预测张量流模型

Question

我是机器学习的新手。我正在研究虹膜数据集。并使用Sepal长度，萼片宽度，花瓣长度来预测使用神经网络的花瓣宽度。从而使得3个输入节点作为A1具有偏置b1,10个隐藏节点作为A2具有偏置b2和1个输出节点。 此外，x_val_train，x_val_test，y_val_train，y_val_test变量用于训练和测试 主要功能如下。

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() 

而且，在这段代码中， hidden_​​output = tf.nn.relu（tf.add（tf.matmul（x_data，A1），B1））`

我已经成功地训练后，我的模型正常化我的数据。但我需要预测用户输入数据的输出。

这里，

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

print("pred: ", pred) 

我写了这个代码来预测结果，但始终预解码返回0。我也尝试过超过100个样本，它仍然返回0。你能告诉我在哪里错了吗？

Answer 1

摘要

让我们来看看

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

这是（几乎）等于

predict = tf.argmax(final_output) 

的argmax是主要问题。如果final_output是单热编码，那么argmax将是有意义的，但final_output只是一个标量数组。

完整的工作守则

这里是给予了充分的工作代码，您有

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))