Tensorflow API用於測試數據

Question

我有一個包含5列的數據集，我將前3列作爲輸入，其他2列作爲輸出。

我已經成功執行了該程序，但我不確定如何通過將我自己的值作爲輸入並從模型中獲取預測輸出來測試模型。

任何人都可以請幫助我，我怎麼能在訓練完成後用我自己的價值測試模型？

我在Python中使用Tensorflow。我能夠顯示測試的準確度，但實際上，我怎麼用值預測，如果我通過一些隨機輸入（在這裏，我需要通過3個輸入值，獲得2個輸出值）

這裏是我的代碼：

# 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() 

Answer 1

請注意，這個答案需要對輸入進行一些假設，您在原始問題中沒有提供這些假設。

在這一點上，我認爲你的功課需要完成一些更多的工作。第4列和第5列可能是花瓣寬度（真實值）和虹膜類型（編碼爲類別）。這意味着結果你可能需要一個真正的有價值的輸出（花瓣寬度）和一個分類預測。這意味着你的softmax_cross_entry_with_logits可能無法很好地展現花瓣寬度。此外，您正在應用argmax作爲預測的一部分，並且這將返回具有最高值的索引（在此情況下爲花瓣寬度或編碼值），這也是沒有意義的。因此，作爲一個輔助調試爲什麼不先從：

print(sess.run([yhat], feed_dict={X: np.array([[20.14, 46.93, 1014.66]])}) 

這將打印出yhat，（基於輸入

[20.14, 46.93, 1014.66]（這是一個巨大的花，完全不像數據集內的任何東西），將包含2個輸出。