test_multiple.py

#coding=utf-8

import tensorflow as tf
import numpy as np

def softmax(x):
    """Compute softmax values for each sets of scores in x."""
    e_x = np.exp(x - np.max(x))
    return e_x / e_x.sum()

def predict(test_data):
    tf.reset_default_graph()
    # Network Parameters
    n_hidden_1 = 16  # 1st layer number of features
    n_hidden_2 = 8  # 2nd layer number of features
    n_input = 20  # MNIST data input (img shape: 28*28)
    n_classes = 2  # MNIST total classes (0-9 digits)

    # tf Graph input
    x = tf.placeholder("float", [None, n_input])
    y = tf.placeholder("float", [None, n_classes])
    keep_prob = tf.placeholder("float")

    # Create model
    def multilayer_perceptron(x, weights, biases):
        # Hidden layer with RELU activation
        layer_1 = tf.add(tf.matmul(x, weights['h1']), biases['b1'])
        layer_1 = tf.nn.relu(layer_1)
        # Hidden layer with RELU activation
        layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2'])
        layer_2 = tf.nn.relu(layer_2)
        # Output layer with linear activation
        out_layer = tf.matmul(layer_2, weights['out']) + biases['out']
        return out_layer

    # Store layers weight & bias
    weights = {
        'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),
        'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),
        'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]))
    }
    biases = {
        'b1': tf.Variable(tf.random_normal([n_hidden_1])),
        'b2': tf.Variable(tf.random_normal([n_hidden_2])),
        'out': tf.Variable(tf.random_normal([n_classes]))
    }

    # Construct model
    pred = multilayer_perceptron(x, weights, biases)

    saver = tf.train.Saver()

    # Initializing the variables
    init = tf.global_variables_initializer()

    # Launch the graph
    with tf.Session() as sess:
        saver.restore(sess, "./model_nn/model.ckpt")
        p = sess.run(pred, feed_dict={x: test_data})
        p = softmax(p)
    # print(p)
    if p[0][0] > p[0][1]:
        return "异常"
    else:
        return "正常"