = 100
learning_rate=0.01
epoch_plt = []
accuracy_plt = []
cost_plt = []

x = tf.placeholder('float', [None, len(train_x[0])])
y = tf.placeholder('float')

#构建神经网络
def neural_network_model(data):

hidden_1_layer = {
'weights': tf.Variable(tf.random_normal( [len(train_x[0]), n_nodes_hl1],mean=0,stddev=1)),
'biases': tf.Variable(tf.zeros([n_nodes_hl1]))
}

hidden_2_layer = {
'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2],mean=0,stddev=1)),
'biases': tf.Variable(tf.zeros([n_nodes_hl2]))
}

hidden_3_layer = {
'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3],mean=0,stddev=1)),
'biases': tf.Variable(tf.zeros([n_nodes_hl3]))
}

output_layer = {
'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes],mean=0,stddev=1)),
'biases': tf.Variable(tf.zeros([n_classes]))
}

l1 = tf.add(tf.matmul(data, hidden_1_layer['weights']), hidden_1_layer['biases'])
l1 = tf.nn.relu(l1)

l2 = tf.add(tf.matmul(l1, hidden_2_layer['weights']), hidden_2_layer['biases'])
l2 = tf.nn.relu(l2)

l3 = tf.add(tf.matmul(l2, hidden_3_layer['weights']), hidden_3_layer['biases'])
l3 = tf.nn.relu(l3)

output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']
return output

def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
epochs = 20
with tf.Session() as sess:
sess.run(tf.global_variables_initializer()) #初始化变量
for epoch in range(epochs):
i = 0
while i < len(train_x):
#按批进行训练
start = i
end = i + batch_size
batch_x = np.array(train_x[start:end])
batch_y = np.array(train_y[start:end])
train = sess.run(optimizer,feed_dict={x: batch_x, y: batch_y})
c = sess.run(cost, feed_dict={x: batch_x, y: batch_y})
i += batch_size
cost_plt.append(c)
epoch_plt.append(epoch+1)
print('Epoch', epoch + 1, 'loss:', c)
#比较预测值和实际值是否相同,1表示按照 1轴方向
correct = tf.equal(tf.arg_max(prediction, 1), tf.argmax(y, 1))
#将布尔值转为float类型后求平均
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
accuracy = sess.run(accuracy,feed_dict={x: test_x, y: test_y})
accuracy_plt.append(accuracy)
print('Accuracy:',accuracy)

plt.plot(epoch_plt,accuracy_plt,'r',lw=2,label="accuracy")
plt.grid(True)
plt.legend()
plt.show()

plt.plot(epoch_plt,cost_plt,'c',lw=2,label="cost")
plt.grid(True)
plt.legend()
plt.show()

train_neural_network(x)

结果