张量流模型的预测

Question

我不熟悉机器学习。我正在研究虹膜数据集。利用神经网络对萼片长度、萼片宽度、花瓣长度进行预测。从而使3个输入节点作为A1，偏置b1，10个隐藏节点作为A2，偏置b2和1个输出节点。 此外，训练和测试使用x_val_train、x_val_test、y_val_train、y_val_测试变量 主要功能如下。在

x_val = np.array([x[0:3] for x in iris.data])
y_val = np.array([x[3] for x in iris.data])

hidden_layer_size = 10

#Generate a 1D array of random numbers range round(len(x_val)*0.8
train_indices = np.random.choice(len(x_val), round(len(x_val)*0.8), replace = False)

#Create a set which does not contain the numbers in train_indices and turn it into array
test_indices = np.array(list(set(range(len(x_val))) - set(train_indices)))
#print("Train Indexes\n",train_indices,test_indices)

x_val_train = x_val[train_indices]
x_val_test = x_val[test_indices]
y_val_train = y_val[train_indices]
y_val_test = y_val[test_indices]


x_data = tf.placeholder(shape=[None, 3], dtype = tf.float32)
y_target = tf.placeholder(shape = [None, 1], dtype = tf.float32) #Figure out usage of None

#Create Layers for NN

A1 = tf.Variable(tf.random_normal(shape = [3,hidden_layer_size])) #Input -> Hidden
b1 = tf.Variable(tf.random_normal(shape = [hidden_layer_size])) #bias in Input for hidden

A2 = tf.Variable(tf.random_normal(shape = [hidden_layer_size,1])) #Hidden -> Output
b2 = tf.Variable(tf.random_normal(shape=[1])) #Hidden Layer Bias

#Generation of Model

hidden_output = tf.nn.relu(tf.add(tf.matmul(x_data,A1),b1))
final_output = tf.nn.relu(tf.add(tf.matmul(hidden_output,A2),b2))

cost = tf.reduce_mean(tf.square(y_target - final_output))

learning_rate = 0.01

model = tf.train.AdamOptimizer(learning_rate).minimize(cost)

init = tf.global_variables_initializer()

sess.run(init)

#Training Loop

loss_vec = []
test_loss = []
epoch = 500

for i in range(epoch):
    #generates len(x_val_train) random numbers
    rand_index = np.random.choice(len(x_val_train), size = batch_size)
    #Get len(x_val_train) data with its 3 input notes or 
    rand_x = x_val_train[rand_index]
    #print(rand_index,rand_x)
    rand_y = np.transpose([y_val_train[rand_index]])
    sess.run(model, feed_dict = {x_data: rand_x, y_target: rand_y})

    temp_loss = sess.run(cost, feed_dict = {x_data: rand_x, y_target : rand_y})
    loss_vec.append(np.sqrt(temp_loss))

    test_temp_loss = sess.run(cost, feed_dict = {x_data : x_val_test, y_target : np.transpose([y_val_test])})
    test_loss.append(np.sqrt(test_temp_loss))

    if (i+1)%50!=0:
        print('Generation: ' + str(i+1) + '.loss = ' + str(temp_loss))

    predict = tf.argmax(tf.add(tf.matmul(hidden_output,A2),b2), 1)

    test = np.matrix('2  3  4')
    pred = predict.eval(session = sess, feed_dict = {x_data : test})


print("pred: ", pred)

plt.plot(loss_vec, 'k-', label='Train Loss')
plt.plot(test_loss, 'r--', label='Test Loss')
plt.show()

同样，在这部法典中， 隐藏的输出=tf.nn.relu公司(tf.添加(tf.matmul公司（x_数据，A1），b1））`

在规范化数据之后，我成功地训练了我的模型。但是我需要通过用户输入数据来预测输出。在

在这里

^{pr2}$

我编写了这段代码来预测结果，但是pred总是返回0。我也尝试了100多个样本，仍然返回0。你能告诉我哪里出错了吗？在

Answer 1

摘要

我们来看看

predict = tf.argmax(tf.add(tf.matmul(hidden_output,A2),b2), 1)

这（几乎）等于

^{pr2}$

argmax是主要问题。如果final_output是1-hot编码，那么argmax是有意义的，但是最终的输出只是一个标量数组。在

完整工作代码

这里是完整的工作代码，你有

import numpy as np
import tensorflow as tf

import os
import urllib

# Data sets
IRIS_TRAINING = "iris_training.csv"
IRIS_TRAINING_URL = "http://download.tensorflow.org/data/iris_training.csv"

IRIS_TEST = "iris_test.csv"
IRIS_TEST_URL = "http://download.tensorflow.org/data/iris_test.csv"

# If the training and test sets aren't stored locally, download them.
if not os.path.exists(IRIS_TRAINING):
  raw = urllib.urlopen(IRIS_TRAINING_URL).read()
  with open(IRIS_TRAINING, "w") as f:
    f.write(raw)

if not os.path.exists(IRIS_TEST):
  raw = urllib.urlopen(IRIS_TEST_URL).read()
  with open(IRIS_TEST, "w") as f:
    f.write(raw)

training_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TRAINING, target_dtype=np.int, features_dtype=np.float32)
test_set = tf.contrib.learn.datasets.base.load_csv_with_header( filename=IRIS_TEST, target_dtype=np.int, features_dtype=np.float32)

x_val_train = training_set.data[:,:3]
x_val_test = test_set.data[:,:3]
y_val_train = training_set.data[:,3].reshape([-1,1])
y_val_test = test_set.data[:,3].reshape([-1,1])

x_data = tf.placeholder(shape=[None, 3], dtype = tf.float32)
y_target = tf.placeholder(shape = [None, 1], dtype = tf.float32) #Figure out usage of None

#Create Layers for NN
hidden_layer_size = 20

A1 = tf.Variable(tf.random_normal(shape = [3,hidden_layer_size])) #Input -> Hidden
b1 = tf.Variable(tf.random_normal(shape = [hidden_layer_size])) #bias in Input for hidden

A2 = tf.Variable(tf.random_normal(shape = [hidden_layer_size,1])) #Hidden -> Output
b2 = tf.Variable(tf.random_normal(shape = [1])) #Hidden Layer Bias

#Generation of model

hidden_output = tf.nn.relu(tf.add(tf.matmul(x_data,A1),b1))
final_output = tf.add(tf.matmul(hidden_output,A2),b2)

loss = tf.reduce_mean(tf.square(y_target - final_output))

learning_rate = 0.01
train = tf.train.AdamOptimizer(learning_rate).minimize(loss)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

#Training Loop

loss_vec = []
test_loss = []
epoch = 2000
batch_size = 100


def oneTrainingSession(epoch,loss_vec,test_loss,batch_size) :
    rand_index = np.random.choice(len(x_val_train), size = batch_size)

    rand_x = x_val_train #[rand_index,:]
    rand_y = y_val_train #[rand_index,:]

    temp_loss,_ = sess.run([loss,train], feed_dict = {x_data: rand_x, y_target : rand_y})
    loss_vec.append(np.sqrt(temp_loss))

    test_temp_loss = sess.run(loss, feed_dict = {x_data : x_val_test, y_target : y_val_test})
    test_loss.append(np.sqrt(test_temp_loss))

    if (i+1)%500 == 0:
        print('Generation: ' + str(i+1) + '.loss = ' + str(temp_loss))

for i in range(epoch):
    oneTrainingSession(epoch,loss_vec,test_loss,batch_size)

test = x_val_test[:3,:]
print "The test values are"
print test
print ""
pred = sess.run(final_output, feed_dict = {x_data : test})
print("pred: ", pred)

输出

Generation: 500.loss = 0.12768
Generation: 1000.loss = 0.0389756
Generation: 1500.loss = 0.0370268
Generation: 2000.loss = 0.0361797
The test values are
[[ 5.9000001   3.          4.19999981]
 [ 6.9000001   3.0999999   5.4000001 ]
 [ 5.0999999   3.29999995  1.70000005]]

('pred: ', array([[ 1.45187187],
       [ 1.92516518],
       [ 0.36887735]], dtype=float32))