Question

我有一个包含5列的数据集，我将前三列作为输入，另外两列作为输出。我已成功执行该程序，但我不确定如何通过将自己的值作为输入并从模型中获取预测输出来测试模型。

任何人都可以帮助我，我怎样才能在训练结束后用自己的价值来测试模型？我在Python中使用Tensorflow ..我能够显示测试的准确性，但如果我传递一些随机输入，我如何实际预测值（这里，我需要传递3个输入值以获得2个输出值）

这是我的代码：

# Implementation of a simple MLP network with one hidden layer. Tested on the iris data set.
# Requires: numpy, sklearn>=0.18.1, tensorflow>=1.0

# NOTE: In order to make the code simple, we rewrite x * W_1 + b_1 = x' * W_1'
# where x' = [x | 1] and W_1' is the matrix W_1 appended with a new row with elements b_1's.
# Similarly, for h * W_2 + b_2
import tensorflow as tf
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
import pandas as pd

RANDOM_SEED = 1000
tf.set_random_seed(RANDOM_SEED)


def init_weights(shape):
    """ Weight initialization """
    weights = tf.random_normal(shape, stddev=0.1)
    return tf.Variable(weights)

def forwardprop(X, w_1, w_2):
    """
    Forward-propagation.
    IMPORTANT: yhat is not softmax since TensorFlow's softmax_cross_entropy_with_logits() does that internally.
    """
    h    = tf.nn.sigmoid(tf.matmul(X, w_1))  # The \sigma function
    yhat = tf.matmul(h, w_2)  # The \varphi function
    return yhat

def get_iris_data():
    """ Read the iris data set and split them into training and test sets """
    df = pd.read_csv("H:\MiniThessis\Sample.csv")
    train_X = np.array(df[df.columns[0:3]])
    train_Y = np.array(df[df.columns[3:]])
    print(train_X)

    # Convert into one-hot vectors
    #num_labels = len(np.unique(train_Y))
    #all_Y = np.eye(num_labels)[train_Y]  # One liner trick!
    #print()
    return train_test_split(train_X, train_Y, test_size=0.33, random_state=RANDOM_SEED)

def main():
    train_X, test_X, train_y, test_y = get_iris_data()

    # Layer's sizes
    x_size = train_X.shape[1]   # Number of input nodes: 4 features and 1 bias
    h_size = 256                # Number of hidden nodes
    y_size = train_y.shape[1]   # Number of outcomes (3 iris flowers)

    # Symbols
    X = tf.placeholder("float", shape=[None, x_size])
    y = tf.placeholder("float", shape=[None, y_size])

    # Weight initializations
    w_1 = init_weights((x_size, h_size))
    w_2 = init_weights((h_size, y_size))

    # Forward propagation
    yhat    = forwardprop(X, w_1, w_2)
    predict = tf.argmax(yhat, axis=1)

    # Backward propagation
    cost    = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=yhat))
    updates = tf.train.GradientDescentOptimizer(0.01).minimize(cost)

    # Run SGD
    sess = tf.Session()
    init = tf.global_variables_initializer()
    sess.run(init)

    for epoch in range(3):
        # Train with each example
        for i in range(len(train_X)):
            sess.run(updates, feed_dict={X: train_X[i: i + 1], y: train_y[i: i + 1]})

        train_accuracy = np.mean(np.argmax(train_y, axis=1) == sess.run(predict, feed_dict={X: train_X, y: train_y}))
        test_accuracy  = np.mean(np.argmax(test_y, axis=1) ==sess.run(predict, feed_dict={X: test_X, y: test_y}))

        print("Epoch = %d, train accuracy = %.2f%%, test accuracy = %.2f%%"
              % (epoch + 1, 100. * train_accuracy, 100. * test_accuracy))


    correct_Prediction = tf.equal((tf.arg_max(predict,1)),(tf.arg_max(y,1)))
    best = sess.run([predict], feed_dict={X: np.array([[20.14, 46.93, 1014.66]])})
    #print(correct_Prediction)
    print(best)


    sess.close()

if __name__ == '__main__':
    main()

一旦完成训练我们的神经网络就预测输出

0 个答案: